A genetic approach to the relationship between taste perception and lifestyle in Africa.

MSc A genetic approach to the relationship between taste perception and lifestyle in Africa Marisa Sofia Sousa Oliveira 2012 A genetic approach to the relationship between taste perception and lifestyle in Africa Marisa Sofia Sousa Oliveira 2012 A genetic approach to the relationship between taste perception and lifestyle in Africa Marisa Sofia Sousa Oliveira Mestrado em Genética Forense Departamento de Biologia 2012 Orientadora Professora Doutora Maria João Prata Martins Ribeiro, Professora Associada c/ Agregação na Faculdade de Ciências da Universidade do Porto e Investigadora no Instituto de Patologia e Imunologia Molecular da Universidade do Porto. Todas as correções determinadas pelo júri, e só essas, foram efetuadas. O Presidente do Júri, Porto, ______/______/_________ Dissertação de candidatura ao grau de Mestre em Genética Forense submetida à Faculdade de Ciências da Universidade do Porto. O presente trabalho foi desenvolvido sob a orientação científica da Professora Doutora Maria João Prata Martins Ribeiro e foi inteiramente realizado no Instituto de Patologia e Imunologia Molecular da Universidade do Porto. Dissertation for applying to a Master’s Degree in Forensic Genetics submitted to the Faculty of Sciences of the University of Porto. The present work was developed under the scientific supervision of Professor Maria João Prata Martins Ribeiro and was entirely done in the Institute of Molecular Pathology and Immunology of University of Porto. FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 7 Agradecimentos Em primeiro lugar queria agradecer à minha orientadora, Professora Maria João Prata, por me ter dado a oportunidade de participar neste projecto, prontificando-se sempre a acompanhar-me e a ensinar-me. Agradeço o apoio e auxílio prestados no decorrer deste ano e a simpatia com que sempre me recebeu. À Cristina e à Sofia, pela forma amigável como me acolheram desde o início e pela paciência e compreensão que tiveram durantes todas as fases, além da motivação que me transmitiram. Um agradecimento em particular à Cristina por toda a orientação e conselhos dados. Ao Luis estou grata pela disponibilidade e contribuição que deu para a realização desta tese. Ao Professor Amorim pela oportunidade de integrar o seu grupo e por tudo o que me ensinou ao longo dos últimos 2 anos. Ao Grupo da Genética Populacional, pela forma agradável com que me recebeu. Além disso, a todos os outros que contribuem para o bom funcionamento e ambiente do IPATIMUP. Aos colegas de mestrado por terem-se mostrado mais que simples colegas, principalmente as meninas Ana, Lídia e as redescobertas Filipa e Sofia, pela companhia e boa disposição diárias. Além disso, às duas aquisições vindas de Aveiro, Inês e Catarina, que vieram animar os nossos dias. Aos amigos de licenciatura, que sem dúvida contribuíram para ter chegado aqui. Mas não posso deixar de referir em particular a Susana, Sara e Catarina, que se tornam aquele tipo de amigas que se quer manter durante a vida. E ainda, um agradecimento especial à Catarina, pois não só me acompanhou durante a licenciatura mas também no mestrado, e em ambos os casos ajudou-me (e aturou-me) como poucos. Obrigada Catarina! Aos meus meninos, Ricardo e André, que me fazem rir em todas as ocasiões e conseguem tornar o pior dia num bem mais agradável…Obrigada por estarem presentes e serem assim e, sem dúvida, pela grande amizade que fomos construindo! Às minhas meninas, Mónica e Tânia, pela amizade constante e por todos os momentos partilhados, bons ou maus, que nos fizeram crescer e sem dúvida contribuíram para o que hoje somos…Obrigada MarMoTa! (E ao Rui também!) À Sofia, por tudo! Porque tens sido uma amiga excecional e sabes bem o valor que tens para mim… FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 8 Ao Prof. Zé Carlos, por ter sido muito mais que um professor mas um verdadeiro amigo e exemplo de vida. Por me ter dado importantes lições de Matemática na escola e no dia a dia e também por me ter ensinado a ser melhor e ter acreditado em mim. Por me ter feito “querer e crer” mais, não só como aluna mas como pessoa e pela fantástica oportunidade que foi conhecê-lo de perto e acompanhá-lo nos momentos difíceis. Nem todos tiveram essa oportunidade e eu não podia estar mais grata por pertencer a esse grupo. Obrigada Professor, até um dia! À minha família e ao Jorge, por todo o apoio, não só neste percurso, mas em todos os momentos. Aos meus avós, Eduardo e Margarida, por tudo o que fizeram por mim desde sempre, pela companhia, bons momentos e por me terem ensinado o verdadeiro significado da palavra “família”. Acredito que poucos têm uns avós como vocês, e eu estou orgulhosa de ser vossa neta! À minha mãe…uma verdadeira lutadora e, acima de tudo, a minha melhor amiga. Obrigada por tudo e sei que, o que sou hoje, a ti o devo! FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 15 Admite-se que a dieta tenha sido um importante fator modelador dos padrões atuais de diversidade genética humana. A evolução humana está associada a muitas mudanças na dieta, entre as quais se salienta a que ocorreu à volta de 10 000 anos, quando as sociedades humanas começaram a abandonar a caça e recoleção em benefício da agricultura e pastorícia como novos modos de subsistência. A introdução de novos comportamentos alimentares pode acarretar alterações nos processos biológicos, conduzindo a pressões seletivas, tanto a nível do metabolismo como na sensibilidade ao gosto. Quanto ao gosto, atualmente são reconhecidas cinco qualidades básicas – amargo, doce, salgado, ácido e umami. Apesar de se saber que as diferenças a nível individual na sensibilidade aos diferentes sabores são parcialmente reguladas pela genética, ainda pouco se sabe acerca dessa relação. O trabalho apresentado surge com o principal objetivo de caracterizar populações africanas com distintos estilos de vida usando uma bateria de SNPs relacionados com o gosto. Neste sentido, três sociedades agrárias – de Angola, Moçambique e Guiné Equatorial, e uma pastoril – do Uganda, foram genotipadas com recurso a duas reações Multiplex especificamente desenhadas e otimizadas, contendo 11 polimorfismos implicados na perceção do gosto e distribuídos por quatro genes: TAS1R1 e TAS1R3, envolvidos na perceção do umami e doce, e TAS2R16 e TAS2R38, ambos associados à deteção do amargo. Além destas populações, foi caracterizada uma amostra de portugueses, que funcionou como um controlo de uma população fora do continente africano. Quanto aos polimorfismos estudados, não foram encontrados desvios ao equilíbrio de Hardy-Weinberg em qualquer das cinco populações. Relativamente ao gene TAS2R16, observou-se uma clara distinção entre populações africanas e não africanas e em África encontrou-se um interessante paralelismo entre a distribuição de frequências do SNP testado nesse gene e a expansão Bantu. Para a maioria das variações estudadas, os resultados da AMOVA revelaram que a geografia era um importante determinante dos respetivos padrões genéticos encontrados a nível mundial. Neste trabalho, não se detetou qualquer associação clara entre estilo de vida e padrão de variação genética relacionada com o gosto. Contudo a escassez de dados sobre populações africanas com diferentes modos de subsistência, não permite ainda excluir que o estilo de vida tenha contribuído para configurar a diversidade genética das variações em análise. Os padrões mundiais de distribuição de frequências quanto a algumas dessas variações, apresentam características peculiares, sugestivas de que possam ter estado sujeitas a efeitos de pressões seletivas, cuja natureza ainda se desconhece. O presente estudo contribuiu para enriquecer o estado de caracterização genética em África, abrindo portas para se chegar a uma melhor compreensão dos fatores que influenciam os padrões de diversidade genética com impacto na sensibilidade gustativa. Palavras-chave: Dieta, Modo de Subsistência, Sensibilidade ao Gosto, África, Diversidade Genética. FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 17 Table of Contents Agradecimentos .......................................................................................................... 7 ABSTRACT .................................................................................................................. 9 RESUMO .................................................................................................................... 13 Table of Contents ..................................................................................................... 17 Tables Index .............................................................................................................. 19 Figures Index ............................................................................................................ 21 Abbreviations ............................................................................................................ 23 I. INTRODUCTION ................................................................................................. 25 1. Population Genetics ......................................................................................... 27 1.1. Selection and Genetic Adaptation ............................................................. 27 1.1.1. Selective Factors ................................................................................... 28 2. Diet .................................................................................................................. 29 2.1. Neolithic Revolution .................................................................................. 30 2.1.1. Diversity in Africa ................................................................................... 32 2.1.1.1. Agriculture and its expansion ............................................................. 34 2.1.1.2. Pastoral migrations ............................................................................ 35 2.2. Dietary Adaptations resulting from Neolithic innovations ........................... 36 3. Taste Perception .............................................................................................. 37 3.1. Bitter Taste ................................................................................................ 37 3.2. Umami Taste ............................................................................................. 39 3.3. Sweet Taste .............................................................................................. 40 3.4. Salty Taste ................................................................................................ 40 3.5. Sour Taste ................................................................................................ 41 4. Forensic Genetics Approach ............................................................................ 43 II. AIMS .................................................................................................................... 45 III. MATERIAL & METHODS .................................................................................... 49 1. Samples and DNA extraction ........................................................................... 51 2. Amplification Multiplexes Design ...................................................................... 52 2.1. Genetic Markers and Target polymorphisms selection .............................. 52 2.2. Multiplex PCR amplification ....................................................................... 53 2.3. Optimization of the multiplex ..................................................................... 54 FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 18 2.4. Electrophoresis ......................................................................................... 56 3. SNP genotyping ............................................................................................... 57 3.1. Single Base Extension Multiplexes ............................................................ 57 3.2. Minisequencing protocol ............................................................................ 58 4. Sanger Sequencing .......................................................................................... 61 5. Data analysis.................................................................................................... 62 IV. RESULTS & DISCUSSION ................................................................................. 63 1. Locus by locus approach .................................................................................. 65 1.1. Bitter taste ................................................................................................. 65 1.2. Umami taste .............................................................................................. 79 1.3. Sweet taste ............................................................................................... 87 2. The role of lifestyle and geography in the different loci ..................................... 92 V. CONCLUSIONS .................................................................................................. 97 VI. BIBLIOGRAPHY ............................................................................................... 101 VII. APPENDIX ........................................................................................................ 111 FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 19 Tables Index Table 1 – Single Nucleotide Polymorphisms linked to PTC perception in TAS2R38. . 38 Table 2 – Amplification primers and their characteristics. ........................................... 53 Table 3 – Volumes added in one PCR reaction of Multiplex 1. ................................... 54 Table 4 – Volumes needed in one PCR reaction of Multiplex 2. ................................. 55 Table 5 – Single Base Extension Primers. ................................................................. 57 Table 6 – Elements and respective volumes needed in one SNaPshotTM reaction for Multiplex 1. ................................................................................................................. 58 Table 7 – Components and volumes required in one SNaPshotTM reaction for Multiplex 2. ................................................................................................................................ 59 Table 8 – PCR program used in Sanger Sequencing. ................................................ 61 Table 9 – TAS2R16*T516 allele frequency plus standard deviation in different populations. ................................................................................................................ 65 Table 10 – FST values among Uganda, Angola, Mozambique, Equatorial Guinea and Portugal for TAS2R16 SNP. ........................................................................................ 66 Table 11 – AMOVA results in Africa. .......................................................................... 70 Table 12 – AMOVA results corresponding to groups formed with data from 76 populations with different regions and ethnics. ............................................................ 71 Table 13 – TAS2R38 haplotypes frequencies and diversities. .................................... 73 Table 14 – FST values based on TAS2R38 haplotype frequencies. ............................. 74 Table 15 – AMOVA results to TAS2R38 corresponding to groups formed with the 23 populations represented in figure 13. .......................................................................... 77 Table 16 – TAS1R1 haplotype frequencies and diversities. ........................................ 79 Table 17 – FST values correspondent to TAS1R1 haplotypes. .................................... 81 Table 18 – TAS1R3 umami haplotype frequencies and diversities. ............................ 83 Table 19 – FST values correspondent to TAS1R3 haplotypes. .................................... 85 Table 20 – AMOVA results correspondent to groups formed with 12 populations’ data from different regions and ethnics. .............................................................................. 86 Table 21 – TAS1R3 haplotype frequencies and diversities obtained to sweet taste. ... 87 FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 20 Table 22 – FST values correspondent to TAS1R3 haplotypes involved in sweet perception. .................................................................................................................. 89 Table 23 – AMOVA results corresponding to groups formed with 8 populations’ data from different regions and ethnics. .............................................................................. 91 Table 24 – AMOVA results to all genes in study regarding continental groups. .......... 93 FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 21 Figures Index Fig. 1 – Schematic and simplified representation of the principal dietary shifts since the split between human and chimpanzee ........................................................................ 29 Fig. 2 – Representation of human lifestyles, diet and population size in the last 10 000 years ........................................................................................................................... 31 Fig. 3 – Principal linguistic families of Sub-Saharan Africa simplified.. ........................ 33 Fig. 4 – Principal language families in Africa .............................................................. 33 Fig. 5 – Representation of African continent and part of Eurasia with the sampled countries labelled. ....................................................................................................... 51 Fig. 6 – PCR program used for Multiplex 1. ................................................................ 54 Fig. 7 – PCR program correspondent to Multiplex 2. .................................................. 55 Fig. 8 – Band patterns observed after the electrophoresis for Multiplex 1 (I) and Multiplex 2 (II) and respective fragments length and SNPs. ........................................ 56 Fig. 9 – Electropherograms from Multiplex 1 and Multiplex 2. ..................................... 59 Fig. 10 – Procedures for allele-specific Primer extension SNP assay. ........................ 60 Fig. 11 – Multidimensional scaling plot of FST values corresponding to TAS2R16 ....... 67 Fig. 12 – T516 frequency contour map of Africa and Eurasia ..................................... 69 Fig. 13 – Multidimensional scaling plot of FST values corresponding to TAS2R38 haplotypes .................................................................................................................. 75 Fig. 14 – Network of TAS1R1 haplotypes. ................................................................. 80 Fig. 15 – Network with TAS1R3 haplotypes influencing the umami taste. ................... 84 Fig. 16 – Network with TAS1R3 haplotypes correspondent to sweet taste. ................ 88 Fig. 17 – Multidimensional scaling plot of FST values to TAS1R3 sweet haplotypes. ... 90 Fig. 18 – Graphic representation of the percentage of variation among groups to all genes studied. ............................................................................................................ 94 FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 23 Abbreviations aa amino acid ALFRED Allele FREquency Database AMOVA Analysis of Molecular Variance BLAST Basic Local Alignment Search Tool BLAT BLAST-like Alignment Tool bp base pair ddNTP dideoxynucleotide triphosphate DNA Deoxyribonucleic Acid dNTP dideoxynucleotide triphosphate GMP Guanosine 5’-monophosphate GPCR G protein-coupled receptor He Expected Heterozygosity Ho Observed Heterozygosity IMP Inosine 5’-monophosphate LGM Last Glacial Maximum MDS Multidimensional Scaling µL microlitre mL millilitre Min minute MSG monosodium glutamate NCBI National Center for Biotechnology Information PCR Polymerase Chain Reaction PROP 6-n-propylthiouracil PTC phenylthiocarbamide RFLP Restriction Fragment Length Polymorphism rpm rotations per minute SAP Shrimp Alkaline Phosphatase Sec second SNP Single Nucleotide Polymorphism STR Short Tandem Repeat UCSC University of California, Santa Cruz YBP Years before Present FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 31 Fig. 2 – Representation of human lifestyles, diet and population size in the last 10 000 years. The first times of the transition to new dietary habits were characterized by the appearance of disorders caused by the non-adaptation of humans to them. Genetic and cultural alterations emerged, leading to agricultural population growth till present. Adapted from Patin and Quintana-Murci, 2008. Despite the innovations brought by agriculture, like other major changes, it was also accompanied by complications. During the transition period (figure 2) occasional diseases as anaemia and metabolic disorders must have been common, once the organism and genome were not adapted to the new food resources (Jobling et al., 2004; Balaresque et al., 2007). The drastic dietary change, the new alimentary behaviours and sedentary lifestyle, led to the emergence of a series of associated diseases that progressively would turn into major health problems, like diabetes, cardiovascular syndromes, cancer and obesity (Feero et al., 2010; Luca et al., 2010). The nowadays burden of these diseases in most populations suggests that possible genome adjustments initiated in the last 10 000 years, do not fit the needs to deal with the current dietary practices and standards of living (Luca et al., 2010; Ye and Gu, 2011). Besides genetic adaptations, other answers to the Neolithic innovation were given with a repertoire of cultural adaptations (figure 2). Among them, food processing was very important once it enabled the consume of certain types of food decreasing their FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 32 toughness and neutralizing their toxins. Furthermore, the dissemination of techniques such as cooking and fermentation, allowed the demographic growth and the maintenance of larger groups (Patin and Quintana-Murci, 2008). Agriculture did not have a single centre of origin. When it arose in Sub-Saharan Africa, it implied a major reshaping of the human landscape with the advent not only of farming communities but also of the pastoralist lifestyle. Besides, contrarily to what happened in most of the other continents, a few hunter and gatherer groups remained till present. 2.1.1. Diversity in Africa Africa is one of the most interesting continents from the anthropological point of view. Besides having been the region where hominids and modern humans evolved, Africa has a long-lasting rich history of people movements. In the last centuries, the most impressive was the massive African slave trade initiated with the Age of Discoveries, during which also occurred some influx, although minor, of Europeans into Africa. The continent possesses an extraordinary diversity: climatic, geographical (from deserts and mountain ranges to savannah and tropical rainforest) and even of infectious agents (some of them almost eradicated elsewhere). Furthermore, the highest levels of genetic diversity are present in African populations. Many distinct groups with a wide range of lifestyles can be found in Africa, hunter-gatherers, agriculturalists and pastoralists, notwithstanding the massive demographic impact of the farmer expansions that begun in Neolithic around 4000 YBP (Reed and Tishkoff, 2006; Campbell and Tishkoff, 2008). It is also very high the linguistic diversity. Languages spoken in Africa belong to different families (figures 3 and 4), three of which can be related to distinct lifestyles. Within the Niger-Kordofanian branch, are the Bantu languages, which are associated with the spread of agriculture. The Nilo-Saharan family contains languages spoken by pastoralists’ societies. The Khoisan branch is typical from hunter-gatherer groups; it is thought to be the most ancient linguistic family in Africa, which was dramatically abandoned due to the expansion of the Bantu languages during Neolithic (Tishkoff et al., 2009; Campbell and Tishkoff, 2010). FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 33 Fig. 4 – Principal languages families in Africa. The Niger-Kordofanian branch includes all the Bantu languages, which are primarily linked with agriculturalist lifestyle; the pastoral dispersion is associated with Nilo-Saharan languages and the remaining practitioners of hunting and gathering speak Khoisan-related languages. The area occupied by the language families is approximately the same filled by the related lifestyle. Adapted from Cavalli-Sforza and Feldman, 2003. Fig. 3 – Principal linguistic families of Sub-Saharan Africa simplified. Adapted from Cavalli-Sforza, 1997. African Niger-Kordofanian Nilo-Saharan Congo-Saharan Khoisan FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 34 2.1.1.1. Agriculture and its expansion Neolithic in Sub-Saharan Africa started around 5000 to 4000 YBP probably due to a severe change in climate from humid to drier, resulting in the introduction of new crops adequate to the changed conditions. Soon after, begun the Bantu expansions, a long series of migrations that would represent a remarkable process of farming/language co-dispersal (Diamond and Bellwood, 2003; Campbell and Tishkoff, 2010). The centre of origin of the diffusion of African farmers is widely thought to be an area around eastern Nigeria and western region of Cameroon (Diamond and Bellwood, 2003; Tishkoff et al., 2009; Campbell and Tishkoff, 2010; Pakendorf et al., 2011; Gignoux et al., 2011). From there, two distinct dispersal routes arose: one throughout western Africa and another towards central and eastern regions of the continent, both in direction towards south. As a result, within Bantu people, western subgroups tend to be somehow more diverse than eastern ones (Pakendorf et al., 2011). Furthermore, while most languages from Western Bantus is classified as “Forest” languages, those from Eastern subgroups are most often “Savannah” languages (Holden, 2006; Campbell and Tishkoff, 2010; Pakendorf et al., 2011). The agricultural expansion was highly correlated with the spread of Bantu languages through Sub-Saharan African. As a consequence of the massive codispersion, pre-existing hunter-gatherers and their culture were rapidly replaced and/or assimilated by the incomers Bantu-speakers. Evidence exists that intermarriage between Bantu and Khoisan people was a sex-biased process, having involved preferentially mattings between hunter-gatherer women and agriculturalist men (Diamond and Bellwood, 2003). Tracing the farming expansion routes has been a difficult endeavour, due to the complexity of the movements, which is further complicated by the fact of being quite common situations of multilingualism. Nowadays, however, the geographical distribution of Bantu languages and speakers vastly dominates the sub-Saharan Africa. (Berniell-Lee et al., 2009; Campbell and Tishkoff, 2010; Pakendorf et al., 2011). FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 35 2.1.1.2. Pastoral migrations Another lifestyle that emerged in Africa during the Neolithic was pastoralism. This mode of subsistence essentially relies on domestic herd products such as milk, blood and meat. Naturally, the main activities in pastoralist groups are related to animal husbandry. This lifestyle is deeply correlated with Nilo-Saharan languages spread among eastern and central Africa. A major original region from where herders become to migrate is thought to have been in the south of Sudan (Campbell and Tishkoff, 2010). Movements of small groups of herders to southern regions might have been coerced by the enlargement of arid areas surrounding the Sahel desert (Marshall, 1990; Campbell and Tishkoff, 2010). In Africa, pastoralists often complement herd products with others obtained through hunting, fishing, cultivation and foraging, which affords a richer diet. This form of pastoralism is normally named generalist, having appeared at least 4000 YBP. Still, in East Africa other varieties of pastoralists are present that are more specialized, living almost exclusively from herder labour and excluding all meat sources from wild nature (Campbell and Tishkoff, 2010). This form of pastoralism is considered to be more recent, arising around 3000 YBP, probably to cope with the climatic conditions in that time (Campbell and Tishkoff, 2010). Climatic changes seem had played a major role in driving the Nilo-Saharan expansion to surrounding regions, initially to north Kenya and Lake Chad. Still, a much smaller group expanded in direction to eastern Sahara (Campbell and Tishkoff, 2010). Around 3000 YBP in the late Holocene occurred another climate shift into a drier weather that resulted in a reduction of humid areas, such as lakes and rivers, facilitating the acclimation of cattle to these new territories. Then, around 3000 YBP, a distinct group from the east side of Sudan initiated their expansions to south, through routes essentially leading them to Uganda, Kenya and Tanzania regions. Nowadays they are denominated Nilotic pastoralists (Marshall, 1990; Bower, 1991; Campbell and Tishkoff, 2010). FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 36 2.2. Dietary Adaptations resulting from Neolithic innovations Increased population densities, reduced dietary diversity, sedentary lifestyle and exposure to animal pathogens, together represented a major set of challenges appearing with the Neolithic, some of which may still have an impact today. A common view is that the arrival of agriculture signalled the start of an era of dietary maladaptation and to face with that humans begun answering with a series of genetic adaptations (Balaresque et al., 2007). To elucidate which genetic adaptations might have evolved in response to dietary changes and the adoptions of dietary specializations, numerous genes have been investigated, especially those involved in food metabolism (Nielsen, 2005). Up to now, one of the most well evidenced cases of genetic metabolism adaptation refers to the human tolerance to lactase in later stages of life, regarding which groups of African pastoralists are clearly differentiated from non-pastoralist groups (Hollox et al., 2001; Powell et al., 2003). Copy number variation of the AMY gene was also associated with starch intake and lifestyle in human populations, accounting for differences between agriculturalists and hunter-gatherers (Perry et al., 2007). These findings support that some local adaptations exist which are correlated with dietary behaviours and main modes of subsistence adopted (Campbell and Tishkoff, 2010; Ye and Gu, 2011). However, feeding is a multisensory experience. The processing of food in the mouth leads to the release of molecules that stimulate, among others, the sense of taste (Luca et al., 2010). Taste is an important factor of food selection. Besides determining individual differences in food preferences, taste sensibility is very important to avoid the ingestion of substances like poisons or simply spoiled food (Bachmanov and Beauchamp, 2007). An additional function of taste is the detection of energetic food resources as a guarantee of individuals’ continuity and their reproduction (Drewnowski and Rock, 1995; Kim et al., 2004; Tepper, 2008). As so, a few studies appeared recently sought to understand whether taste in humans might also represent a genetic adaptation to dietary changes (Luca et al., 2010). FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 37 3. Taste Perception In general, the term “taste” has been applied to all sensations that come from oral cavity. However, its biological meaning only comprises the sensations originated from the stimulation of taste receptor cells by the chemical particles present in food consumed. The taste system comprises several complex and specialised structures that allow the identification of different types of taste (Bachmanov and Beauchamp, 2007; Luca et al., 2010). Nowadays are widely recognized five basic tastes: bitter, umami, sweet, salty and sour (Drewnowski, 2002), although new qualities of taste have been proposed in recent years such the ability to taste water and fat (Laugerette et al., 2005; Laugerette et al., 2007). Each of the 5 basic tastes is associated to specific taste receptors that transmit the signal to the brain in order to obtain the corresponding taste perception (de Krom et al., 2009). Despite the number of studies performed in the last years to better understand the mechanism of taste perception, it is still poorly known how genetic variation influences taste perception (Wooding, 2005). 3.1. Bitter Taste The bitter perception has presumably evolved to avoid the ingestion of plant toxins, provoking an unpleasant sensation in mouth which induces their rejection. Bitter compounds have high molecular diversity and are found in a wide range of plants such as cruciferous vegetables, spinach and endives. Furthermore, its presence is detected in other non-natural types of food, such for example certain cheeses, products with soy, beer and coffee (Keller et al., 2002; Garcia-Bailo et al., 2009). The sensibility to bitter has been intensively study since the 1930s, after an interesting observation made by Arthur L. Fox, during his work with a compound named phenylthiocarbamide (PTC) and its related 6-n-propylthiouracil (PROP). Testing PTC sensitivity in a large population sample, he discovered two principal phenotypic classes of perception: one contained people who were sensible to low concentrations of PTC compound, denominated tasters, and the other those who only tasted PTC at high concentrations, the non-tasters. Although PTC has being the more known and studied compound eliciting bitter taste, it is not naturally present in food, but it triggers a similar response to the one FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 38 obtained with isothiocyanates, isoflavones and polyphenols, just citing few examples (Wooding, 2006; Garcia-Bailo et al., 2009; Tepper et al., 2009; Feeney et al., 2011). The bitter receptors are encoded by a large family of seven-transmembrane G protein-coupled receptor (GPCR) genes named TAS2R. The gene family is composed by at least 38 functional genes and 5 pseudogenes which are organized in gene clusters located in three chromosomes, 5p, 7p and 12p, and it has evolved through gene duplication during mammalian evolution (Bufe et al., 2005; Drayna, 2005; Tepper et al., 2009; Ye and Gu, 2011). TAS2R38 is the more known gene of this family because it is responsible for PTC sensitivity. Its length is around 1143 base pairs (bp), codes for 333 amino acids (aa) and its location is on chromosome 7 (http://www.ensembl.org). The PTC genotype is commonly accessed by studying three functional single nucleotide polymorphisms (SNPs), which are present in table 1. They define different haplotypes, among which are PAV and AVI haplotypes that are strongly associated to tasters and non-tasters phenotypes, respectively. Table 1 – Single Nucleotide Polymorphisms linked to PTC perception in TAS2R38. Reference SNP (rs) Position in sequence (bp) Possible Alleles (bp) Position in sequence (aa) Possible Alleles (aa) rs713598 145 C 49 Proline G Alanine rs1726866 785 C 262 Alanine T Valine rs10246939 886 G 296 Valine A Isoleucine Another gene from the family that is calling attention is TAS2R16. It is also located at chromosome 7, spanning 996 bp that encoding 291 aa (http://www.ensembl.org). This gene determines the sensitivity to bitter β-glucopyranosides, which are present in a wide range of plants and are characterised by high toxic cyanogenic activity. The receptor encoded by TAS2R16 also mediates the signaling response to salicin and amygdalin. Moreover, the gene has been studied in the context of the behaviour of alcohol intake (Soranzo et al., 2005; Garcia-Bailo et al., 2009; Ye and Gu, 2011). So far, only one SNP at TAS2R16 (rs846664) – G516T – has been associated to increased β-glucopyranosides sensibility (Bufe et al., 2002). Both genes show signs of being under selection (Wooding, 2011). In the case of TAS2R38, it has been found to be under balancing selection due the excess of FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 39 intermediate frequency variants. Concerning TAS2R16, based on the very high frequency of some derived alleles involving nonsynonymous nucleotide changes, it was also proposed that they were putative targets of selection (Ye and Gu, 2011). Nevertheless, the reasons underlying the detected selection signals remain unclear. 3.2. Umami Taste The umami taste was the last to be included in the list of taste qualities and it is usually described as a savoury flavour. It was firstly identified by Kikunae Ikeda in 1908 (Lindemann et al., 2002; Kurihara, 2009), hence the origin of the Japanese word umami that means good taste. It is used to describe the taste of the amino acid Lglutamate, usually present in food as monosodium glutamate (MSG), together with Laspartate. Inosine 5’-monophosphate (IMP) and guanosine 5’-monophosphate (GMP) are responsible for increased MSG and L-aspartate umami gustation. The umami taste is characteristic of meat, fish, milk, cheese, mushrooms, potatoes, tomatoes, soy and seafood, among others (Kurihara and Kashiwayanagi, 2000; Chaudhari et al., 2009; Garcia-Bailo et al., 2009). The umami receptors are very complex, but also implicate G protein-coupled receptor genes, among which are TAS1R1 and TAS1R3, which harbour several SNPs known to be correlated with umami sensibility. These genes, as well as TAS1R2, belong to the TAS1R gene family that is located in a single cluster on chromosome 1 (Li et al., 2002; Zhao et al., 2003). To enable the perception of umami taste, the proteins encoded by genes TAS1R1 and TAS1R3 that are predominantly expressed in taste buds need to form heteromeric taste receptors. Besides the referred umami receptor, two glutamate-selective GPCRs, mGluR1 and mGluR4, have also been proposed as candidate taste receptors for umami (Nelson et al., 2002; Chaudhari et al., 2009; Garcia-Bailo et al., 2009; Shigemura et al., 2009a; Shigemura et al., 2009b). Till this point, several SNPs were identified in both genes shown to affect umami sensitivity, having TAS1R1 more polymorphisms than TAS1R3, which can be related with TAS1R3 double function in the perception of umami and sweet tastes (Kim et al., 2006; Garcia-Bailo et al., 2009; Chen et al., 2009). Despite that, the understanding about how genetic variations influence the sensibility to umami and food choice remains limited. FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 40 3.3. Sweet Taste Food containing sweet substances usually provides a pleasant sensation in humans, possibly reflecting evolutionary pressures to select foods high in energy (Hladik et al., 2002). Most often the sweet perception in humans is elicited by natural sugars, like glucose, fructose, sucrose, and sugar alcohols such as sorbitol. Several other natural compounds, structurally unrelated to carbohydrates, also taste sweet. Most commonly, certain amino acids, such as glycine, taste sweet to humans. Furthermore, artificial sweeteners have been developed activating similar responses to those obtained with natural sugars (Bachmanov et al., 2002; Breslin and Spector, 2008; Boughter and Bachmanov, 2007; Garcia-Bailo et al., 2009; Bachmanov et al., 2011). As umami taste, sweet is also dependent on proteins encoded by genes of TAS1R family and GPCRs. In this case, the genes involved are TAS1R2 and TAS1R3, whose proteins form a heteromer enabling sweet identification in the taste buds of oral cavity. Comparative levels of gene variability, reveal that TAS1R2 is notably more polymorphic than TAS1R3 or TAS1R1, suggesting that variation at TAS1R2 generates the ability to perceive a wide variety of structurally different sweet substances (Garcia-Bailo et al., 2009). The TAS1R3 gene has demonstrated strong association with differences in sweet taste perception of saccharin (Nelson et al., 2002; Zhao et al., 2003; Kim et al., 2006). Despite the discovery of these genes as coding for an heteromer responsible for sweet taste detection, the number of sweet taste receptors that exist is still unresolved (Garcia-Bailo et al., 2009; Bachmanov et al., 2011). 3.4. Salty Taste The salty sense can be stimulated by several ions like Li+, K+ and NH4+, but the most effective stimulus of this sensation is NaCl (DeSimone and Lyall, 2006; Roper, 2007). NaCl is required to preserve the electrolyte equilibrium, regulate blood pressure and volume. Furthermore, NaCl provides ions that have crucial roles in many physiological mechanisms (Kim et al., 2004; Chandrashekar et al., 2006). Salt recognition is thought to occur in sodium channels from the epithelium, although in humans other mechanisms were proposed to have a role in its perception. Even so, the molecular mechanism responsible for salt taste perception in humans FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 47 In order to investigate the relationship between variability in taste perception and lifestyle, African populations with distinct modes of subsistence will be studied. The interest in African populations relies in several reasons, among which is the fact that Africa has revealed to have the highest levels of diversity at worldwide scale (Campbell and Tishkoff, 2008). Despite that, the knowledge about genetic diversity in this continent remains scarce. Besides, the three main modes of subsistence – hunting-gathering, pastoralism and agriculture – are still represented in Africa, with fewer modifications than in other regions of the world. After a brief review for each taste and their candidate genes, three qualities of taste were selected to be our focus: bitter perception, due to its importance to prevent toxin ingestion, and sensitivity to sweet and umami, both implied in detection of energetic sources (Feeney et al., 2011). To obtain insights on the main question under study, the present work aims at reaching the following objectives: 1. Design a PCR-Multiplex reaction selecting a battery of relevant SNPs, known to be implied in taste sensibility; 2. Characterize three African agrarian societies – Angola, Mozambique and Equatorial Guinea – and one pastoralist from Karamoja region (Uganda) for the previous selected SNPs; 3. Genotype an European control group, which will be represented by a sample of Portuguese, with the same battery of SNPs; 4. Explore the correspondent patterns of diversity and their relation with the different lifestyles of the studied populations; 5. Identify some of the factors that have influenced the detected patterns of genetic diversity. In addition, the utility of these polymorphisms in the forensic genetics field, such as DNA phenotyping, will be evaluated, since they can be a useful complement to already used polymorphisms linked to phenotypic traits, increasing the discrimination power of the entire set. III. MATERIAL & METHODS FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 51 1. Samples and DNA extraction In order to perform the present work, samples from four African and one European country (figure 1) have been analysed. To characterize the agrarian societies we have used 27 samples of Bantu speakers from Angola, mainly from Cabinda; 34 from Mozambique; and 76 samples from Equatorial Guinea. To represent the pastoralist lifestyle, 49 male individuals, belonging to an ethnic group living in the north-east region of Uganda (Karamojong people) and who speaks an Eastern Nilotic language, have been typed. A control group comprising 49 samples from Portugal has been used as a reference from outside Africa. Once data about the dietary habits of the selected African populations were not collected during the sampling, their characterization was performed attending to linguistic family, region and other information obtain in literature. The samples have been extracted previously for other works, through three different methods – Chelex®-100TM (Biorad) described by Lareu et al. (1994), phenolchloroform (Maniatis et al., 1989) and commercial Generation® Capture Card kit (Gentra Systems Inc, Minneapolis, USA) – following the standard protocols. Fig. 5 – Representation of African continent and part of Eurasia with the sampled countries labeled. Portugal (n=49) Uganda (n=49) Equatorial Guinea (n=76) Angola (n=27) Mozambique (n=34) FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 52 2. Amplification Multiplexes Design 2.1. Genetic Markers and Target polymorphisms selection One of the most important steps in this project was to select carefully the markers to be analysed. All the markers selected were single nucleotide polymorphisms, also known as SNPs. To access the genomic information about taste singularities in pastoralist and agriculturalist lifestyles, a battery of eleven SNPs distributed across four taste-related genes have been studied. The genes selected were associated with bitter, umami and sweet tastes; this assignment was performed considering the relevance of polymorphisms described in the literature and its effects in taste perception. In relation to umami taste, 2 SNPs from TAS1R1 gene (rs41278020 and rs34160967) and 3 from TAS1R3 (rs76755863, rs111615792 and rs307377) were selected. As for the sweet taste, two SNPs from gene promoter of TAS1R3 were chosen – rs307355 and rs35744813. Concerning the bitter perception, the SNP rs846664 from TAS2R16 gene and 3 SNPs from TAS2R38 gene – rs713598, rs1726866 and rs10246939 – were included in the present work. FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 53 2.2. PCR Multiplex amplification Viewing maximum efficiency in laboratory routine, nine pairs of primers have been designed to amplify in multiplex the specific DNA regions containing the target SNPs, which allows the amplification of a large number of fragments at the same time. The gene sequences were retrieved from Ensemble Genome Browser (Ensembl 64 – Sep 2011) (http://www.ensembl.org) and used in Primer3 v.0.4.0 software (www://frodo.wi.mit.edu/) (Rozen and Skaletsky, 2000) to design the amplification primers avoiding annealing with polymorphic regions. To determine the specificity of the primers retrieved by Primer3, a double test was performed to each pair in PrimerBLAST, provided by NCBI (http://www.ncbi.nlm.nih.gov/tools/primer-blast/), and UCSC In-Silico PCR (http://genome.csdb.cn/cgi-bin/hgPcr), choosing only the human genome. If any of the oligonucleotides anneals in other region(s) of the genome, it could imply a less efficient reaction or a non-specific amplification. Attending this, all the primers were tested individually in BLAST (NCBI) (http://blast.ncbi.nlm.nih.gov/) and BLAT (UCSC) (http://genome.ucsc.edu/cgi-bin/hgBlat?command=start). The last step of multiplex design consisted in using the AutoDimer software version 1.0 and OligoCalc (http://www.basic.northwestern.edu/biotools/OligoCalc.html) to check if the designed amplification primers could form primers dimers and/or hairpins, which could contribute to decrease the reaction efficiency. In table 2 are present all the amplification primers and SNPs of interest. Table 2 – Amplification primers and their characteristics. Gene SNP Allele Forward Primer (5’->3’) Reverse Primer (5’->3’) bp Forward bp Reverse Product Size TAS1R1 rs41278020 C329T TCAATGAGCATGGCTACCAC CACCGTAGGGGAATAGTGGA 20 20 218 TAS1R1 rs34160967 G1114A CCTGAAGGCGTTTGAAGAAG GGCAGAACTCATGGAGAAGG 20 20 160 TAS1R3 rs76755863 G13A CCTGTTGGAAGTTGCCTCTG ACGTAGTCCCCCTTCATCCT 20 20 129 TAS1R3 rs111615792 G740A GGGCCTGAGCATCTTCTCG ACCACCTGCACGCTGCTC 19 18 150 TAS1R3 rs307377 C2269T CTGGCCTTTCTCTGCTTCCT CAAAGGAGACCCAGGTGATG 20 20 119 TAS1R3 rs307355 -C1572T CGTGTGTGCTGTGAGCGTA AATATGGCGCACATGCGAA 19 19 520 TAS1R3 rs35744813 -C1266T TAS2R16 rs846664 G516T GGCTGAGGTGGAGAATTTTG CCAGGAACAGGATGAAAGGA 20 20 231 TAS2R38 rs713598 C145G CAATGCCTTCGTTTTCTTGGTG GATGGCTTGGTAGCTGTGGT 22 20 190 TAS2R38 rs1726866 C785T CCCACATTAAAGCCCTCAAG TCTCCTCAACTTGGCATTGC 20 20 197 TAS2R38 rs10246939 G886A FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 54 2.3. Optimization of the multiplex In order to ensure the minimal primer dimer formation and to improve the amplification reaction performance two independent multiplexes were constructed joining the pairs of primers that better fit together. All the amplification reactions were executed in 2720 Thermal Cycler (Applied Biosystems) and Thermal Cycler (BioRad). Multiplex 1 Multiplex 1 (M1) included the 5 SNPs selected from TAS1R3 gene, rs34160967 from TAS1R1 and rs713598 from TAS2R38 gene. Before amplification reaction, a mixture containing all the forward and reverse primers has been prepared so that all of them had a final concentration of 2.0 µM in the mix. The final concentrations and volumes used in each PCR reaction are present in table 3. Table 3 – Volumes added in one PCR reaction of Multiplex 1 for Portugal, Uganda, Mozambique, Angola and Equatorial Guinea samples. Reagents 1 reaction MyTaq™ HS Mix (Bioline) 5 µL Primers Mix (M1) 1 µL DNA 1 µL* Deionized water till 10 µL *In some cases, such as Angola samples, larger volumes of DNA were used, always performing a final volume of 10 µL. The multiplex PCR amplification program used is described in figure 6. Fig. 6 – PCR program used for Multiplex 1. 95 oC 94 oC 72 oC 61 oC 94 oC 72 oC 65 oC 72 oC 1 min 10 min 90 sec 30 sec 1 min 90 sec 30 sec 15 min 1x Hold 2 Hold 3 Hold 4 30x 1x 7x Hold 1 FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 55 Multiplex 2 Multiplex 2 (M2) was constituted by the remaining SNPs - rs41278020 and rs846664 from TAS1R1 and TAS2R16 genes, respectively and rs1726866 and rs10246939 belonging to TAS2R38 gene. As in Multiplex 1, a previous mixture comprising forward and reverse primers has been done. All the primers were in the same concentration (2.0 µM) before being added to the PCR reaction. The volumes required to one PCR reaction are presented in table 4. Table 4 – Volumes needed in one PCR reaction of Multiplex 2 for Portugal, Uganda, Mozambique, Angola and Equatorial Guinea samples. Reagents 1 reaction Qiagen® PCR Multiplex Kit 5 µL Primers Mix (M2) 1 µL DNA 1 µL* Deionized Water till 10 µL *In some cases, such as Angola samples, larger volumes of DNA were used, always performing a final volume of 10 µL. To carry out the PCR amplification for M2, the program presented in figure 7 was used. Fig. 7 – PCR program correspondent to Multiplex 2. 95 oC 94 oC 72 oC 58 oC 94 oC 72 oC 56 oC 72 oC 1 min 10 min 90 sec 30 sec 1 min 90 sec 30 sec 15 min 1x Hold 2 Hold 3 Hold 4 28x 1x 10x Hold 1 FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 56 2.4. Electrophoresis To detect the amplified fragments and to test for the possible existence of contamination, a polyacrylamide gel (T9C5) electrophoretic run was performed for each sample. The gel was stained following the Silver Staining standard procedure (figure 8). I. II. C2269T - 119 bp G13A - 129 bp G740A - 150 bp G1114A - 160 bp C145G - 190 bp -C1572T & -C1266T - 520 bp C785T & G886A - 197 bp C329T - 218 bp G516T - 231 bp Fig. 8 – Band patterns observed after the electrophoresis for Multiplex 1 (I) and Multiplex 2 (II) and respective fragments length and SNPs. IV. RESULTS & DISCUSSION FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 65 1. Locus by locus approach From the analysis of expected and observed heterozygosity, no deviations to Hardy-Weinberg Equilibrium were detected among our populations after the application of the Bonferroni correction for multiple tests (supplementary table 1). 1.1. Bitter taste To access the genetic variation that influences bitter perception in human populations, two genes were selected – TAS2R16 and TAS2R38 – because, before, both had been clearly associated to the phenotypic variability in the discernment of bitter-tasting compounds. TAS2R16 As previous referred, TAS2R16 codes for a receptor mediating response to various β–glucopyranosides commonly found in nature that elicit a bitter taste. Within this gene only the SNP G516T (rs846664) was selected to be here screened because previous transient transfection studies have demonstrated that the two alleles at this SNP confer different ability to taste several glycosides compounds: the ancestral G516 allele was linked to lower sensitivity to those compounds comparing to the derived T516 allele (Soranzo et al., 2005). For simplicity, G516 will be referred to as the non-taster allele whilst T516 as the taster one. Estimates of T516 allele frequencies for the populations studied are presented in table 9. Table 9 – TAS2R16*T516 allele frequency plus standard deviation in different populations. Sample Allele Uganda (n=49) Angola (n=27) Mozambique (n=34) Equatorial Guinea (n=76) Portugal (n=49) T 0.8571±0.0355 0.6034±0.0648 0.7059±0.0557 0.7368±0.0358 1.0000±0.0000 In the four African populations, the ancestral G516 allele was usually found at much lower frequencies than the derived T516 and it was not detected in the individuals typed from Portugal. These results are in accordance with data previously reported for other populations revealing that the frequency of the taster allele, T516, is FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 66 1 or very near fixation outside Africa (Soranzo et al., 2005). Apart from Africa, G516 was only rarely found in Middle Eastern populations or other with recognized African ancestry, such as African Americans (Hinrichs et al., 2006). Concerning the African populations here studied, all three Bantu-speaking/ agrarian populations – from Angola, Mozambique and Equatorial Guinea – showed higher frequencies of the non-taster G516 allele comparatively to the pastoralist society from Uganda. This allele reached its maximum frequency in Angola, being present in ~40% of Angolan chromosomes. In order to put our results in a broader framework we recruited data available from the literature (Soranzo et al., 2005; Hinrichs et al., 2006) and from the HapMap (http://hapmap.ncbi.nlm.nih.gov/) (The International HapMap Consortium, 2010) or ALFRED (http://alfred.med.yale.edu/) (Rajeevan et al., 2012) databases (supplementary table 3). A total of 76 populations were used in this comparative analysis. For G516, values of identical magnitude to that estimated for Angola were only registered in the Yoruba (36.7%) and the Ibo (42.4%), two agriculturalist groups from Nigeria, or in the Lisongo (38%), a group from Democratic Republic of the Congo. As for the distribution of the derived taster T516, the highest frequency in African populations here tested was detected in the pastoralist population from Uganda (85.7%). In Africa, very elevated values (up to 75%) for this allele have been also reported in other pastoralist groups (the Mozabite from Algeria, the Maasai from Kenya or the Bedouin from North Africa), in hunter-gatherers (the San and the Biaka and Mbuti pygmies), and some agriculturalist populations (for instance Amhara and Zaramo, from Ethiopia and Tanzania, respectively). However, frequencies lower than 75% in Africa were only observed in agrarian groups. Pairwise FST values were calculated between the populations typed in this study yielding the values presented in table 10. Table 10 – FST values among Uganda, Angola, Mozambique, Equatorial Guinea and Portugal for TAS2R16 SNP. Mozambique Uganda Angola Equatorial Guinea Portugal Mozambique * Uganda 0.0644 * Angola 0.0155 0.1593 * Equatorial Guinea 0.0000 0.0420 0.0299 * Portugal 0.3372 0.1423 0.4703 0.2289 * The significant values after Bonferroni’s correction are in blue. Significant genetic distances were found between the 3 African agrarian populations and Uganda, as well as between all African populations and Portugal. Yet, FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 67 when the Bonferroni correction for multiple tests was applied, concerning African populations only the distance between Uganda and Angola remained significant. Differences between Portuguese and Africans (0.1423 to 0.470) were in general considerably larger then between pairs of African populations (0.0000 to 0.1593), and the largest FST values observed were between Portugal and the agrarian African societies, especially from Angola (0.4703) and Mozambique (0.3372). The population from which Portugal differed less was the pastoralist from Uganda (0.1423). Genetic distances were also obtained considering not only the populations here studied but in addition those listed in supplementary table 3; the corresponding FST values are shown in supplementary table 6. To obtain a visual representation of the relationships between all populations, the matrix of FST distance was used to build a MDS plot which is present in figure 11. MZ BP MP AN Ca EG UG Hs YRI Ibo MS BSA Tun MA Be MKK AE LWK Zrm Ch San Sdw EJ Me MEX Cam Surui c) Drz Kw Plt Ad Brg Tus Sa Ts AA ASW Bsq PT Jp CEU a) YJ Br Ba b) Ha Mak Pp CHB Maya Co BNE -0,40 -0,30 -0,20 -0,10 0,00 0,10 0,20 0,30 0,40 0,50 -1,00 -0,50 0,00 0,50 1,00 1,50 2,00 Dim 2 Dim 1 S-stress=0.04749 0.0 0.0 0.5 0.4 0.3 0.2 0.1 -0.1 -0.2 -0.3 -0.4 -1.0 -0.5 0.5 1.0 1.5 2.0 Fig. 11 – Multidimensional scaling plot of FST values corresponding to TAS2R16 SNP. AN: Angola; EG: Equatorial Guinea; MZ: Mozambique; UG: Uganda; PT: Portugal; Ca: Cameroon; Li: Lisongo; YRI: Yoruba; Ch: Chagga; ASW: African ancestry in Southwest USA; Hs: Hausa; MP: Mbuti Pygmies; MS: Mandenka ; BSA: Bantu from South Africa; BNE: Bantu from North-eastern Africa; BP: Biaka Pygmies; AA: African-American; Zrm: Zaramo; EG: Equatorial Guinea; MKK: Maasai; LWK: Luhya; MA: Mozabite; AE: Amhara; Sdw: Sandawe; EJ: Ethiopian Jews; Plt: Palestinian; Br: Brahui; Tus: Tuscan; Cam: Cambodian; Sa: Sardinian; Tun: Tunisian; MEX: Mexican; Ts: Tsaatan; Co: Colombian; Kw: Kuwaiti; Ad: Adygei; Pp: Papuan; Ha: Hazara; Orc: Orcadian; YJ: Yemenite Jews; Be: Bedouin; Bsq: Basque; Brg: Bergamo; Drz: Druze; Mak: Makrani; JPT: Japanese; CEU: Utah residents with European ancestry. Xibo, Mongolian, Tu, Miaozu, Yizu, She, Tujia, Hezhen, Dai, Lahu, Naxi, Daur, Oroqen and Uygur are represented by a) and b) contains Pathan, Burusho, Kalash, Russian, Pima and Yakut populations. In c) are represented Sindhi, French and Karitiana individuals. FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 68 As can be seen, the 75 worldwide populations, encompassing all continents, are grouped in two main clusters – one with the majority of African populations (red circle) and other with all the Non-African populations plus a few of North-African ones such as Bedouin and Tunisian (blue circle). Figure 11 also reveals that non-African populations are quite concentrated in their cluster, whereas the African ones stand much more dispersed between each other. By other words, this means that diversity for G516T is considerably reduced out of Africa compared to that registered inside the African continent. Within the African cluster, the sample from Uganda, the Maasai from Kenya (MKK) and the Mozabite from Algeria, all constituting pastoralist groups, are positioned in the periphery of the cluster (green oval), quite near the group of non-African populations. Hunter-gatherers, such as the San or the Biaka and Mbuti Pygmies, also assemble near each other (orange triangle). So, despite not being clearly differentiated from the remaining African populations, either pastoralists or hunter-gatherers tend to cluster with populations with identical lifestyle. African agriculturalist populations are much more widely dispersed in the African cluster, but, interestingly, Bantu-speaker groups mainly from Western Sub-Saharan Africa (samples from Angola and Cameroon, or the Ibo) occupy in the MDS plot another peripheral side on the cluster of African populations. Angola, Cameroon and the Ibo are indeed the populations among which the frequency of the non-taster G516 allele systematically reaches the highest values. In overall, the clustering pattern of the African populations, although indicating that the distribution of G516T in Africa might be in a certain extent related with the typical lifestyle of populations, suggests that geography is a variable clearly influencing the diversity at this polymorphism. In figure 12 is illustrated the contour map across Africa, Europe and Asia of T516 allele frequency. FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 69 Fig. 12 – T516 frequency contour map of Africa and Eurasia. The remaining continents (Oceania and America) were omitted due the lack of enough data. This distribution is quite intriguing since in the African continent it can be seen a remarkable correspondence between the frequency distribution of this SNP and the Bantu dispersion. In fact, the region showing the lowest frequencies of T516 is rather coincident with the centre of origin of Bantu languages (Diamond and Bellwood, 2003; Jobling et al., 2004) in Cameroon and Eastern Nigeria. From that region, the frequency of the taster allele tends to increase, suggesting a dispersion pattern that evokes the Bantu dispersal routes. Since agriculturalist lifestyle is intrinsically correlated with Bantu people, the strong representativeness of the ancestral allele in these populations can be related to their dietary habits, possibly indicating that the detection of glycosides compounds to agrarian groups may not be as relevant as to pastoralists and huntergatherers. To further dissect the factors contributing to explain the pattern of distribution of G516T, several AMOVA tests were performed using different criteria to establish groups of populations. Firstly we have restricted the analysis to African populations and grouped them according to lifestyle, geography and malaria risk. This latter assay was tested because previously, Soranzo et al. (2005), based on a certain similarity in the distribution of the low-sensitivity allele and that of some malaria-resistant alleles, speculates that the ancestral G516 allele could confer a modest selective advantage in malaria-infested 0.50 0.60 0.70 0.65 0.75 0.80 0.55 0.85 0.90 0.95 1.00 FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 70 areas through favouring chronic low level ingestion of cyanogenic glycosides. Although some β-glucopyranosides compounds are toxic to humans, their moderate consume has been associated with an increased protection against malaria (Soranzo et al., 2005; Jackson, 1990). Information for classifying populations based on malaria risk was recruited from http://cdc-malaria.ncsa.uiuc.edu/. In the three AMOVA scenarios tested, the major proportion of variation was always ascertained to the within populations component, which was never less than ~93% of the total variation observed for G516T (table 11). Table 11 – AMOVA results in Africa. Source of Variation Percentage of variation P-value Lifestyle Among groups 3.11 0.0287±0.0017 Among populations within groups 3.80 0.0000±0.0000 Within populations 93.09 0.0000±0.0000 Geography (by region) Among groups 3.21 0.0085±0.0009 Among populations within groups 2.09 0.0002±0.0001 Within populations 94.70 0.0000±0.0000 Malaria Distribution Among groups 4.12 0.0045±0.0007 Among populations within groups 2.95 0.0000±0.0000 Within populations 92.93 0.0000±0.0000 However, the proportions of variation among groups, although being incomparably much smaller than those due to differences within populations, were also always significant in three distinct scenarios – lifestyle, geography and malaria distribution. When lifestyle was considered (hunting-gathering, agriculture and pastoralism) the percentage of variation among groups was 3.11%. That proportion increased to 3.21% when the geographical criterion was applied (the groups were: Central Africa, East Africa, West Africa, South Africa, North Africa and South-East Africa). But the variation among groups reached the highest value, 4.12%, when the malaria risk was used in the clustering system. Taken together these results indicates that, from the 3 factors tested, malaria risk contributes more to explain the distribution of G516T in Africa than geography, and FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 71 both malaria risk and geography more than lifestyle. Since malaria distribution in Africa is obviously correlated with geography, the meaning of the differences in the AMOVA tests need to be interpreted with much caution. Next, AMOVA was performed considering the entire set of populations available across the world using the same three criteria for clustering populations, but additionally testing African versus non-African populations when geography was assumed. Results are presented in table 12. Table 12 – AMOVA results corresponding to groups formed with data from 76 populations with different regions and ethnics. Source of Variation Percentage of variation P-value Lifestyle Among groups 1.69 0.1426±0.0038 Among populations within groups 7.73 0.0000±0.0000 Within populations 90.58 0.0000±0.0000 Geography (by region) Among groups 14.49 0.0000± 0.0000 Among populations within groups 2.20 0.0000± 0.0000 Within populations 83.31 0.0000±0.0000 Malaria Distribution Among groups 7.76 0.0002±0.0001 Among populations within groups 10.34 0.0000±0.0000 Within populations 81.90 0.0000±0.0000 Africa vs. Non-Africa Among groups 15.43 0.0000±0.0000 Among populations within groups 6.86 0.0000±0.0000 Within populations 77.72 0.0000±0.0000 At a worldwide level, differences among populations determined by lifestyle decreased to 1.69% and lost statistical significance. Contrarily, the proportions of variation ascribed to geography or malaria risk substantially increased, reaching very highly significant values. However, whereas malaria distribution accounted to explain 7.8% of the total variation at G516T, geography explained approximately two times more differences among groups: 14.49% FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 72 when the region of origin was considered and even more, 15.43% when Africans were contrasted to non-Africans. The recognition of bitter natural toxins is thought to may have conferred an important selective advantage during human evolution, alerting humans to noxious foods (Soranzo et al., 2005), having been admitted that in hunter-gatherers’ communities, sensitivity to bitterness was presumably advantageous because of various noxious plants (Li et al., 2011). Both Soranzo et al. (2005) and later Li et al. (2011) reported on signs of selection at TAS1R16, with the first authors having further hypothesized that the global pattern at this gene has resulted from a balance between protection against malaria and protection against toxins in malaria-free zones. Our findings indicate that whereas in Africa the distribution of G516T is only faintly correlated with lifestyle, demographic factors, likely related with the Bantu expansion, might have played an important role in shaping diversity at this SNP. In fact, the excess of the non-taster G516 allele in Western regions of Africa affected by malaria, which Soranzo et al. (2005) interpreted as a sign of selection driven by malaria, seems as well be explainable by the effects of the Bantu expansion. For this polymorphism differences between Africans and non-Africans, although statistically significant, are not higher than usually registered, indicating that bottleneck effects consequent to the out of Africa dispersion of humans seem enough to explain the differences observed. FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 79 1.2. Umami taste As already mentioned in point 3.2 of chapter I (Introduction), umami taste is one of five basic taste qualities and plays a key role in the intake of amino acids. Umami is elicited by L-glutamate, typically as its Na salt (monosodium glutamate: MSG), some amino acids and purine nucleotides (such as IMP and GMP). It is known that taste sensitivity to umami substances varies widely among individuals. Distribution of individual MSG thresholds shows a bi-modal curve, and taste thresholds of MSG differ about 5-fold between taster and hypotaster groups (Lugaz et al., 2002; Shigemura et al., 2009b). One well established umami receptor is a heterodimeric G protein-coupled receptor, consisting of the proteins T1R1 (taste receptor type 1, member 1) and the T1R3 (taste receptor type 1, member 3), which are encoded by TAS1R1 and TAS1R3 genes, respectively (Nelson et al., 2002). Both TAS1R1 and TAS1R3 were studied in this work, using the information provided by the haplotypes defined by the SNPs screened in each gene. Due to the very high rate of unsuccessful genotyping in the sample from Angola, this sample was excluded from the TAS1R1 and TAS1R3 analyses. TAS1R1 In this work we selected two common non-synonymous SNPs at TAS1R1 showing evidence of being implied in umami perception: C329T and G1114A (Shigemura et al., 2009b; Raliou et al., 2009). Since the T allele at position 329 and the G at position 1114 were both associated with low sensitivity to glutamate, we assumed that the TG haplotype confers low umami perception, the CA haplotype high sensitivity, while the remaining two combinations are intermediate haplotypes (Raliou et al., 2009). Table 16 – TAS1R1 haplotype frequencies and diversities. Sample Haplotype Uganda (n=37) Mozambique (n=32) Equatorial Guinea (n=76) Portugal (n=49) CA 0.0541±0.0258 0.0156±0.0155 0.0592±0.0185 0.1122±0.0321 CG 0.9459±0.0258 0.9844±0.0155 0.9211±0.0209 0.8674±0.0341 TG - - 0.0197±0.0108 0.0204±0.0141 Haplotype Diversity 0.1037±0.0469 0.0312±0.0300 0.1487±0.0382 0.2371±0.0529 FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 80 In table 16 are presented the TAS1R1 haplotype frequencies estimated for Uganda, Mozambique, Equatorial Guinea and Portugal. Three different haplotypes were found, CA, CG and TG, with CG, the unique intermediate haplotype detected in the present work, being the overwhelmingly predominant haplotype in the four populations, occurring at its lowest frequency, 86.7%, in Portugal. The CA haplotype was also shared by all populations, but showed the highest frequency among the Portuguese, 11.2%. The TG combination was only rarely found in Equatorial Guinea and Portugal, accounting for around 2% of chromosomes in both populations. For the TAS1R1 locus, Portugal presented greater haplotype diversity than any of the 3 African populations here studied, which is in opposition to the usual observation of highest levels of diversity among Africans. Nevertheless, haplotype diversities for all populations are visible moderate or low. For TAS1R1 few data was available for the comparisons. The more comprehensive study across the world was performed by Kim et al., 2006, who have analysed a total of 8 populations, one from African (Cameroon), other from NativeAmerican and the remaining from Eurasian regions. However the sizes of the 8 samples were particularly low (minimum 8, maximum 20 individuals per population), and so the study only provided rough estimates of haplotype frequencies for TAS1R1. Even so, data from the study of Kim et al. (2006) and from our own work are globally concordant (figure 14). The 3 different haplotypes here identified were also the unique reported by Kim et al. (2006). These authors only detected the intermediate haplotype, CG, in 7 out the 8 studied populations, which is understandable given, at one hand, the extremely high frequency of this haplotype, and on the other hand, the very small number of individuals analysed. Likely, if samples sizes were enlarged, other haplotypes would be found, but probably at low or intermediate frequencies, as we did observed in this work. In the study of Kim et al. (2006), the 3 haplotypes were Equatorial Guinea Mozambique Uganda Portugal Native America Northern Europe Pakistan Russia Cameroon China Hungary Japan TG CA CG Fig. 14 – Network of TAS1R1 haplotypes. FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 81 exclusively detected in the sample from Cameroon, which was indeed the largest sample they screened, comprehending 20 Cameroonians. Among them, the CG haplotype represented 75% of the sampled chromosomes, constituting the lowest frequency out of the populations now available, while the combined frequency of the haplotypes, CA and TG, summed up to 25%. As a matter of fact, haplotype diversity in the Cameroonians was the highest reported to date, meaning that for TAS1R1 Africans do not present necessarily lower diversities than non-Africans, as our data could indicate. Pairwise FST distances between populations from our study and that of Kim et al. (2006) were calculated and are shown in table 17. Table 17 – FST values correspondent to TAS1R1 haplotypes. Mz: Mozambique; Ca: Cameroonian; EG: Equatorial Guinea; UG: Uganda; Ch: Chinese; NA: Native American; Hu: Hungarian; Pa: Pakistani; Ja: Japanese; Ru: Russian; PT: Portugal; NE: Northern European. MZ Ca EG UG Ch NA Hu Pa Ja Ru PT NE MZ * Ca 0.2252 * EG 0.0465 0.1280 * UG 0.0367 0.1452 0.0241 * Ch 0.0071 0.1647 0.0402 0.0340 * NA 0.0071 0.1647 0.0402 0.0340 0.0303 * Hu 0.0071 0.1647 0.0402 0.0340 0.0303 0.0303 * Pa 0.0000 0.1498 0.0332 0.0262 0.0303 0.0303 0.0303 * Ja 0.0071 0.1647 0.0402 0.0340 0.0303 0.0303 0.0303 0.0303 * Ru 0.0071 0.1647 0.0402 0.0340 0.0303 0.0303 0.0303 0.0303 0.0303 * PT 0.0889 0.0514 0.0374 0.0459 0.0753 0.0753 0.0671 0.0671 0.0753 0.0753 * NE 0.0071 0.1647 0.0402 0.0340 0.0303 0.0303 0.0303 0.0303 0.0303 0.0303 0.0753 * The significant values after Bonferroni’s correction are in blue. Obviously the distances between the populations from the work of Kim et al. (2006), where only the CG haplotype was detected, presented very similar values among them, but these results need to be cautiously interpreted given the limitation of the small samples size. Despite that, the remaining FST values were in general very low, and accordingly non-significant, excepting those referring to the distances involving the Cameroonians. The sample from Cameroon exhibited the greatest level of differentiation for TAS1R1, showing significant distances with almost all populations when the conventional P=0.05 was considered. When the correction for multiple tests FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 82 was applied, distances with Mozambique and Equatorial Guinea remained significant. The population from which the Cameroonians differed less was Portugal, with the low FST value of 0.0514 translating well the similarity of the distribution of the TAS1R1 haplotypes in the two populations. Kim et al. (2006) did not give details on the samples analysed, but their region of origin. Nevertheless, diversity at TAS1R1 across populations is very narrow and does not seem to be correlated with diet/lifestyle. At least our sample of pastoralist from Uganda is not statistically different from the agriculturalists from Mozambique and Equatorial Guinea. Geography also does not represent a factor appearing to influence the general distribution at the loci. When we conducted AMOVA assuming groups according to geography (continent of origin) no significant variation among groups was detected, which was predictable given the absence of sharp dissimilarities across populations. In the future, more comprehensive studies are needed to obtain a better scenario of the distribution of the TAS1R1 haplotypes at a worldwide level. However, its variation seems to be relatively uniform across human populations, indicating that TAS1R1 does not contributes much to differences in umami taste sensitivity among them. This uniformity in diverse populations, from distant geographical origins and different lifestyles, raises the questions of the factors that underlie such distribution pattern. Previous evolutionary analyses support that several genes belonging to the TAS1R family, including TAS1R1, has been under positive selection (Kim et al., 2006). Yet, if selection has somehow operated in this gene, it must not have been driven by diet or geographical related pressures. FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 83 TAS1R3 With respect to TAS1R3, we have studied 3 non-synonymous variations for which there are indications that they might partially account for the inter-individual variability in umami taste perception: G13A, G740A and C2269T (Chen et al., 2009; Raliou et al., 2009; Shigemura et al., 2009b). For this locus, we also inferred the haplotypes defined by the 3 tested SNPs. From the 3 selected SNPs, C2269T is the unique for which functional evidence was gathered revealing that the T allele is associated with diminished sensitivity to substances eliciting the umami taste (Raliou et al., 2009, Chen et al., 2009; Shigemura et al., 2009b). For this reason, we have assumed that haplotypes harbouring C at this SNP were high sensitivity haplotypes while those carrying the T allele were low sensitivity haplotypes. Table 18 – TAS1R3 umami haplotype frequencies and diversities. Sample Haplotype Uganda (n=47) Mozambique (n=34) Equatorial Guinea (n=76) Portugal (n=49) GAC 0.1170±0.0312 0.2794±0.0558 0.1974±0.0329 0.0102±0.0099 GGC 0.8724±0.0320 0.7059±0.0577 0.7829±0.0341 0.9286±0.0252 GGT 0.0106±0.0097 - - 0.0612±0.0238 GAT - 0.0147±0.0150 0.0197±0.0115 - Haplotype Diversity 0.2276±0.0527 0.4298±0.0499 0.3500±0.0405 0.1353±0.0456 The haplotype frequencies estimated for TAS1R3 in different populations are presented in table 18. Our screening resulted in the detection of four haplotypes, but not simultaneously present in all populations. The low sensitivity haplotype, AAT, was not observed. In all populations the clearly predominant haplotype was GGC, the haplotype associated with increased sensitivity, reaching the very high value of 92.9% in Portugal. This haplotype was also very well represented in Africans, but only ranging from 70.6% to 87.2%. In all African populations the second most common haplotype was GAC, supposedly also conferring increased umami taste sensibility, always occurring with frequencies up to 11%. Two other less-taster haplotypes were detected, although much more rarely observed: GGT, present in Uganda and Portugal, and GAT present in Mozambique and Equatorial Guinea, although accounting for less than 2% of chromosomes in both populations. As commonly observed for the majority of loci, the 3 African populations from Uganda, Mozambique and Equatorial Guinea all revealed higher haplotype diversities at TAS1R3 than the unique non-African population investigated in this work, the Portuguese sample. FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 84 Comparing our results with those obtained by Kim et al. (2006), who carried out the more thorough study encompassing diverse worldwide populations, either for TAS1R3 or, as mentioned in the previous point, for TAS1R1, good agreement exists between data from the 2 studies. Also, as before referred, a major drawback of the study of Kim et al. (2006) was the very small sizes of the population samples analysed. Even so, similarly to the findings of this work, the authors observed that GGC was overwhelmingly present in their set of 8 populations, which included samples from Native Americans, Africans and Eurasians, being even the unique haplotype detected in 6 out the 8 populations, likely because they were quite undersized (from 8 to 20 individuals). Besides GGC, Kim et al. (2006) only detected other haplotypes in the Japanese and Cameroonians. Among the Japanese, GAC, which was the second most frequent haplotype detected in our study, occurred at 10% frequency; among the Cameroonians the additional haplotypes were GGT (25%) – also detected in this study, and AGC (2.5%) – which seems to be a rare haplotype not having been observed in the populations here examined. A network was constructed with all TAS1R3 haplotypes up to now detected, being shown in figure 15. Equatorial Guinea Mozambique Uganda Portugal Native America Northern Europe Pakistan Russia Cameroon China Hungary Japan AGC GAC GAT GGT GGC High Sensibility Low Sensibility Fig. 15 – Network with TAS1R3 haplotypes influencing the umami taste. FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 85 Overall, haplotypes conferring high sensibility to umami clearly prevail over those associated with decreased high sensibility. The presence of a reticulation in the network suggests that intragenic recombination likely accounted for the generation of the GAT haplotype combination. Concerning the distribution of the combined frequency of high (GGC+GAC+AGC) and low sensibility haplotypes (GGT+GAT), in non-African populations low sensibility haplotypes were only found in the Portuguese studied in this work, among whom GGT was present at 6.1% frequency. Within African populations, no major differences in the distribution of high and low sensibility haplotypes were observed, excepting the findings for the Cameroonian sample. High sensibility haplotypes represented 98% of chromosomes in Equatorial Guinea, 98.5% in Mozambique and attained the highest value in Uganda, 99%. Yet, in Cameroon their frequency was the lowest up to now register in any human population, 75%, although the value must be carefully considered, due to the small size of this sample (20 individuals). So, in the African context, the genetic variation here assessed at TAS1R3, which is thought to influence umami taste perception, does not appears to be correlated with lifestyle or even geographical location of populations. This conclusion can also be drawn from the analysis of pairwise FST genetics distances between populations, comprehending samples now studied plus those reported by Kim et al. (2006), which are presented in table 19. Table 19 – FST values correspondent to TAS1R3 haplotypes. Mz: Mozambique; EG: Ca: Cameroonian; EG: Equatorial Guinea; UG: Uganda; Ch: Chinese; NA: Native American; Hu: Hungarian; Pa: Pakistani; Ja: Japanese; Ru: Russian; PT: Portugal; NE: Northern European. MZ Ca EG UG Ch NA Hu Pa Ja Ru PT NE MZ * Ca 0.1560 * EG 0.0347 0.1429 * UG 0.0978 0.1558 0.0443 * Ch 0.2046 0.2000 0.1282 0.0755 * NA 0.2046 0.2000 0.1282 0.0755 0.0285 * Hu 0.2046 0.2000 0.1282 0.0755 0.0285 0.0285 * Pa 0.1924 0.1839 0.1204 0.0671 0.0285 0.0285 0.0285 * Ja 0.0857 0.1296 0.0356 0.0000 0.0811 0.0811 0.0811 0.0653 * Ru 0.2046 0.2000 0.1282 0.0755 0.0285 0.0285 0.0285 0.0285 0.0811 * PT 0.2182 0.1674 0.1236 0.0642 0.0379 0.0379 0.0379 0.0379 0.0303 0.0379 * NE 0.2046 0.2000 0.1282 0.0755 0.0285 0.0285 0.0285 0.0285 0.0285 0.0285 0.0379 * The significant values after Bonferroni’s correction are in blue. FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 86 The stronger genetic differentiations were always registered in pairs involving an African and a non-African population. After applying the correction for multiple tests, few results maintained the significance, among which were the distances between Portugal and Mozambique, Cameroon and Equatorial Guinea. However, these significant genetic distances ranged considerably, from 21.8% (Portugal-Mozambique) to its half, 12.4% (Portugal – Equatorial Guinea). Within Africans, significant differentiations were only observed between pairs involving the Cameroonian sample, illustrating the singular genetic profile of this population (average genetic distance with other African populations of 15.2%). Concerning AMOVA, the most relevant results are presented in table 20. Despite the several criteria used for grouping populations, proportions of variation among groups were only significant when the continent of origin was considered or when Africans were confronted with non-Africans. Using the latter criterion, the percentage of variation among groups was higher, significantly explaining 9.03% of the variation observed for TAS1R3. Table 20 – AMOVA results correspondent to groups formed with 12 populations’ data from different regions and ethnics. Source of Variation Percentage of variation P-value Geography (by continent) Among groups 6.59 0.0234± 0.0013 Among populations within groups 4.60 0.0002± 0.0002 Within populations 88.82 0.0000±0.0000 Africa vs. Non-Africa Among groups 9.03 0.0018±0.0004 Among populations within groups 3.66 0.0003±0.0002 Within populations 87.31 0.0000±0.0000 According to a recent study, umami taste perception seems to be linked with obesity in humans (Donaldson et al., 2009). Being so, it would be important to obtain a better understanding of the genetic factors underlying umami taste sensibility, as well as of the selective forces, if they really were exerted, contributing to the current patterns of genetic variations implied in the ability to sense the umami taste. This characteristic seems, therefore, to be an important piece to the health-care debate. FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 87 1.3. Sweet taste Two SNPs were selected in the promoter of TAS1R3, a gene encoding for a subunit that together with the subunit coded by TAS1R2 also forms a human sweet receptor heterodimer. The two SNPs were rs307355 (-C1572T) and rs35744813 (- C1266T), and they were chosen because a previous study provided evidence of being strongly correlated with the ability of people to correctly sort ascending concentrations of sucrose (Fushan et al., 2009). Both are C/T variations, and individuals carrying the T alleles were proven to display reduced sensitivity to sucrose compared to those who carry C alleles at the two nucleotide positions (Fushan et al., 2009). For simplicity, hereafter, the CC haplotype will be referred to as the sucrose taster, the TT as the nontaster, whereas other arrangements will be considered intermediate haplotypes. In table 21 are presented the haplotype frequencies estimated in the surveyed populations. Table 21 – TAS1R3 haplotype frequencies and diversities obtained for sweet taste. Sample Haplotype Uganda (n=48) Angola (n=17) Mozambique (n=34) Equatorial Guinea (n=76) Portugal (n=49) CC 0.2708±0.0472 0.0882±0.0492 0.3088±0.0558 0.3668±0.0407 0.7959±0.0403 CT 0.1667±0.0390 0.1471±0.0609 0.2059±0.0493 0.1661±0.0306 0.0510±0.0220 TC - - - 0.0148±0.0405 - TT 0.5625±0.0517 0.7647±0.0730 0.4853±0.0600 0.4523±0.0108 0.1531±0.0356 Haplotype Diversity 0.5885±0.0342 0.3999±0.0711 0.6361±0.0294 0.6352±0.0181 0.3440±0.0547 A remarkable difference was observed between Portugal and any of the African populations: whereas the taster CC haplotype clearly predominates in Portugal (79.6%), the most frequent one in Africans was always the non-taster TT (varying from 45.2% to 76.5%). Besides these two haplotypes, the intermediate CT was as well shared by all populations, occurring at low frequency in Portugal (5.1%) while reaching moderate values in Africans (14.7%-20.6%). In Equatorial Guinea, the rare intermediate TC haplotype was also detected at 1.5% frequency. These data are entirely consistent with those reported by Fushan et al. (2009), who performed a worldwide survey and similarly detected strong discrepancy in the frequencies of the CC and TT haplotypes between Europeans and Africans. Fushan et al. (2009) further studied an Asian sample, which showed much tighter affinities with the European sample than with the African one. CT haplotype was not found in Asians and its frequency was very low (0.5%) in the European individuals in comparison to the FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 88 Portuguese population, where this intermediate haplotype was much higher frequency (5.1%). Figure 16 shows the network of haplotypes and their frequencies in the populations studied in this work and in those screened by Fushan et al. (2009). The network shows up that at a broad geographical level there are two major haplotypes, which interestingly are the opposite CC (taster) and TT (non-taster). Although both are distributed across all geographical regions, CC is clearly overrepresented in Europe and Asia, while TT is much more frequent in Africa. The intermediate CT haplotype is rarely found out of Africa, occurring mainly in African populations, usually at moderate frequencies. The other intermediate haplotype, TC, here detected for the first time, was uniquely observed in Equatorial Guinea. Probably this haplotype combination has arisen by intragenic recombination between chromosomes harbouring the CC and TT haplotypes. Fig. 16 – Network with TAS1R3 haplotypes correspondent to sweet taste. The FST distances between pair of populations studied in this work and in that of Fushan et al. (2009) are presented in table 22. All distances between pairs involving an African and a non-African population were highly significant, even under the correction for multiple tests. The range of genetic distances obtained, 19%-72% (average value 39%) reflects well the strong differentiation between African and non-African Equatorial Guinea Mozambique Uganda Angola Portugal Africa Europe Asia TT CC CT TC FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 95 across the world suggests that some selective force, not dependent on geography, has acted maintaining at very high frequencies a haplotype associated to intermediate sensibility to umami perception (table 24). By the contrary, any possible selective factor affecting the sweet-related SNPs at TAS1R3, was likely related with variable(s) dependent on geography, determining the strong differentiation nowadays observed among populations from different continents, and especially among African and nonAfrican regions (figure 17). V. CONCLUSIONS FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 99 The present work represented a contribution to enlarge the knowledge about the genetic diversity in Africa that influences taste perception and its relationship with lifestyle, and associated diets, of populations. The samples analysed included representatives of two modes of subsistence: a sample of pastoralists from Uganda and three samples of agriculturalists from Angola, Mozambique and Equatorial Guinea. A population sample from Portugal was also studied to act as a control of non-African origin, in order to facilitate a more clear interpretation of the data. Till now, there is only one work published by Campbell and co-workers (2012), focusing the analysis of the correlation between different lifestyles practised in Africa and a taste related gene, which was TAS2R38, a gene that influences sensitivity to bitter compounds. In this study, besides TAS2R38, three additional genes were examined in order to obtain a better picture of the genetic architecture underlying taste perception across African populations, as well as to obtain insights on how typical diets in populations were related with their genetic variability in taste perception. Different SNPs previously implied in the phenotypic variability in umami, sweet and bitter taste perception were analysed, located in the following four genes: TAS1R1, TAS1R3, TAS2R16 and TAS2R38. For their genotyping two PCR-multiplexes systems were successfully optimized. One important achievement of this work was the characterization of five populations not studied before for the selected genetic variations. The data obtained constitutes an enrichment of the raw information essential to develop further studies, enrichment that is especially important regarding African populations, since they are clearly understudied up to date. With the exception of TAS1R1, for the remaining tested loci African populations had higher genetic diversities compared to other populations. This was in conformity with previous works performed at worldwide scale, which clearly demonstrated that Africa is the continent harbouring the greatest level of genetic diversity (Campbell and Tishkoff, 2008). At a worldwide level, it was only possible to analyse the influence of the mode of subsistence and genetic diversity for the two bitter genes here studied: TAS2R16 and TAS2R38. However, for both genes we did not found any significant association between lifestyle and the corresponding patterns of worldwide allele distribution. For TAS2R16, a marginally significant association was detected when focusing the analysis in African populations, but yet lifestyle accounted no more than 3% to explain the variation within Africa. FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 100 A factor that highly influences the distribution patterns of the majority of the tested genetic variations is geography. Indeed, excluding the SNPs at TAS1R1, for which high homogeneity was observed across populations from different world regions, concerning the remaining variations the continent of origin was found to be correlated with the level of differentiation between populations. However, some hints were also obtained indicating that geography and history of populations do not appear enough to explain the pattern of diversity at some of the variations. The worldwide distributions of variations at TAS2R38, TAS1R1 and TAS1R3 raise the question of whether selection has not contributed to model diversity at those loci, although the nature of the hypothetical selective pressures remains completely obscure. The utility of these polymorphisms, in cases of DNA phenotyping, must be more deeply explored in the future. So, many further studies are needed to continue the line of investigation initiated with this work. Future Directions In order to reach another level of knowledge and information about the influence of population’ lifestyles and genetic diversity at taste related variations, samples from African hunter-gatherers should be genotyped with the two multiplexes optimized in this work. It also would be important to extend the genetic characterizations to populations from other continents that still maintaining distinct modes of subsistence. Another study that in the future needs to be importantly performed, is to deeply investigate the relationship between genetic variation and phenotypic variability for the different categories of taste perception, a study that until now has been almost exclusively performed in European populations or in those of European descent. 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Supplementary data for the eleven loci studied. Table S1 – Locus by locus Heterozygosity and correspondent P-values for all populations studied. P=0.0045 (after the Bonferroni correction for multiple tests) Table S2 – Genotypic data for all populations studied. I. Angola TAS1R1 TAS1R3 TAS2R16 TAS2R38 C329T G1114A G13A G740A C2269T -C1572T -C1266T G516T C145G C785T G886A A1 n/a n/a n/a G n/a n/a n/a n/a G n/a n/a n/a n/a n/a G n/a n/a n/a n/a C n/a n/a A2 C n/a n/a G C n/a n/a n/a C n/a n/a C n/a n/a G C n/a n/a n/a C n/a n/a A3 C G G n/a C T T T G T G C G G n/a C T T T C C A A4 n/a n/a n/a G C n/a n/a n/a G n/a n/a n/a n/a n/a G C n/a n/a n/a G n/a n/a A5 C n/a G G C T T T C T G C n/a G A C T T G C T A A6 C n/a n/a n/a C T T T C n/a G C n/a n/a n/a C T T G C n/a G A7 C n/a n/a A C n/a n/a T G n/a n/a C n/a n/a A C n/a n/a T G n/a n/a A8 n/a n/a G G C T T T G T A n/a n/a G A C T T G G T A A9 C n/a n/a G C T T T C T G C n/a n/a G C T T G C C A A10 C n/a n/a A C T T T G C A C n/a n/a A C C T G G C A A11 C n/a G G C T T T G C G C n/a G G C T T T C C G A12 C n/a n/a A n/a n/a n/a G G C A C n/a n/a A n/a n/a n/a G C C A A13 n/a n/a G G C T T n/a G T A n/a n/a G G C T T n/a G T A A14 C n/a n/a n/a n/a n/a n/a T n/a C G C n/a n/a n/a n/a n/a n/a T n/a C A A15 C n/a G G C T T n/a G n/a n/a C n/a G A C C T n/a C n/a n/a A16 C n/a n/a G C T T T G C G C n/a n/a A C T T G C C A Uganda Angola Mozambique Equatorial Guinea Portugal Locus Ho He P-value Ho He P-value Ho He P-value Ho He P-value Ho He P-value C329T monomorphic locus monomorphic locus monomorphic locus 0.0395 0.0390 1.0000±0.0000 0.0408 0.0404 1.0000±0.0000 G1114A 0.1042 0.0998 1.0000±0.0000 monomorphic locus 0.0303 0.0303 1.0000±0.0000 0.1184 0.1122 1.0000±0.0000 0.2245 0.2014 1.0000±0.0000 G13A 0.0204 0.0204 1.0000±0.0000 monomorphic locus monomorphic locus monomorphic locus monomorphic locus G740A 0.1429 0.2014 0.0920±0.0003 0.3182 0.4598 0.1781±0.0004 0.4118 0.4214 1.0000±0.0000 0.3290 0.3422 0.7410±0.0004 0.0204 0.0204 1.0000±0.0000 C2269T 0.0204 0.0204 1.0000±0.0000 monomorphic locus 0.0294 0.0865 0.0147±0.0001 0.0395 0.0390 1.0000±0.0000 0.1225 0.1161 1.0000±0.0000 -C1572T 0.3674 0.4949 0.0837±0.0003 0.2353 0.3708 0.1769±0.0004 0.4412 0.5070 0.5044±0.0005 0.4079 0.5011 0.1120±0.0003 0.2245 0.2619 0.2932±0.0005 -C1266T 0.2857 0.3939 0.0692±0.0003 0.0588 0.1658 0.0910±0.0003 0.4412 0.4333 1.0000±0.0000 0.3158 0.4751 0.0066±0.0001 0.2449 0.3282 0.0889±0.0003 G516T 0.2500 0.2518 1.0000±0.0000 0.4286 0.4936 0.6596±0.0005 0.3438 0.4241 0.3950±0.0005 0.3158 0.3904 0.1346±0.0003 monomorphic locus C145G 0.6250 0.5018 0.1418±0.0004 0.3478 0.5101 0.2073±0.0004 0.5294 0.5057 1.0000±0.0000 0.5658 0.5025 0.3571±0.0005 0.5306 0.5033 0.7767±0.0004 C785T 0.4565 0.4193 0.7241±0.0005 0.2000 0.4308 0.0263±0.0002 0.5484 0.4321 0.2077±0.0004 0.4079 0.4196 0.7898±0.0004 0.5306 0.4949 0.7706±0.0004 G886A 0.6458 0.5156 0.0825±0.0002 0.4286 0.5122 0.6628±0.0005 0.5938 0.5035 0.4740±0.0005 0.5526 0.5019 0.4887±0.0005 0.5306 0.4999 0.7747±0.0004 FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 114 A17 C n/a n/a n/a n/a n/a n/a G n/a T A C n/a n/a n/a n/a n/a n/a G n/a T A A18 C n/a n/a G n/a n/a n/a G C C G C n/a n/a A n/a n/a n/a G C C G A19 C n/a n/a G C n/a n/a T n/a C G C n/a n/a A C n/a n/a T n/a C A A20 n/a n/a n/a G C C C G G C G n/a n/a n/a G C C C G C C A A21 C n/a G G C T T T G T A C n/a G G C C C G G C A A22 C n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a C n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a A23 C n/a n/a A n/a T T T C C G C n/a n/a A n/a T T T C C G A24 C n/a G G C T T T G C G C n/a G G C T T T G C A A25 C n/a n/a G C n/a n/a T C C G C n/a n/a A C n/a n/a T C C G A26 C n/a n/a G C C T T C C G C n/a n/a G C C T G C C G A27 C G G G C T T T G T G C G G G C C T G C C A II. Mozambique TAS1R1 TAS1R3 TAS2R16 TAS2R38 C329T G1114A G13A G740A C2269T -C1572T -C1266T G516T C145G C785T G886A M1 C G G G C T T T G T G C G G A C C C T C C A M2 C G G G C T T T C C G C G G A C T T T C C G M3 C G G G C C C G G C G C G G G C C C G C C A M4 C G G G T T T T G C G C G G A C T T G C C A M5 C G G G C C C T G T A C G G G C C C T G C A M6 C G G G C C T T G T G C G G G C C C G C C A M7 C G G G C T T T C T G C G G G C C T G C C G M8 C G G G C T T T G T G C G G A C T T T C C A M9 C G G A C T T T G T A C G G A C T T G G C A M10 C G G G C T T G C T G C G G A C T T G C C G M11 C G G A C T T T G C G C G G A C T T G C C A M12 C G G G C T T T G T G C G G A C C T T C C A M13 C G G G C T T T G T A C G G A C C C T G T A M14 C G G A C T T T G C G C G G A C T T G G C A M15 C G G G C C C T G T G C G G A C C C T C C A M16 C G G G C T T T C C G C A G G C C C G C C G M17 n/a G G G C T T n/a C n/a n/a n/a G G A C C C n/a C n/a n/a M18 C G G G C T T G G T G C G G G C C C G C C A M19 C G G G C C T T G n/a A C G G G C C C T G n/a A M20 C G G G C T T T G T G C G G A C T T G C C A M21 C G G G C T T T G C G C G G A C C T T C C A M22 C G G G C T T T G T G C G G G C T T G C C A M23 C G G G C T T T C C G C G G A C C C T C C G M24 C G G G C T T T G C G C G G G C C C G C C A M25 C G G G C C T T C T G C G G G C C C T C C G FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 115 M26 C G G G C C T T C T G C G G G C C C T C C G M27 C G G G C C T T G C G C G G G C C T T C C A M28 C G G G C C T T G T G C G G G C C C T C C A M29 C G G G C T T T G C A C G G A C C T T G C A M30 C G G G C T T T G C G C G G G C C T T C C A M31 C G G G G T T T C C G C G G G G C T T C C G M32 C G G G C C T T G T G C G G G C C C G C C A M33 C G G G C T T G G T G C G G A C C C G C C A M34 n/a n/a G G C T T n/a G n/a n/a n/a n/a G G C C C n/a G n/a n/a III. Equatorial Guinea TAS1R1 TAS1R3 TAS2R16 TAS2R38 C329T G1114A G13A G740A C2269T -C1572T -C1266T G516T C145G C785T G886A EG1 C G G G C C C T G C G C G G G C T T T C T A EG2 C G G G C C C T G C G C G G G C T T T C T A EG3 C G G G C C C T G C G C A G G C T T T C T A EG4 C G G G C C T T G C G C G G A C T T T C C A EG5 C G G G C C C G C C G C G G G C C C T C C G EG6 C G G G C C T T C C G C A G G C T T T C C G EG7 C G G G C C C G G C G C G G G C T T T C C A EG8 C G G G C C T T G C G C G G G C C T T C C A EG9 C G G A C T T G G C G C G G A C T T G C C A EG10 C G G G C C C G G C A C G G G C C C G G T A EG11 C G G G C C C T C C G C G G A C T C T C C G EG12 C G G G C C C T G C G C G G G C C T T C T A EG13 C G G G C T T G G C G C G G A C T T G C T A EG14 C G G G C C C G G C A C G G A C T T T G C A EG15 C G G G C T T G C C G C G G A C T T G C C G EG16 C G G G C T T T G C G C G G A C T T T C C A EG17 C G G G C C C T G T A C G G G C C C T G T A EG18 C G G G C C C T G C G C G G G C C T T C C A EG19 C G G G C T T T C C G C G G G C T T T C C G EG20 C G G G C C C T G C G C A G G C C C T C T A EG21 C G G G C C T T G C G C G G G C C T T C C A EG22 C G G G C C C T G C G C G G G C C C T C T A EG23 C G G G C C T T G C G C G G A C T T T C C A EG24 C G G G C C C T C C G T G G G C C C T C C G EG25 C G G G C C T G G C G C A G G C T T T C T A EG26 C G G G C T T T G C G C A G A C T T T C C A EG27 C G G G C C T T G C G C G G G C C T T C C A FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 116 EG28 C G G G C T T T G C G C A G A C T T T C T A EG29 C G G G C C T G G C G C G G G C T T T C T A EG30 C G G G C T T T G C G C G G A C T T T C C A EG31 C G G G C C C G G C A C G G G C T T T G T A EG32 C G G G C C T G G C G C G G G C T T T C T A EG33 C G G G C C C G G C A C G G A C T T T G T A EG34 C G G G C T T G G C G C G G A C T T T C C A EG35 C G G G C C C T G C G C G G A C T T T C C A EG36 C G G G C C C G G C G C G G G C T T T C T A EG37 C G G G C T T G G C A C G G A C T T T G T A EG38 C G G G C C C T G C G T G G G C C C T C T A EG39 C G G G C C C G C C G C G G G C C C T C C G EG40 C G G G C T T T G T A C G G A T T T T G T A EG41 C G G A C T T G G C G C G G A C T T T C T A EG42 C G G G C C T T C C G C G G G C T T T C C G EG43 C G G G C C C T C C G C G G G C T T T C C G EG44 C G G G C C C T G C G C G G G C C T T C C A EG45 C G G G C C C T G T A C G G G C C C T G T A EG46 C G G G C C C G C C G C A G G C C T T C C G EG47 C G G G C C T G G C G C G G A C T T G C C A EG48 C G G G C C C T G T A C G G G C T T T G T A EG49 C G G G C C C T C C G C G G A C T C T C C G EG50 C G G G C C C T G C G C G G G C T T T C C A EG51 C G G G C C C T G C G C G G G C T T T C T A EG52 C G G G C C C T C C G C G G G C C T T C C G EG53 C G G G C C T G G C G C G G A C T T T C C G EG54 C G G G C T T T G T A C G G A T T T T G T A EG55 C G G G C T T T G C G C G G G C T T T C T A EG56 C G G A C T T T G C G C G G A C T T T C T A EG57 C G G G C C C G G C G C G G G C C C T C C A EG58 C G G G C C C T G C G C G G G C C C T C T A EG59 C G G G C C T G G C G C G G G C T T T C T A EG60 C G G G C C T G G T A C G G G C T T G G T A EG61 C G G G C C C G G C G C G G A C T T G C T A EG62 C G G G C T T T G C A C G G A C T T T G T A EG63 C G G G C C T T C C G C G G G C T T T C C G EG64 C G G A C T T T G C G C A G A C T T T C C A EG65 C G G G C C C G G C G C G G G C C C T C T A EG66 C G G G C C C T G C G C G G G C C T T C T A EG67 C G G G C C C G G T A FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 117 C G G G C T T T G T A EG68 C G G G C T T G C C G C G G A C T T T C C G EG69 C G G G C T T T G C A C G G A T T T T G T A EG70 C G G G C T T G C C G C G G G C T T T C C G EG71 C G G G C C C G G C G C G G A C C T T C T A EG72 C G G G C C C G C C G C G G G C C C G C C G EG73 C G G G C C C T G C A T G G A C T T T G T A EG74 C G G G C C C G C C G C G G G C T T T C C G EG75 C G G G C C C G C C G C G G G C C C T C C G EG76 C G G G C C C T G C G C A G G C C C T C T A IV. Uganda TAS1R1 TAS1R3 TAS2R16 TAS2R38 C329T G1114A G13A G740A C2269T -C1572T -C1266T G516T C145G C785T G886A U1 C G G G C T T T G T G C G G G C C C T C C A U2 C G G G C T T T G n/a G C G G A C C T T C n/a A U3 C G G G C T T T G T A C G G G C T T T G C A U4 C G G G C T T T G C A C G G G C C C G G C A U5 C G G G C T T T G C G C G G G C C C T C T A U6 C G G G C T T T G T A C A G A C T T T G C A U7 C G G G C T T T G T G C G G G C C C T C C A U8 C G G G C T T T G T G C G G G C C T G C T A U9 C G G G C T T T G T G C G G G C C T T C C A U10 C G G G C T T T G T G C G G A C C C T C C A U11 C G G G C T T T G T A C G G G C T T T C C G U12 C G G G C C C T G T G C A G G C C C T C C A U13 n/a G G G C T T n/a G n/a n/a n/a G G G C T T n/a C n/a n/a U14 C G G G C C C T G C A C G G G C C C T G C A U15 C G G G C C T T G T A C G G G C C C T G C A U16 C G G G T T T T G T G C G G G C T T T G C A U17 C G G A C T T T G C G C G G A C T T T G C A U18 C G G G C T T T G C G C G G G C T T T C C A U19 C G G G C C C T G C G C G G G C C C T C C A U20 C G G G C T T T G T G C G G G C C T G C C A U21 C G G G C T T T G T G C G G G C C C T C C A U22 C G G G C T T T C C G C G G A C T T T C C G U23 C G A G C C T T C C G C G G G C C C G C C G U24 C G G G C T T T G C G C G G G C T T T C C A U25 C G G G C T T T C C G C G G G C C T T C C G U26 C G G G C C C T G n/a G C G G G C C C G C n/a A U27 C G G G C C T T G T G FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 118 C G G G C C T T C C A U28 C G G G C T T T G C G C G G G C C C G C C A U29 C G G G C T T T C C G C G G G C C T T C C G U30 C G G G C T T T G T A C G G A C T T G G T A U31 C G G A C T T T C C G C G G A C T T G C C G U32 C G G G C T T T G T T C G G G C T T T C C G U33 C G G G C C C T G T A C G G G C C C T G C A U34 C G G G C T T T G C G C G G A C C T T C C A U35 C G G G C C T T G T G C A G G C C C G C C A U36 C G G G C T T T G C G C G G G C T T T C C A U37 C G G G C C C T G T G C G G G C C C T C C A U38 C G G G C T T T G C G C A G G C T T T C C A U39 C G G G C T T T G C G C G G A C C T T C C A U40 C G G G C T T T G C G C G G G C T T T C C A U41 C G G G C T T T G C G C G G G C C C G C C A U42 C G G G C T T T G T G C G G G C C T T C C A U43 C G G G C C T T G T A C G G G C C C G G T A U44 C G G G C T T T C C G C G G G C T T G C C G U45 C G G G C C T T C C G C G G G C C C T C C G U46 C G G G C T T T G C G C A G G C C C T C C A U47 C G G G C T T T G T A C G G G C T T T G C A U48 C G G G C T T T G T G C G G G C T T T C C A U49 C n/a G G C T T G n/a C G C n/a G G C T T G n/a C G V. Portugal TAS1R1 TAS1R3 TAS2R16 TAS2R38 C329T G1114A G13A G740A C2269T -C1572T -C1266T G516T C145G C785T G886A P1 C G G G C C C T G C G C G G G C C C T C C G P2 C G G G C C C T C C G C G G G C C C T C C G P3 C G G G C C C T G T A C A G G C C C T G T A P4 C G G G C C C T G C G C G G G C T T T C T A P5 C G G G C C C T G C G C A G G C C C T C T A P6 C G G G C C C T G C G C A G G C C C T C C G P7 C G G G C C C T C C G C G G G C C C T C C G P8 C G G G C C C T G C G C A G G C C C T C T A P9 C G G G C C C T C C G C G G G C C C T C C G P10 C G G G C C C T C C G C G G G C C C T C C G P11 C G G G C C C T G C A C A G G T T T T G C A P12 C G G G C C C T G C G C G G G C C T T C T A P13 C G G G C C C T G C G C G G G C C C T C T A P14 C G G G C T T T G C G FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 119 C G G G C T T T C T A P15 C G G G C C C T G C G C G G G C C C T C T A P16 C G G G C C C T G C G C G G G C C T T C T A P17 C G G G C C C T G C G C G G G C C C T C T A P18 C G G G C C C T G C G C A G A C T T T C T A P19 C G G G C C C T G C G C A G G C C C T C T A P20 C G G G C C C T G C G C G G G C C C T C T A P21 C G G G C C C T G C G C G G G C C C T C T A P22 C G G G C C C T G T A C G G G C C C T G T A P23 C G G G C C C T C C G C G G G C C C T C C G P24 C G G G C C C T G C G T G G G T T T T C T A P25 C G G G C C C T G C G C G G G C C C T C T A P26 C G G G C C C T G C G C G G G C T T T C T A P27 C G G G C C T T C C G C A G G C T T T C C G P28 C G G G C T T T G C G C G G G C T T T C T A P29 C G G G C C C T G C G C G G G C C C T C T A P30 C G G G C C C T G T A C G G G C C C T G T A P31 C G G G C C C T G C G C A G G C C C T C T A P32 C G G G C C C T C C G T G G G C C C T C C G P33 C G G G C C C T C T A C G G G C C C T C T A P34 C G G G C C C T G T A C G G G C C C T G T A P35 C G G G C C C T G T A C G G G C C C T G T A P36 C G G G C C C T C C G C G G G C C C T C C G P37 C G G G C C C T C C G C G G G C C C T C C G P38 C G G G C C C T C C G C G G G C C C T C C G P39 C G G G C C C T G C G C A G G C C C T G T A P40 C G G G C C C T G C G C G G G T T T T C T A P41 C G G G C C C T G C G C G G G T T T T C T A P42 C G G G C C C T G C G C G G G C C C T C T A P43 C G G G C C C T G T A C G G G C C T T G T A P44 C G G G C C C T G C G C G G G C C C T G T A P45 C G G G C C C T G C G C G G G C T T T C T A P46 C G G G C C C T C C G C G G G T T T T C C G P47 C G G G C C C T C C G C G G G C C C T C C G P48 C G G G C C T T G C G C G G G T T T T C T A P49 C G G G C C C T G T A C A G G C C C T G T A FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 120 Annex 2. TAS2R16 supplementary material Table S3 – Data and information collected for G516T from TAS2R16. Paper/ Database Publication/ Sample reference Population Data about the samples Abbreviation n T516 frequency Lifestyle Soranzo et al. 2005 Curr Biol Tunisian Tunisia Tun 17 1 n/a Soranzo et al. 2005 Curr Biol Bantu South Africa BSA 28 0.79 agriculture Soranzo et al. 2005 Curr Biol Amhara Ethiopia AE 20 0.925 agriculture Soranzo et al. 2005 Curr Biol Cameroonian Cameroon Ca 12 0.67 agriculture HapMap PhaseII+III, August 10 CEU CEPH (Utah residents with ancestry from northern and western Europe) - 65 1.000 n/a HapMap PhaseII+III, August 10 CHB Han Chinese in Beijing, China - 45 1.000 n/a HapMap PhaseII+III, August 10 JPT Japanese in Tokyo, Japan - 44 1.000 n/a HapMap PhaseII+III, August 10 YRI Yoruba in Ibadan, Nigeria - 147 0.633 agriculture HapMap PhaseII+III, August 10 ASW African ancestry in Southwest USA - 54 0.676 n/a HapMap PhaseII+III, August 10 LWK Luhya in Webuye, Kenya - 109 0.807 agriculture HapMap PhaseII+III, August 10 MEX Mexican ancestry in Los Angeles, California - 58 0.966 n/a HapMap PhaseII+III, August 10 MKK Maasai in Kinyawa, Kenya - 156 0.772 pastoralist Hinrichs et al. 2006 Am J Hum Genet Biaka Pygmy Central African Republic BP 36 0.79 hunter-gatherer Hinrichs et al. 2006 Am J Hum Genet Mandenka Senegal MS 24 0.79 agriculture Hinrichs et al. 2006 Am J Hum Genet Mbuti Pygmies Democratic Republic of Congo MP 15 0.8 hunter-gatherer Hinrichs et al. 2006 Am J Hum Genet Bantu NE North-Eastern Africa BNE 12 0.83 agriculture Hinrichs et al. 2006 Am J Hum Genet San South - 7 0.86 hunter-gatherer Hinrichs et al. 2006 Am J Hum Genet Mozabite Algeria MA 30 0.9 pastoralist Hinrichs et al. 2006 Am J Hum Genet Palestinian Israel Plt 50 0.94 n/a Hinrichs et al. 2006 Am J Hum Genet Maya México - 25 0.96 n/a Hinrichs et al. 2006 Am J Hum Genet Bedouin North Africa Be 47 0.98 pastoralist Hinrichs et al. 2006 Am J Hum Genet Brahui Pakistan Br 24 0.98 n/a Hinrichs et al. 2006 Am J Hum Genet Adygei Russian Caucasus Ad 17 1 n/a Hinrichs et al. 2006 Am J Hum Genet Balochi Iran Ba 26 1 n/a Hinrichs et al. 2006 Am J Hum Genet Makrani Pakistan Mak 26 1 n/a Hinrichs et al. 2006 Am J Hum Genet Sindhi Pakistan Si 24 1 agriculture Hinrichs et al. 2006 Am J Hum Genet Burusho Pakistan Bu 25 1 n/a Hinrichs et al. 2006 Am J Hum Genet Hazara Pakistan Ha 22 1 pastoralist Hinrichs et al. 2006 Am J Hum Genet Kalash Pakistan Ka 25 1 agriculture Hinrichs et al. 2006 Am J Hum Genet Pathan Afghanistan Pa 25 1 n/a Hinrichs et al. 2006 Am J Hum Genet Basque Spain Bsq 30 1 n/a Hinrichs et al. 2006 Am J Hum Genet French France Fr 24 1 n/a Hinrichs et al. 2006 Am J Hum Genet Bergamo Italy Brg 27 1 n/a Hinrichs et al. 2006 Am J Hum Genet Sardinian Italy Sa 14 1 n/a Hinrichs et al. 2006 Am J Hum Genet Tuscan Italy Tus 8 1 n/a Hinrichs et al. 2006 Am J Hum Genet Cambodian Cambodia Cam 11 1 n/a FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 127 Annex 3. TAS2R38 supplementary material Table S7 – Data and information collected for TAS2R38 gene. Paper Publication Population Data about Sampling Abbreviation n PAV AAV AAI AVI PVI PAI Lifestyle Wooding et al. 2004 Am J Hum Genet African 9 sub-Saharan Africans from Coriell Human Variation panel HD12, 22 Cameroonians SSA 31 0.6129 0.0323 0.1774 0.1613 0.0161 0 n/a Wooding et al. 2004 Am J Hum Genet European (10 hungarians, 45 utah samples from Centre d'Etude du Polymorphisme Humain) NWE 55 0.4636 0.0455 0 0.4909 0 0 n/a Wooding et al. 2004 Am J Hum Genet North-American (10 southwest Native Americans) NA 10 0.95 0 0 0.05 0 0 n/a Wooding et al. 2010 Chem Senses Caucasian Texas Cc 50 0.49 0.06 0 0.49 0.04 0.02 n/a Campbell et al. 2012 Mol Biol Evol Afroasiatic Cameroon AA_WCA 73 0.506 0.007 0.22 0.267 0 0 Agriculture/ pastoralism Campbell et al. 2012 Mol Biol Evol Nilo-Saharan Cameroon NS_WCA 26 0.4031 0.0193 0.2123 0.3653 0 0 Agriculture Campbell et al. 2012 Mol Biol Evol NigerKordofanian Cameroon NK_WCA 62 0.387 0.008 0.307 0.298 0 0 Agriculture Campbell et al. 2012 Mol Biol Evol Fulani Cameroon - 48 0.553 0 0.145 0.308 0 0 Pastoralism Campbell et al. 2012 Mol Biol Evol Pygmy Cameroon - 62 0.54 0 0.25 0.21 0 0 Hunting and Gathering (HG) Campbell et al. 2012 Mol Biol Evol Afroasiatic Kenya AA_EA 132 0.492 0.038 0.152 0.318 0 0 Agriculture/ pastoralism/HG Campbell et al. 2012 Mol Biol Evol Nilo-Saharan Kenya NS_EA 130 0.568 0.023 0.1 0.309 0 0 Pastoralism (n=114) /HG (n=16) Campbell et al. 2012 Mol Biol Evol NigerKordofanian Kenya NK_EA 34 0.514 0.015 0.25 0.221 0 0 Agriculture Campbell et al. 2012 Mol Biol Evol Luo Kenya - 21 0.667 0 0.143 0.19 0 0 Pastoralism Campbell et al. 2012 Mol Biol Evol Hadza/Sandawe Tanzania (Khoisan) - 23 0.587 0 0.152 0.261 0 0 Hunting and Gathering Campbell et al. 2012 Mol Biol Evol Americas Mexico/Nahua - 13 0.846 0 0 0.154 0 0 n/a Campbell et al. 2012 Mol Biol Evol Middle East Palestinian Plt 20 0.5 0 0.05 0.45 0 0 n/a Campbell et al. 2012 Mol Biol Evol Pakistan Brahui Br 8 0.4375 0 0 0.5625 0 0 n/a Campbell et al. 2012 Mol Biol Evol East Asia Chinese/Japanese EAsian 71 0.627 0 0 0.373 0 0 n/a FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 128 Table S8 – AMOVA groups with significant percentages of variation among groups for TAS2R38. I. Region Group1 Group2 Group3 Group4 Group5 Group6 Afroasiatic WCA Uganda Angola Mozambique Nahua Native American Equatorial Guinea Khoisan Nilo-Saharan WCA Luo Niger-Kordofanian WCA Niger-Kordofanian EA Fulani Afroasiatic EA Pygmy NiloSaharan EA Group7 Group8 Group9 Group10 Group11 Portugal NW European Brahui Palestinian East Asian II. Continent Africa Europe Asia America Angola Pygmy Portugal Brahui Nahua Mozambique Khoisan NW European Palestinian Native American Equatorial Guinea Luo East Asian Uganda Niger-Kordofanian EA Afroasiatic WCA Afroasiatic EA Nilo-Saharan WCA Nilo-Saharan EA Niger-Kordofanian WCA African Fulani Table S9 – FST values obtained to TAS2R38 and used to construct the MDS plot presented in chapter IV (Results & Discussion). AN: Angola; MZ: Mozambique; EG: Equatorial Guinea; UG: Uganda; PT: Portugal. The other abbreviations are equal to the presented in Table S7. AN MZ EG UG AA_WCA NS_WCA NK_WCA Fulani Pygmy Khoisan Luo NK_EA AA_EA NS_EA SSA PT NWE Brahui Plt EAsian Nahua NA Cc AN * MZ -0.0179 * EG 0.0042 0.0101 * UG -0.0123 -0.0111 0.0018 * AA_WCA -0.0022 -0.0012 -0.0045 -0.0037 * NS_WCA -0.0015 0.0065 0.0026 -0.0062 0.0030 * NK_WCA 0.0008 -0.0032 0.0190 -0.0039 0.0101 -0.0033 * Fulani 0.0125 0.0207 -0.0068 0.0097 -0.0015 0.0099 0.0315 * Pygmy 0.0025 -0.0029 0.0034 0.0028 -0.0031 0.0216 0.0186 0.0070 * Khoisan 0.0089 0.0149 -0.0089 0.0091 -0.0054 0.0178 0.0343 -0.0137 -0.0029 * Luo 0.0347 0.0379 0.0133 0.0378 0.0154 0.0617 0.0709 0.0055 0.0079 -0.0125 * NK_EA 0.0586 0.0758 0.0209 0.0587 0.0340 0.0650 0.0955 0.0071 0.0417 -0.0021 -0.0013 * AA_EA 0.0047 0.0165 -0.0029 0.0046 0.0013 -0.0006 0.0217 -0.0029 0.0140 -0.0019 0.0246 0.0231 * NS_EA 0.0271 0.0414 0.0023 0.0264 0.0114 0.0245 0.0539 -0.0046 0.0222 -0.0078 0.0088 0.0001 0.0035 * SSA 0.0142 0.0178 0.0115 0.0238 0.0089 0.0500 0.0531 0.0088 -0.0002 -0.0081 -0.0145 0.0167 0.0217 0.0157 * PT 0.0488 0.0770 0.0235 0.0484 0.0400 0.0261 0.0728 0.0159 0.0663 0.0242 0.0591 0.0211 0.0138 0.0108 0.0658 * NWE 0.0836 0.1139 0.0525 0.0756 0.0720 0.0394 0.0943 0.0469 0.1077 0.0642 0.1126 0.0615 0.0363 0.0407 0.1178 -0.0009 * Brahui 0.0448 0.0716 0.0094 0.0417 0.0293 0.0180 0.0693 -0.0022 0.0574 0.0066 0.0444 -0.0054 0.0015 -0.0092 0.0561 -0.0320 -0.0228 * Plt 0.0475 0.0701 0.0174 0.0397 0.0336 0.0122 0.0585 0.0113 0.0628 0.0234 0.0678 0.0292 0.0089 0.0101 0.0735 -0.0136 -0.0103 -0.0393 * EAsian 0.0855 0.1105 0.0378 0.0738 0.0582 0.0477 0.0991 0.0270 0.0872 0.0376 0.0738 0.0257 0.0288 0.0197 0.0883 -0.0039 0.0031 -0.0358 -0.0120 * Nahua 0.1575 0.1670 0.1051 0.1524 0.1153 0.1908 0.1974 0.0907 0.1083 0.0730 0.0320 0.0333 0.1114 0.0744 0.0587 0.1311 0.1940 0.1448 0.1691 0.1383 * NA 0.2380 0.2398 0.1807 0.2248 0.1862 0.2828 0.2687 0.1732 0.1725 0.1619 0.1081 0.1279 0.1861 0.1513 0.1219 0.2279 0.2962 0.3179 0.2903 0.2406 0.0104 * Cc 0.0573 0.0912 0.0422 0.0602 0.0592 0.0295 0.0803 0.0372 0.0911 0.0506 0.0939 0.0510 0.0274 0.0327 0.0963 -0.0039 -0.0055 -0.0208 -0.0086 0.0060 0.1716 0.2664 * The significant values of genetic distances after Bonferroni’s correction are labelled in red. FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 129 Annex 4. Umami taste supplementary material Table S10 – Data and information collected for TAS1R1 gene. Paper Publication Population Abbreviation n CG CA TG Kim et al. 2006 Chem Senses Cameroonian Ca 20 0.75 0.2 0.05 Kim et al. 2006 Chem Senses Chinese Ch 10 1 0 0 Kim et al. 2006 Chem Senses Hungarian Hu 10 1 0 0 Kim et al. 2006 Chem Senses Japanese Ja 10 1 0 0 Kim et al. 2006 Chem Senses Native American NA 10 1 0 0 Kim et al. 2006 Chem Senses Northern European NE 10 1 0 0 Kim et al. 2006 Chem Senses Pakistani Pa 8 1 0 0 Kim et al. 2006 Chem Senses Russian Ru 10 1 0 0 Table S11 – Data and information collected for TAS1R3 gene. Paper Publication Population Abbreviation n GGC GGT GAC AGC Kim et al. 2006 Chem Senses Cameroonian Ca 20 0.725 0.25 0 0.025 Kim et al. 2006 Chem Senses Chinese Ch 10 1 0 0 0 Kim et al. 2006 Chem Senses Hungarian Hu 10 1 0 0 0 Kim et al. 2006 Chem Senses Japanese Ja 10 0.9 0 0.1 0 Kim et al. 2006 Chem Senses Native American NA 10 1 0 0 0 Kim et al. 2006 Chem Senses Northern European NE 10 1 0 0 0 Kim et al. 2006 Chem Senses Pakistani Pa 8 1 0 0 0 Kim et al. 2006 Chem Senses Russian Ru 10 1 0 0 0 Table S12 – AMOVA groups regarding the continent of origin for TAS1R1 and TAS1R3. Africa Europe Asia America Mozambique European Chinese Native American Uganda Hungarian Pakistani Cameroonian Portugal Japanese Equatorial Guinea Russian FCUP A genetic approach to the relationship between taste perception and lifestyle in Africa 130 Annex 5. Sweet taste supplementary material Table S13 – Data and information collected for TAS1R3 gene concerning sweet taste. Paper Publication Population n CC CT TT Fushan et al. 2009 Curr Biol African 15 0.4 0.23 0.37 Fushan et al. 2009 Curr Biol European 92 0.91 0.005 0.085 Fushan et al. 2009 Curr Biol Asian 37 0.78 0 0.22 Table S14 – AMOVA groups regarding the continent of origin. Africa Europe Asia Mozambique European Asian Angola Portugal Equatorial Guinea African