The genetic analysis of tolerance to infections : a review

METHODS ARTICLE published: 14 Decembe 2012 doi: 10.3389/ gene.2012.00262 The gene ic analysis o ole ance o in ec ions: a e iew An i Kause1*and Jø gen Ødegå d2 1Bio echnology and Food Resea ch, Biome ical Gene ics, MTT Ag i ood Resea ch Finland, Jokioinen, Finland 2No ima, Ås, No way Edi ed by: And ea B. Doeschl-Wilson, The Uni e si y o Edinbu gh, UK Re iewed by: Mai ead L. Be mingham, The Uni e si y o Edinbu gh, UK Sunday Pe e s, Co nell Uni e si y, USA *Co espondence: An i Kause, Bio echnology and Food Resea ch, Biome ical Gene ics, MTT Ag i ood Resea ch Finland, FI-31600, Jokioinen, Finland. e-mail: an i.kause@m . i Tole ance o in ec ions is de ined as he abili y o a hos o limi he impac o a gi en pa hogen bu den on hos pe o mance. Uncoupling esis ance and ole ance is a challenge, and he e is a need o be able o sepa a e hem using speci ic ai eco ding o s a is ical me hods. We p esen h ee s a is ical me hods ha can be used o in es iga e gene ics o ole ance- ela ed ai s. Fi s ly, using andom eg essions, ole ance can be analyzed as a eac ion no m slope in which hos pe o mance (y-axis) is eg essed agains an inc easing pa hogen bu den (x-axis). Gene ic a iance in ole ance slopes is he gene ic a iance o ole ance. Va ia ion in ole ance can induce geno ype e- anking and changes in gene ic and pheno ypic a ia ion in hos pe o mance along he pa hogen bu den ajec o y, con ibu ing o en i onmen -dependen gene ic esponses o selec ion. Such geno ype-by-en i onmen in e ac ions can be quan i ied by combining andom eg essions and co a iance unc ions. To apply andom eg essions, pa hogen bu den o indi iduals needs o be eco ded. Secondly, when pa hogen bu den is no eco ded, he cu e model o ime-un il-dea h da a allows sepa a ing wo ai s, suscep ibili y and endu ance. Suscep ibili y is whe he o no an indi idual was suscep ible o an in ec ion, whe eas endu ance deno es how long ime i ook un il he in ec ion killed a suscep ible animal (in luenced by ole ance). Thi dly, he no mal mix u e model can be used o classi y con inuously dis ibu ed hos pe o mance, such as g ow h a e, in o di e en sub-classes (e.g., non-in ec ed and in ec ed), which allows es ima ion o hos pe o mance educ ion speci ic o in ec ed indi iduals. Mo eo e , gene ics o hos pe o mance can be analyzed sepa a ely in heal hy and a ec ed animals, e en in he absence o pa hogen bu den and su i al da a. These me hods p o ide no el ools o inc ease ou unde s anding on he impac o pa asi es, pa hogens, and p oduc ion diseases on hos ai s. Keywo ds: cu e model, geno ype-by-en i onmen in e ac ion, mix u e model, quan i a i e gene ics, andom eg ession, esis ance, s a is ical me hods, ole ance INTRODUCTION Tole ance and esis ance a e wo di e en de ense mechanisms o de end agains pa hogens and pa asi es. Resis ance is he abil- i y o a hos o p e en pa hogen en y and o con ol pa hogen li ecycleinaway o educepa hogenbu denwi hinahos indi- idual. Tole ance o in ec ions, in u n, is de ined as he abili y o he hos o limi he impac o a gi en pa hogen bu den on hos heal h, pe o mance, and ul ima ely on hos i ness (Clunies- Ross, 1932; Pain e , 1958; Albe s e al., 1987; Simms and T iple , 1994; Simms, 2000)(Figu e 1). Being able o uncouple esis ance and ole ance is essen- ial o se e al easons. Fi s ly, hey ha e di e en impac on he a ms- ace co-e olu ion be ween he hos and he pa hogen (Mau icio e al., 1997; Raushe , 2001; Bishop and MacKenzie, 2003; Bes e al., 2008). Mo eo e , bo h in ani- mals and plan s, ole ance and esis ance a e weakly gene i- cally co ela ed, and hus hey a e gene ically di e en ai s (Leimu and Ko iche a, 2006; Ødegå d e al., 2011b; Kause e al., 2012). Finally, animal and plan b eede s should exploi bo h inc eased esis ance and ole ance o ensu e global ood secu i y. In addi ion o pa hogens, ole ance can be assessed agains abio ic ac o s such as empe a u e, hea y me als, o agains p oduc ion diseases causing damage o body issues (Ra agnolo and Misz al, 2000a,b; Scha e al., 2002; Bloemho e al., 2012; Kause e al., 2012). Na u ally, p oduc ion diseases, such as asci es, a e no s anda d disease ai s caused by a pa hogen o pa asi e in ec ion. Thus, he e is no co-e olu ion be ween a hos and a p oduc ion disease, and he p oduc ion disease does no e ol e in esponse o he e olu ion o he hos . Ne e heless, imp o ed esis ance and ole ance can be bo h used o educe he ha m ul e ec s o p oduc ion diseases on a med animals, mo i a ing hei ole ance analysis (Kause e al., 2012). F om he eon in his pape , pa hogen bu den is used as a gene al e m o e e o a pa hogen load o an indi idual, o ins ance, numbe o biomass o ec o- and endopa asi es, numbe o pa hogens in a blood sample, o se e i y o a p oduc ion disease. In plan s, pa hogen bu den may e e o he biomass o numbe o he bi o es, o pe cen age o lea a ea los o he bi o es. The objec i e o his pape is o p esen ecen s a is ical ad ances in he gene ic analysis o ole ance- ela ed ai s. Fi s ly, andom eg ession models ha e been applied o www. on ie sin.o g Decembe 2012 | Volume 3 | A icle 262 |1 Kause and Ødegå d Gene ic analysis o ole ance FIGURE 1 | Tole ance o in ec ions. Tole ance is he eac ion no m slope o hos pe o mance eg essed agains indi idual’s pa hogen bu den. The lines ep esen pe o mance o h ee geno ypes wi h a di e en deg ee o ole ance. ole ance analysis. They allow a sophis ica ed gene ic analysis o ai s de ined as unc ions as well as he quan i ica ion o geno ype-by-en i onmen in e ac ions (G ×E) induced by in ec- ions (Kause, 2011; Kause e al., 2012). Secondly, Ødegå d e al. (2011b,c) in oduced a cu e model o sepa a e “suscep ibili y” and “endu ance” om challenge es da a wi h ime-un il-dea h obse a ions (wi hou ha ing any knowledge abou in ec ion s a- us o he animals). The i s ai is compa able o esis ance, while endu ance may be in luenced by ole ance. “Suscep ibili y” can be de ined as whe he o no he animal is liable o die as a esul o an in ec ion (i.e., long- e m su i al, which is likely asso- cia ed wi h esis ance), while “endu ance” is de ined as how long ime i akes be o e a po en ial in ec ion kills he animal (which is likely associa ed wi h ole ance). Bo h endu ance and suscep i- bili y may show gene ic a ia ion, and may be iewed as di e en gene ic ac o s a ec ing su i al unde an in ec ion. Finally, no - mal mix u e models can be ex ended o in ol e esponses in hos pe o mance ai s (e.g., g ow h cu es) speci ic o heal hy and a ec ed indi iduals (Wang and Bodne , 2007; Madsen e al., 2008). RANDOM REGRESSION MODELS Tole ance is by de ini ion he change in hos pe o mance as a unc ion o pa hogen bu den (Simms, 2000), and hence, i is na u al o apply andom eg ession models o es ima e gene ic pa ame e s and b eeding alues o ole ance (Kause, 2011). Using andom eg essions, ole ance can be analyzed as a eac ion no m in which hos pe o mance (on y-axis) is eg essed agains pa hogen bu den o indi iduals (on x-axis) (Box 1). I is impo - an o no e ha pa hogen bu den is measu ed sepa a ely om each indi idual, and i is no a gene al en i onmen al cha ac e is- ic. The slope o such a eg ession is consis en wi h he de ini ion o ole ance (Figu e 1), and hence gene ic a iance in eg ession slopes is he gene ic a iance o ole ance (Kause, 2011). The in e cep o he ole ance eg ession is in e p e ed as he hos pe o mance in a pa hogen- ee en i onmen , and he gene ic co ela ion be ween he slope and he in e cep quan i ies he deg ee o which hos pe o mance unde no in ec ion is Box 1 | A andom eg ession model. An animal model andom eg ession model is o he o m: yi=b0+b1PBu den +b0i+b1iPBu deni+i, whe e yiis hos pe o mance o an indi idual ia i s pa hogen bu den PBu den,b0is he ixed popula ion mean in e cep , b1PBu den is he ixed popula ion mean ole ance slope, b0iis he andom gene ic e ec o in e cep o an indi idual i,b1iPBu deniis he andom gene ic e ec o ole ance slope o an indi idual i,and iis he andom e o e m. Bo h b0iand b1ia e modeled wi h a pedig ee, allowing he es ima ion o hei gene ic a iance. Co a iance unc ions. Gene ic a iance o hos pe o mance as a unc ion o pa hogen bu den can be calcula ed: as x’PBu denGxPBu den, whe e G=σ2 b0σb0b1 σb0b1σ2 b1,σ2 b0and σ2 b1a e gene ic a iances o in e cep and slope, espec i ely, and σb0b1 is co a iance be ween he wo e ms (Kolmodin and Bijma, 2004). The e m xPBu den isa ec o [1PBu den]in which PBu den e e s o a pa hogen bu den alue on he x-axis. A gene ic co ela- ion be ween he pe o mance o non-in ec ed (PBu den =0) and in ec ed indi iduals a a ce ain PBu den alue can be calcula ed as: G=x 0GxPBu den x 0Gx0×x PBu denGxPBu den , whe e Gis he gene ic (co) a iance ma ix o slope and in e cep , x0isa ec o o [10] , and xPBu den is as desc ibed ea lie (Calus e al., 2004). gene ically aded o wi h ole ance. Mo eo e , gene ic co ela- ions o he slope and in e cep wi h hi d-pa y ai s can be es ima ed by ex ending he andom eg ession model o mul i ai animal o si e model (Kause e al., 2012). In animals, pa hogen bu den is ypically a con inuously dis- ibu ed ai , especially when a popula ion is unde a na u al pa hogen in ec ion (S ea e al., 1995; Kuukka-An ila e al., 2010). E en in a challenge es in which all indi iduals a e exposed o he same ini ial pa hogen load, a ia ion among indi iduals in esis ance c ea es con inuous a ia ion in pa hogen bu den. Random eg ession models allow gene ic analysis o ole ance along a con inuous pa hogen bu den ajec o y. In animal b eed- ing, andom eg ession models ha e been commonly applied o he eac ion no m analysis o G ×E(Hende son, 1982; Meye and Hill, 1997; Calus e al., 2004; Schae e , 2004; Lillehamme e al., 2009). TOLERANCE-INDUCED VARIATION IN HOST PERFORMANCE Gene ic a ia ion in ole ance may induce G ×Einhos pe - o mance, leading o changes in gene ic a ia ion o hos pe - o mance along an inc easing pa hogen bu den ajec o y. Fo ins ance, in Figu e 1, gene ic a iance in hos pe o mance is ele a ed along inc eased pa hogen bu den due o di e ging ol- e ance eac ion no ms. In poul y, pigs, and aquacul u e species, b eeding nucleuses may be held in ec ion- ee due o biosecu i y easons, whe eas comme cial p oduc ion and/o collec ion o sib and p ogeny in o ma ion o b eeding alue es ima ion occu s a ield a ms wi h di e se diseases p esen . Such a design may induce G ×Edue o a ia ionin hele elo ole ance,which should be accoun ed o in b eeding alue e alua ions. In an in ec ion- ee en i onmen , indi idual a ia ion in hos pe o mance, e.g., in g ow h a e, is due o a ia ion in gene ic F on ie s in Gene ics | Li es ock Genomics Decembe 2012 | Volume 3 | A icle 262 |2 Kause and Ødegå d Gene ic analysis o ole ance po en ial o g ow h and unexplained en i onmen al a ia ion. Unde in ec ion, in u n, indi idual a ia ion in bo h esis ance and ole ance induce addi ional a ia ion in o hos pe o mance. Some indi iduals a e ully esis an o a e no exposed o an in ec ion, and hus hei g ow h is no in luenced by he in ec- ion. Some indi iduals a e in ec ed, and he deg ee o which hei g ow h a e is educed depends on hei pa hogen bu den and he le el o ole ance. G ow h o ully ole an indi iduals is no a ec ed, whe eas g ow h o e y sensi i e ones is g ea ly educed. Despi e he la ge numbe o s udies dealing wi h he changes induced by bio ic (e.g., die ) and abio ic ac o s in gen- e al (Ho mann and Me ilä, 1999; Kause and Mo in, 2001; Cha man ie and Ga an , 2005), he e has been only a limi ed ocus on in ec ion-induced changes in gene ic pa ame e s and he consequen en i onmen -speci ic gene ic esponses o selec ion ( an de Waaij e al., 2000). In ec ions a e indeed known o induce changes in he i abili y o hos pe o mance ai s (Cha man ie e al., 2004; Pakdel e al., 2005; Ze ehda an e al., 2006; Kause e al., 2007, 2012; Veh iläinen e al., 2008; Lewis e al., 2009). Ye , cu en ly we do no know how much o he pheno ypic a i- a ion in hos pe o mance is in ac c ea ed by in ec ions and he associa ed ole ance. A s udy by Kause e al. (2012)showed ha coe icien o pheno ypic a ia ion in b oile body weigh was ele a ed om 11.5% when bi ds we e heal hy, o 19.1% when bi ds we e se e ely a ec ed by asci es. Simila ly, coe icien o gene ic a ia ion was inc eased om 4.9% o 7.9%, imply- ing he changes in a iance can be ex ensi e (Figu e 2). I is hypo hesized ha in popula ions exposed o in ec ions, a la ge p opo ion o pheno ypic a iance in hos ai s is induced by in ec ions and he associa ed indi idual a ia ion in esis ance and ole ance. Random eg ession models combined wi h co a iance unc- ions (Ki kpa ick e al., 1990; Meye and Hill, 1997)p o ide means o quan i y he changes in pheno ypic and gene ic a i- ances in hos ai s along a con inuous pa hogen bu den a- jec o y (Kause, 2011; Kause e al., 2012). Gi en he gene ic (co) a iance es ima es o ole ance slope and in e cep es ima ed using andom eg essions, he changes in gene ic a iance in hos pe o mance can be calcula ed using o mulas (Box 1;Figu e 2). FIGURE 2 | Tole ance analysis using andom eg essions and co a iance unc ions illus a ed using da a on 7-week body weigh and hea a io o b oile s [ ep oduced om Kause e al. (2012); h p://c ea i e commons.o g/licenses/by/3.0/]. Hea a io, he a io o igh en icula weigh o o al hea weigh , is an indica o o asci es esis ance, he bi ds wi h highe han 27–30% hea a io ypically being asci ic (Wideman e al., 1998). Tole ance is he change in body weigh as a unc ion o inc easing asci es se e i y, measu ed as he hea h a io. (A) Popula ion a e age ole ance cu e wi h body weigh on y-axis and asci es se e i y on x-axis. (B) F equency dis ibu ion o es ima ed b eeding alues o ole ance slopes o indi iduals, showing sensi i e (s eep nega i e slope) and mo e ole an (weak nega i e slope) geno ypes. (C) Inc eased coe icien s o pheno ypic and gene ic a ia ion in body weigh as a unc ion o asci es se e i y, showing asci es molds ai a ia ion. (D) Gene ic co ela ion be ween heal hy bi ds and bi ds wi h di e en deg ee o asci es se e i y, showing asci es c ea es geno ype e- anking (Kause e al., 2012). www. on ie sin.o g Decembe 2012 | Volume 3 | A icle 262 |3 Kause and Ødegå d Gene ic analysis o ole ance The same logic can be applied o he ma e nal and en i onmen al componen s o (co) a iance. TOLERANCE-INDUCED GENOTYPE RE-RANKING IN HOST PERFORMANCE C ossing ole ance eac ion no ms c ea e geno ype e- anking in hos pe o mance ai s ac oss pa hogen bu den ajec o y. This is simila o any geno ype e- anking ac oss en i onmen al g a- dien s (Via and Lande, 1985), wi h he di e ence ha now he en i onmen is pa hogen bu den o indi iduals (Kause e al., 2012). The wo o ms o G ×E, scaling e ec and geno ype e- anking, acili a e en i onmen -dependen gene ic esponses, ye he e- anking is mo e se e e issue o selec i e b eeding because geno ypes in one en i onmen a e no necessa ily he bes ones in he o he en i onmen s. Re- anking ac oss en i onmen s can be quan i ied by a gene ic co ela ion be ween measu emen s in wo en i onmen s o a gi en ai (Falcone , 1952). The deg ee o e- anking be ween any wo pa hogen bu den le els can be calcula ed by combining andom eg ession esul s wi h co a iance unc ions (Box 1). Fo ins ance, asci es induced mode a e geno ype e- anking in b oile body weigh , he gene ic co ela ion o heal hy bi ds wi h weakly a ec ed bi ds being uni y bu wi h se e ely a ec ed bi ds 0.45 (Kause e al., 2012; Figu e 2). In ield da a se s wi h mul iple en i onmen s, in ec ion p essu e is ypically no he only en i onmen al ac o a ying ac oss en i onmen s, ye he e ec o pa hogen bu den on G ×E could be e ealed using a combined eac ion no m and mul i- ai model (Windig e al., 2011), in which pa hogen bu den is modeled as a con inuous eac ion no m and he disc e e en i- onmen s cap u ing o he en i onmen al ac o s a e modeled as sepa a e disc e e ai s. Pe o ming ex ensi e in ec ion-challenge es s is imp ac ical in many a m animal species, bu he com- bined eac ion no m and mul i- ai model may be an e ec i e addi ional me hod o e ealing he deg ee o G ×Einducedby in ec ions. In ec ions do no induce only geno ype e- anking and a change in a iance bu also changes in he co ela ion s uc u e o esis ance, g ow h, and ep oduc ion ai s (de G ee e al., 2001; Kause e al., 2005, 2012; Ze ehda an e al., 2006; Kuukka-An ila e al., 2010). The modi ica ion o gene ic a chi ec u e o hos ai s by pa hogens, pa asi es, and p oduc ion diseases, media ed by ole ance gene ics, may play a mo e undamen al ole in animal b eeding and mic oe olu ion han has been p e iously hough . DATA REQUIREMENTS FOR RANDOM REGRESSION Ob aining a solid x-axis is a majo challenge o he ole - ance analysis in animals because he x-axis should consis s o indi idual-le el quan i a i e da a on pa hogen bu den (e.g., num- be o pa asi es, pa hogen biomass). Quali a i e da a on bu den (in ec ed s. non-in ec ed indi iduals) c ea es biased es ima es o gene ic a iance o ole ance (Kause, 2011). Mo eo e , i he x-axis consis s o he a e age bu dens o each en i onmen , a he han indi idual-le el bu den measu emen s, hen high hos pe - o mance o a geno ype a a gi en pa hogen bu den can be a esul o high esis ance and/o high ole ance, impeding a p ope ole ance analysis. The analyzed hos pe o mance ai , in u n, can be eed in ake, g ow h, ep oduc ion, su i al o a physio- logical ai , which oge he can be used o e eal mechanisms con ibu ing o a ia ion among geno ypes in ole ance. A spli - amily design wi h bo h an in ec ion- ee con ol and an expe imen al challenge es is he mos e ec i e design o ole ance analysis. In his way, in ec ed animals a e a andom sample o hei amily and hus he e will be a eal causal ela- ionship be ween hos pe o mance and pa hogen bu den (Ti in and Inouye, 2000; Kause, 2011). This equi es, howe e , ha all he challenged indi iduals ge he same pa hogen bu den le el. This a ely is he case because indi iduals ha e inna e indi id- ual a ia ion in esis ance, c ea ing a ia ion in pa hogen bu den e en in a challenge es . Va ia ion in esis ance can be po en ially ela ed o he hos pe o mance ai s used on he y-axis in he ole ance eg ession, biasing he es ima e o gene ic a ia ion o ole ance (Ti in and Inouye, 2000; Kause, 2011). As an al e na- i e o he con ol-and-challenge es design, all indi iduals can be i s eco ded unde in ec ion- ee condi ions (e.g., o ma u e body weigh ), and hen e- eco ded a e expe imen al exposi- ion o equal pa hogen bu den le el. Howe e , such an analysis is unjus i ied in cases in which hos pe o mance shows na u- al empo al a ia ion (e.g., a ia ion in g ow h cu es), which is hus con ounded wi h ole ance (Albe s e al., 1987; Bisse and Mo is, 1996; Woolas on and Windon, 2001). T ypano ole ance o A ican ca le has been analyzed as a change in body weigh in esponse o an expe imen al in ec ion by T ypanosoma congolense, bu al hough he numbe o pa asi es in he blood o indi iduals was eco ded, i was no used o s anda dize he hos pe o mance changes o indi iduals (Hano e e al., 2003; an de Waaij e al., 2003). Unde na u ally occu ing in ec ion, i is possible ha ei he high (o low) pe o ming indi iduals a e in ec ed, leading o biased es ima es o gene ic a ia ion o ole ance (Ti in and Inouye, 2000; Kause, 2011). This is a majo weakness o ield da a se s, because i is well es ablished ha indi iduals wi h ini ially di e en g ow h o li e-his o y ai le els may be di - e en ly exposed o in ec ions, pa asi es and p oduc ion diseases (A end , 1997; Rauw e al., 1998), con ounding he cause-and- e ec ela ion be ween pa hogen bu den and educ ion in hos pe o mance. Random eg ession models equi e la ge sample sizes, e.g., wi hin si e amilies. Dec ease in amily size leads o upwa d- biased gene ic a iance es ima es o ole ance slope (Kause, 2011).Thiscanbeillus a edinasi emodelse up.Whenasmall numbe o indi iduals a e sampled o each si e amily, he sam- ple is no longe ep esen a i e o he ue dis ibu ion and single obse a ions ha e s ong impac on he slope es ima e. Fo some amilies he slope is unde es ima ed, o o he s o e es ima ed, and hus gene ic a iance es ima e o slope is a i icially inc eased. Wi h he i abili y o 0.3 o ole ance slope, mo e han 50 sibs pe amily a e equi ed o ob ain unbiased es ima es o slope a i- ance using a si e model analysis (Kause, 2011). Mo eo e , gene ic co ela ion be ween ole ance slope and in e cep is easily biased downwa d when amily size is low. An upwa d (downwa d) bias in he slope o a amily pushes he in e cep downwa d (upwa d), c ea ing an a i icial nega i e gene ic ade-o when i does no exis in eali y. This can be a oided by using high amily sizes and F on ie s in Gene ics | Li es ock Genomics Decembe 2012 | Volume 3 | A icle 262 |4 Kause and Ødegå d Gene ic analysis o ole ance high numbe o non-in ec ed indi iduals ha o ce he in e cep o a geno ype o be placed close o he eal alue (Mau icio e al., 1997; Kause, 2011). When each hos indi idual has only a single pe o mance eco d, i is possible o es ima e gene ic a iance and b eeding al- ues o ole ance slope, bu no i s esidual a iance. He i abili ies o ole ance slope can be es ima ed when each indi idual has se - e al pe o mance obse a ions, e.g., he ini ial pe o mance unde condi ions o no in ec ion and he ea e he pe o mance a e an in ec ion. By using eg ession slopes o indi iduals as aw obse - a ions in he gene ic analysis, bo h en i onmen al and gene ic componen s o slope a iance and he i abili y can be es ima ed (Schae e , 2004). Random eg ession can be applied o non-linea eac ion no ms (Ki kpa ick e al., 1990; Meye and Hill, 1997; Schae e , 2004) and pla eau-linea eg ession models (Ra agnolo and Misz al, 2000a,b; Kause e al., 2012), and hus he impac o pa hogens on hos pe o mance does no need o be analyzed as a linea ela ionship. A CURE MODEL FOR TIME-UNTIL-DEATH DATA The andom eg ession app oach equi es indi idual-le el da a on pa hogen bu den which may be challenging o eco d. The cu e model o ime-un il-dea h da a p o ides a possibili y o analyze gene ics o esis ance (o suscep ibili y) and endu ance wi hou a need o pa hogen bu den eco ding. Many s udies, especially on aquacul u e species, ha e analyzed su i al o ime-un il-dea h in a challenge es in which indi idu- als a e expe imen ally exposed o a speci ic pa hogen (Ødegå d e al., 2011a). Mo eo e , su i al analysis has been applied o ime-un il-dea h da a when mo ali y ac o s emain unknown (e.g., Duc ocq and Casella, 1996; Se enius and S alde , 2004; Veh iläinen e al., 2010). A ypical assump ion in such analyses is ha indi iduals wi h high p obabili y o su i al a e esis an . Howe e , an indi idual can su i e i i has ei he high esis- ance, o low esis ance bu high ole ance (Figu e 3), o was ne e exposed o a pa hogen. The cu e su i al models a e used o modeling o ime-un il-dea h da a which include a ac ion o non-suscep ible animals, i.e., animals ha a e no liable o die as a esul o he in ec ion (Fa ewell, 1982). Ødegå d e al. (2011c) de eloped a cu e model aiming o dis inguish wo ai s, “suscep- ibili y” and “endu ance,” om ime-un il-dea h da a. These wo concep s may be compa able wi h esis ance and ole ance. In a su i al analysis he in ec ion s a us o each animal is ypically unknown. Unde pa hogen a ack, some animals may be ully capable o a oiding dea h (non-suscep ible), ei he by esis ing he in ec ion, o by a success ul eco e y a e he ini- ial in ec ion due o high ole ance (Figu e 3). Fu he mo e, he deg ee o ole ance may also a y among he suscep ible indi id- uals, po en ially causing a ia ion in hei expec ed ime-un il- dea h. As mo ali y is usually eco ded o e a limi ed ollow-up pe iod, a ac ion o suscep ible animals a e also likely o be ali e a he ime o eco ding. Fo suscep ible animals, he abili y o su i e depends on he expec ed ime-un il-dea h o he animal, which may show gene ic a ia ion. Hence, analogy o he e ms “endu ance” and “suscep ibili y” wi h ole ance and esis ance a e no necessa ily clea -cu in a su i al analysis, due o he ac ha FIGURE 3 | Con ibu ion o esis ance and ole ance o mo ali y due o a speci ic pa hogen. Only indi iduals wi hou esis ance and ole ance will e en ually die gi en a su icien ly long ollow-up pe iod. When ha ing a limi ed ollow-up pe iod, indi iduals wi h high ole ance may s ill be ali e a he end o an expe imen . one only obse es he ex eme ou comes o an in ec ion (whe he o no an animal dies). Al hough “endu ance” and “suscep ibil- i y” a e impossible o sepa a e on indi idual su i o s, hese wo ac o s may s ill be dis inguished on a amily le el using longi udi- nal su i al analysis (i.e., sho - e m mo ali y a es s. long- e m su i al). A classical su i al analysis o ime-un il-dea h assumes ha all indi iduals a e a isk and ha all will e en ually die gi en a su icien ly long ollow-up pe iod. When s udying li espan in gene al his is necessa ily ue, bu may no hold when es ing o mo ali y due o a speci ic pa hogen. Fo non-suscep ible ani- mals ime-un il-dea h will necessa ily be censo ed, i espec i e o he ollow-up ime, and su i al ime may hus be a poo indica o o speci ic pa hogen esis ance. The endu ance e lec s he expec ed mo ali y pe ime-uni among suscep ible indi id- uals, bu will ha e no e ec on su i al o he non-suscep ible indi iduals (Fa ewell, 1982). The su i o s a e likely a mix u e o non-suscep ible long- e m su i o s and a ac ion o suscep ible (bu highly endu e) animals being s ill ali e, and he ue condi ion o each animal is unknown (unless he animal dies). In he cu e model, p oba- bili ies o he al e na i e se ings (non-suscep ible o suscep ible bu s ill ali e) can be es ima ed while simul aneously aking in o accoun a ia ion in endu ance among he su i ing animals (Ødegå d e al., 2011c;Box 2). The cu e model has been applied o ime-un il-dea h da a in a med sh imp challenge- es ed wi h he Tau a synd ome i us (Ødegå d e al., 2011b). I was es ima ed ha al hough 72% o he sh imp su i ed, only 62% could be conside ed non- suscep ible. The unde lying he i abili y (±SE) o suscep ibili y was high (0.41 ±0.07), while he he i abili y o endu ance was low, albei signi ican (0.07 ±0.03). The mos s iking esul was ha endu ance and suscep ibili y we e seemingly dis inc gene ic ai s ( G=0.22 ±0.25). The low gene ic a ia ion o endu ance and he gene ic independency o endu ance and sus- cep ibili y a e in line wi h he esul s on o he animal species (Kause e al., 2012). These esul s ha e subs an ial impac on how disease challenge- es ing should be pe o med. I he aim is o imp o e long- e m su i al unde an in ec ion p essu e, www. on ie sin.o g Decembe 2012 | Volume 3 | A icle 262 |5 Kause and Ødegå d Gene ic analysis o ole ance Box 2 | A cu e model. In a mixed popula ion o suscep ible (z=1) and non-suscep ible (z=0) animals he p obabili y o an indi idual being s ill ali e (censo ed) (c=0) a ime is: P (c=0| )=P (c=0| ,z=1)P (z=1) +P (c=0| ,z=0)P (z=0) =P (c=0| ,z=1)P (z=1)+1−P (z=1), whe e P (z=1)is he p io p obabili y o being suscep ible and P (c=0| ,z=0)=1. The p obabili y o being s ill ali e o sus- cep ible animals, P (c=0| ,z=1), is a unc ion o he endu ance o he animal. Fu he mo e, i su i al ime is spli in o a se ies o bina y su i al sco es (e.g., s1 o s , whe e 0 indica es su i al), his p obabili y is: P (c=0| ,z=1)=  j=1 P (sj=0|z=1), whe e P (sj=0|z=1)is he p obabili y o su i ing a pe iod j, gi en ha he animal is suscep ible. Highly endu e animals will ha e highe p obabili ies o su i ing each sub-pe iod and hus also highe p obabili y o su i ing un il end o ollow-up pe iod. Pu a i e non-suscep ible animals will always su i e. Fo animals ha die du ing he ollow up pe iod, suscep ibili y s a us is known (z=1), while o su i ing animals he ue sus- cep ibili y s a us is no obse able. S ill, o hese indi iduals he p obabili y o being suscep ible can be calcula ed as: P (z=1|c=0, )=P (c=0| ,z=1)P (z=1) P (c=0| ) The p oposed cu e model allows o indi idual a ia ion in bo h p io p obabili y o being suscep ible as well as in he endu ance o suscep ible animals (Ødegå d e al., 2011b,c). A de ailed desc ip ion o he cu e model is gi en in Ødegå d e al. (2011c). selec i e b eeding should ocus on suscep ibili y. This implies ha he ollow-up pe iod should con inue un il he as majo i y o suscep ible animals ha e died, ensu ing ha he obse ed end- su i al la gely esembles he ac ion o non-suscep ible animals in he popula ion. NORMAL MIXTURE MODELS No mal mix u e models can be used o analyze gene ics o hos pe o mance, e.g., g ow h a e, wi hin a popula ion consis ing o indi iduals a ec ed and una ec ed by a pa hogen, e en in he absence o pa hogen bu den and ime-un il-dea h da a. Fini e no mal mix u e models ha e ea lie been p oposed o analysis o in ec ion-a ec ed, con inuously dis ibu ed pheno- ypes, assuming ha he ue in ec ion s a uses o indi iduals a e unknown (De illeux and Le oy, 2000; Ødegå d e al., 2003, 2005; Gianola e al., 2004). The mix u e model a emp s o iden- i y hidden ca ego ies (e.g., non-in ec ed and in ec ed) among he obse a ions, assuming ha he con inuous scale obse a ions o igina e om wo no mal dis ibu ions di e ing in mean and Box 3 | A no mal mix u e model. In a mixed popula ion o in ec ed (z=1) and heal hy (z=0) animals, he densi y o an obse a ion ycan be w i en as: P(y)=P(y|z=0)P (z=0)+P(y|z=1)P (z=1). The p obabili y o an animal being in ec ed is hus: P (z=1|y)=P(y|z=1)P (z=1) P(y|z=0)P (z=0)+P(y|z=1)P (z=1)· A de ailed desc ip ion o he no mal mix u e model is gi en in Ødegå d e al. (2003, 2005). FIGURE 4 | An example o a wo-componen mix u e dis ibu ion. The do ed lines a e he unobse ed dis ibu ions o non-in ec ed “heal hy” indi iduals (70% o he obse a ion) wi h ∼N(−1.0, 1.0) and in ec ed “diseased” indi iduals (30%) wi h ∼N(1.0, 1.0). The solid line ep esen s he esul ing dis ibu ion o he obse ed pheno ypes. T ai alues a e gi en on x-axis and he equencies o obse a ions on y-axis. (po en ially) a iance (Box 3;Figu e 4). Fo ins ance, he b oile asci es example gi en in Figu e 2 can be analyzed using a mix u e model analysis assuming ha he hea a io has wo unde ly- ing dis ibu ions, one o non-in ec ed and one o asci ic bi ds (Ze ehda an e al., 2006). Ano he example o a mix u e ai is soma ic cell sco es in milk o dai y ca le (Madsen e al., 2008). Soma ic cell sco e is a low le el in non-in ec ed cows, bu inc ease o high le els in cases o (unobse ed) subclinical mas i is. Hence, he obse ed soma ic cell sco es may be iewed as a mix u e o wo no mal dis ibu ions (non-in ec ed and mas i ic). In A lan ic salmon Salmo sala L., diseases such as in ec ious panc eas nec o- sis and panc eas disease can kill a ac ion o he animals, bu may also educe subsequen g ow h o a ec ed su i o s. Hence, a e an ou b eak, obse ed g ow h o su i o s may be iewed as a mix u e ai depending on he indi iduals’ p e ious heal h s a us. Classical selec ion aims a changing a ai in he desi ed di ec- ion. Howe e , o mix u e ai s he a ia ion is pa ly explained by mixing o he wo (o mo e) sub-dis ibu ions wi h di e - en means, and pa ly by a ia ion wi hin each sub-dis ibu ion (Figu e 4). Hence, i he aim is o educe he incidence o he in ec ion a he han al e ing he obse ed con inuous hos ai F on ie s in Gene ics | Li es ock Genomics Decembe 2012 | Volume 3 | A icle 262 |6 Kause and Ødegå d Gene ic analysis o ole ance i sel , simple di ec ional selec ion o he la e (e.g., o soma ic cell sco e) may no be op imal. The mix u e model opens new possibili ies o selec ion, and can be used o di ec ly selec o educed in ec ion isk. Addi ionally, he ai eco ded on in ec ed and non-in ec ed animals may be iewed as wo dis inc sub- ai s whose gene ic a iances and hei gene ic co ela ion can be es i- ma ed. This esembles he G ×E analysis pe o med wi h andom eg ession models (Figu e 2) wi h he di e ence ha he mix u e model does no ake in o accoun ha in ec ed indi iduals may ha e di e en pa hogen bu dens. No mal mix u e models ypically assume ha an indi id- ual is ei he in ec ed o no , and ha in ec ion has a ce ain e ec on he pheno ype (Figu e 4). Howe e , a ia ion in en i- onmen al pa hogen load and in indi idual ole ance o he in ec ion imply ha he e ec o an in ec ion may a y sub- s an ially among indi iduals and en i onmen s. The p oposed mix u e models may be ex ended o allow o indi idual esponses o in ec ion (Madsen e al., 2008). Al e na i ely, he model may be ex ended o a g ow h mix u e model (Wang and Bodne , 2007). The g ow h mix u e models assume ha he obse a- ions come om di e en la en ajec o ies, i.e., heal h s a us does no only a ec he expec a ion o indi idual obse a ions, bu also he slope o a pheno ypic ajec o y (g ow h cu es). In ec ed and non-in ec ed animals could show di e en a- jec o ies, wi h he non-in ec ed ones being una ec ed by he pa hogen, while he in ec ed indi iduals being a iably a ec ed by he pa hogen bu den. Such models may be use ul o ana- lyze esis ance and in ec ion-a ec ed ai s obse ed on animals wi h unknown in ec ion s a us and in en i onmen s wi h a iable pa hogen loads. APPLICATION OF THE METHODS IN BREEDING PROGRAMMES Random eg ession models a e ou inely applied in a m ani- mal b eeding p og ams, e.g., o milk es -day models in dai y cows and o g ow h cu es (Schae e , 2004). Simila ly, andom eg ession models can be implemen ed o selec o ole ance, gi en sui able da a a e a ailable. The cu e model app oach o he analysis o ime-un il-dea h da a (Vee kamp e al., 2001; Ødegå d e al., 2011a) ha e been implemen ed in he DMU so wa e, allowing he es ima ion o gene ic pa ame e s and b eeding al- ues o p ac ical b eeding (Madsen and Jensen, 2010). To ou knowledge, he cu e model has no been implemen ed in ou ine gene ic e alua ions in any b eeding p og am. Á nason (1999)and U ios e e al. (2007) ha e p oposed a bi a ia e linea - h eshold model which can be used o analyze whe he an animal su - i ed (a h eshold ai ) and how long i ook un il dea h (a linea ai ). Such a model esembles he cu e model and is s aigh o wa d o apply in mul i- ai b eeding alue e alua- ions. Also he no mal mix u e model has been implemen ed in he DMU so wa e (Madsen and Jensen, 2010), and is he e- o e a ailable o mul i- ai gene ic e alua ions, bu o ou knowledge, has no ye been implemen ed in ou ine gene ic e alua ions. The cu e model has been applied o su i al da a in aqua- cul u e species, leading o al e ed ecommenda ions o ou ine disease-challenge es ing (Ødegå d e al., 2011b). His o ically, challenge es s in aquacul u e species ha e been e mina ed a in e media e cumula i e mo ali ies o ensu e maximum a i- a ion in bina y su i al da a. Howe e , his app oach is only p ope gi en ha endu ance and suscep ibili y a e equi alen ai s, which is no necessa ily he case. The cu en ad ice is o con inue es ing un il mo ali y na u ally ceases, e en a le els abo e 50% mo ali y (Ødegå d e al., 2011a). So a , only a limi ed numbe o b eeding p og ams ha e con- side ed selec ing o ole ance. Some A ican ca le b eeding p o- g ams a e speci ically selec ing o ypano ole ance- ela ed ai s, he ole ance being a majo b eeding objec i e ai (Hano e e al., 2003; an de Waaij e al., 2003). In con as , ega dless o he ex ensi e s udies conduc ed in Aus alia and New Zealand on nema ode ole ance in sheep, a decision has been made no o eco d and selec o ole ance because o he need o le animals o su e and p oduc ion o be educed o ole ance o be exp essed (Albe s e al., 1987; Bisse and Mo is, 1996; Woolas on and Windon, 2001). The no el s a is ical me hods and he inc easing awa eness o he de ailed physiological mech- anisms o ole ance (Medzhi o e al., 2012)mayp o idemo e oppo uni ies o ole ance selec ion in a m animals. CONCLUSIONS The ecen s a is ical de elopmen s p o ide ools o inc ease ou unde s anding o gene ics o al e na i e s a egies o de end agains pa asi es, pa hogens, and p oduc ion diseases. Mos o he s a is ical me hods can be applied in b eeding alue e alua ions o b eed o ole ance. Mo eo e , he me hods p esen ed he e p o- ide ools o quan i y geno ype-by-pa hogen bu den in e ac ions ha may explain a signi ican p opo ion o pheno ypic a ia ion in ai s wi hin popula ions ha a e exposed o a ious in ec ions and p oduc ion diseases. The ai s whose a ia ion is a ec ed a e ypically p oduc ion ai s ha a e selec ed o in b eeding p og ams.Tobeable ounambiguouslyselec o hegene ic po en ial o a p oduc ion ai , he e ec s o esis ance and ol- e ance should be sepa a ed om i . The me hods p esen ed in his pape p o ide po en ial o cons uc mo e e ec i e b eeding p og ams o inc ease bo h p oduc i i y and animal heal h. REFERENCES Albe s,G.A.,G ay,G.D.,Pipe ,L. R.,Ba ke ,J.S.,LeJamb e,L. F., and Ba ge , I. A. (1987). The gene ics esis ance and esilience o Haemonchus con o us in ec ion in young me ino sheep. In . J. Pa asi ol. 17, 1355–1363. A end , J. D. (1997). Adap i e in insic g ow h a es: an in eg a ion ac oss axa. Q. Re . Biol. 72, 149–177. Á nason, T. (1999). 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Recei ed: 29 Ma ch 2012; accep ed: 05 No embe 2012; published online: 14 Decembe 2012. Ci a ion: Kause A and Ødegå d J (2012) The gene ic analysis o ole ance o in ec- ions: a e iew. F on . Gene. 3:262. doi: 10.3389/ gene.2012.00262 This a icle was submi ed o F on ie s in Li es ock Genomics, a special y o F on ie s in Gene ics. Copy igh © 2012 Kause and Ødegå d. This is an open-access a icle dis- ibu ed unde he e ms o he C ea i e Commons A ibu ion License,which pe mi s use, dis ibu ion and ep oduc- ion in o he o ums, p o ided he o igi- nal au ho s and sou ce a e c edi ed and subjec o any copy igh no ices conce n- ing any hi d-pa y g aphics e c. www. on ie sin.o g Decembe 2012 | Volume 3 | A icle 262 |9