QTL Mapping, MAS, and Genomic Selection

Transcription

1 QTL Mappig, MAS, ad Geomic Selectio Dr. Be Hayes Departmet of Primary Idustries Victoria, Australia A short-course orgaized by Aimal Breedig & Geetics Departmet of Aimal Sciece Iowa State Uiversity Jue 4-8, With fiacial support from Pioeer Hi-bred It. USE AND ACKNOWLEDGEMENT OF SHORT COURSE MATERIALS Materials provided i these otes are copyright of Dr. Be Hayes ad the Aimal Breedig ad Geetics group at Iowa State Uiversity but are available for use with proper ackowledgemet of the author ad the short course. Materials that iclude refereces to third parties should properly ackowledge the origial source.

2 Likage Disequilbrium to Geomic Selectio

3 Course overview Day Likage disequilibrium i aimal ad plat geomes Day 2 QTL mappig with LD Day 3 Marker assisted selectio usig LD Day 4 Geomic selectio Day 5 Geomic selectio cotiued

4 Marker Assisted Selectio usig LD LD-MAS with sigle markers How may QTL to use i LD-MAS? Bias i QTL effects LD-MAS with marker haplotypes LD-MAS with the IBD approach Gee assisted selectio Optimisig the breedig scheme with marker iformatio

5 Marker Assisted Selectio usig LD Marker assisted selectio (MAS) ca be based o DNA markers i likage equilibrium with a QTL (LE-MAS) i likage disequilibrium with a QTL (LD-MAS) actual mutatio causig QTL effect (Gee-MAS). All three types of MAS are curretly used i the livestock idustries (Dekkers 2004).

6 Table. Examples of gee tests used i commercial breedig for differet species (D = dairy cattle, B = beef cattle, C = poultry, P = pigs, S = sheep) by trait category ad type of marker Trait category Direct marker Likage disequilibrium marker Liakge equilibrium marker Cogeital defects BLAD (D a ) Citruliaemia (D,B b ) DUMPS (D c ) CVM (D d ) Maple syrup urie (D,B e ) Maosidosis (D,B f ) RYR (P g ) RYR (P h ) Appearace CKIT (P i ) Polled (B ) MCR/MSHR (P j,b k,d l ) MGF (B m ) Milk quality -Casei (D o ) ß-lactoglobuli (D o ) FMO3 (D p ) Meat quality RYR (P g ) RYR (P h ) RN/PRKAG3 (P q ) RN/PRKAG3 (P r ) A-FABP/FABP4 (P s ) H-FABP/FABP3 (P t ) CAST (P u, B v ) >5 PICmarq (P w ) THYR (B x ) Lepti (B y ) Feed itake MC4R (P z ) Disease Prp (S aa ) B blood group (C bb ) F8 (P cc ) K88 (P dd ) Reproductio Booroola (S ee ) Booroola (S ff ) Iverdale(S gg ) ESR (P hh ) Haa (S ii ) PRLR (P jj ) RBP4 (P kk ) Growth ad compositio MC4R (P z ) CAST (P u ) QTL (P ll ) IGF-2 (P mm ) IGF-2 (P ) Myostati (B oo ) QTL (B pp ) Callipyge (S qq ) Carwell (S rr ) Milk yield ad compositio DGAT (D ss ) PRL (D tt ) QTL (D uu ) GRH (D vv ) -Casei (D o )

7 Marker Assisted Selectio usig LD LE-MAS is most difficult to implemet. marker-qtl phase withi each family must be established before a icrease i selectio respose ca be realised. LD-MAS ow very attractive due to very large umbers of sigle ucleotide polymorphism (SNP) markers suitable for LD mappig ow available. Gee-MAS requires eormous amout of work ad resources!!

8 Marker Assisted Selectio usig LD LD-MAS as a two step procedure. Step. Effects of a marker or set of markers are estimated i a referece populatio. Step 2. The breedig values of a group of selectio cadidates are calculated usig the marker iformatio.

9 Marker Assisted Selectio usig LD LD-MAS as a two step procedure. Step. Effects of a marker or set of markers are estimated i a referece populatio. Step 2. The breedig values of a group of selectio cadidates are calculated usig the marker iformatio. I may cases, the selectio cadidates will have o pheotypic iformatio of their ow, eg youg dairy bulls which are progey test cadidates.

10 Marker Assisted Selectio usig LD LD-MAS as a two step procedure. Step. Effects of a marker or set of markers are estimated i a referece populatio. Step 2. The breedig values of a group of selectio cadidates are calculated usig the marker iformatio.

11 LD-MAS with sigle markers Estimate effects of marker or markers i referece populatio y = μ + X g+ Zu μ g u = ' X' Z' 'X X'X Z'X 'Z X'Z Z'Z + A λ 'y X'y Z'y

12 Marker Assisted Selectio usig LD LD-MAS as a two step procedure. Step. Effects of a marker or set of markers are estimated i a referece populatio. Step 2. The breedig values of a group of selectio cadidates are calculated usig the marker iformatio.

13 LD-MAS with sigle markers Predict breedig values usig marker iformatio: MEBV + = u X g

14 LD-MAS with sigle markers Example Aimal Sire Dam Pheotpe SNP allele SNP allele

15 LD-MAS with sigle markers The data was simulated as a SNP effect of for 2 allele plus effect of sire of 3 ad sire 5 of -3 + radom effect

16 LD-MAS with sigle markers Example Aimal Sire Dam Pheotpe SNP allele SNP allele

17 Marker Assisted Selectio usig LD LD-MAS as a two step procedure. Step. Effects of a marker or set of markers are estimated i a referece populatio. Step 2. The breedig values of a group of selectio cadidates are calculated usig the marker iformatio.

18 LD-MAS with sigle markers Build: + = Z'y X'y 'y A Z'Z Z'X Z' X'Z X'X X' 'Z 'X ' u λ g μ

19 LD-MAS with sigle markers Example ad X record X Aimal Sire Dam Pheotpe SNP allele SNP allele

20 LD-MAS with sigle markers Example Z Aimal Sire Dam Pheotpe SNP allele SNP allele aimal record

21 LD-MAS with sigle markers Example A λ=/2 Aimal Sire Dam Pheotpe SNP allele SNP allele Aimal aimal

22 LD-MAS with sigle markers μ g u Example Solve equatios.. ' = X' Z' 'X X'X Z'X Z'Z X'Z + 'Z A λ 'y X'y Z'y μ 2.69 g 0.87 u

23 Marker Assisted Selectio usig LD LD-MAS as a two step procedure. Step. Effects of a marker or set of markers are estimated i a referece populatio. Step 2. The breedig values of a group of selectio cadidates are calculated usig the marker iformatio.

24 LD-MAS with sigle markers Predict breedig values usig marker iformatio: MEBV + = u X g

25 LD-MAS with sigle markers Predict breedig values usig marker iformatio: MEBV + = u X g u g MEBV X =

26 LD-MAS with sigle markers Predict breedig values usig marker iformatio: MEBV + = u X g u g MEBV X =

27 LD-MAS with sigle markers Predict breedig values usig marker iformatio: MEBV + = u X g u g MEBV X =

28 LD-MAS with sigle markers The data was simulated as a SNP effect of for 2 allele plus effect of sire of 3 ad sire 5 of -3 + radom effect u g MEBV X =

29 LD-MAS with sigle markers Corr(MEBV,TBV) =0.93 u X g MEBV TBV =

30 LD-MAS with sigle markers Corr(MEBV,TBV) =0.93 Corr(EBV,TBV)=? + = Z'y 'y A Z'Z Z' 'Z ' u λ μ

31 LD-MAS with sigle markers Corr(MEBV,TBV) =0.93 Corr(EBV,TBV)= = Z'y 'y A Z'Z Z' 'Z ' u λ μ

32 Marker Assisted Selectio usig LD LD-MAS with a sigle marker How may QTL to use i LD-MAS? Bias i QTL effects LD-MAS with marker haplotypes LD-MAS with the IBD approach Gee assisted selectio Optimisig the breedig scheme with marker iformatio

33 How may QTL to use i LD-MAS Advatage of MAS over o-mas approximately proportioal to proportio of total geetic variace explaied by QTL Estimates of umber of QTL per trait betwee 00 ad 200 Do we eed to track all these with markers?

34 How may QTL to use i LD-MAS 00 Percetage of geetic variace accouted for Pigs Dairy Percetage of QTL (raked i order of size)

35 How may QTL to use i LD-MAS If we use 0-20 QTL per trait i our LD-MAS program, we will exploit ~ 50% of the geetic variace. Assumes we have perfect kowledge of the QTL alleles. The proportio of geetic variace captured at each QTL i LD-MAS depeds o the extet of likage disequilibrium betwee the marker ad the QTL.

36 How may QTL to use i LD-MAS Use multiple regressio to estimate vector of SNP effects with multiple markers y = μ + X g + X g e

37 How may QTL to use i LD-MAS Use multiple regressio to estimate vector of SNP effects with multiple markers + = y Z y X y X y A Z Z X Z X Z Z Z X X X X X X Z X X X X X X Z X X u g g ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' ' λ μ

38 How may QTL to use i LD-MAS Use multiple regressio to estimate vector of SNP effects with multiple markers Accouts for the fact that some SNPs may be pickig up the same QTL

39 F-value Positio (Millios of Basepairs)

40 LD-MAS with sigle markers Predict breedig values usig marker iformatio: MEBV = u+ X g+ X2 g2 +...

41 How may QTL to use i LD-MAS Use multiple regressio to estimate vector of SNP effects with multiple markers (radom?) μ ' g = X ' g X2' 2 Z' u X 'X X 2 'X Z'X + Iλ 'X 'X Z'X + Iλ Z'Z + A Use variace compoet estimatio to get SNP effects X 2 X 'X 'X X X 2 'Z 'Z 'Z λ 'y X'y X 'y 2 Z'y

42 Marker Assisted Selectio usig LD LD-MAS with a sigle marker How may QTL to use i LD-MAS? Bias i QTL effects LD-MAS with marker haplotypes LD-MAS with the IBD approach Gee assisted selectio Optimisig the breedig scheme with marker iformatio

43 Accoutig for bias i QTL effects Strog tedecy to overestimate QTL effects i a geome sca, as these effects ca exceed sigificace thresholds if the estimate is larger tha the actual effect due to a large positive error term

44 Accoutig for bias i QTL effects Strog tedecy to overestimate QTL effects i a geome sca, as these effects ca exceed sigificace thresholds if the estimate is larger tha the actual effect due to a large positive error term This over-estimatio is more proouced i geome scas of low power, positive error term must be large to overcome the sigificace threshold.

45 Accoutig for bias i QTL effects Strog tedecy to overestimate QTL effects i a geome sca, as these effects ca exceed sigificace thresholds if the estimate is larger tha the actual effect due to a large positive error term This over-estimatio is more proouced i geome scas of low power, positive error term must be large to overcome the sigificace threshold. If the QTL effect is over-estimated, the advatage of MAS ca be eroded substatially (eg LD-MAS with a sigle marker)

46 Accoutig for bias i QTL effects Strog tedecy to overestimate QTL effects i a geome sca, as these effects ca exceed sigificace thresholds if the estimate is larger tha the actual effect due to a large positive error term This over-estimatio is more proouced i geome scas of low power, positive error term must be large to overcome the sigificace threshold. If the QTL effect is over-estimated, the advatage of MAS ca be eroded substatially (eg LD-MAS with a sigle marker) Must regress QTL effects prior to use i MAS

47 Accoutig for bias i QTL effects Accuracy of predictig MEBV SNP effects regressio factor Pedigree oly data set 2 Pedigree ad Markers data set SNP effect regressio factor

48 Accoutig for bias i QTL effects Optios for estimatig ubiased estimates of QTL effect Best method is to estimate QTL effects i a populatio which is completely idepedet of the sample used i the origial geome sca where the QTL were first detected. This will also validate that the markers are ot a artefact of the statistical model used i the geome sca or some uaccouted for populatio stratificatio. But maybe too expesive Use prior kowledge of distributio of QTL effects to regress effects Cross validatio

49 Accoutig for bias i QTL effects Use prior kowledge of distributio of QTL effects to regress effects The for a give size of experimet ad estimated size of effect, we ca calculate the true effect

50 Accoutig for bias i QTL effects Use prior kowledge of distributio of QTL effects to regress effects The for a give size of experimet ad estimated size of effect, we ca calculate the true effect See Weller et al for distributios of QTL effects across traits

51 Accoutig for bias i QTL effects Cross validatio split data set i two regress solutios from data set two o data set oe to get b xx2 the the regressio of the true effects of the SNPs o the solutios from the full data set is b u,xt = 2b xx2 /(+b xx2 )

52 Accoutig for bias i QTL effects Cross validatio Solutios split y = 0.282x R 2 = Solutios split

53 Accoutig for bias i QTL effects Cross validatio split data set i two regress solutios from data set two o data set oe to get b xx2 the the regressio of the true effects of the SNPs o the solutios from the full data set is b u,xt = 2b xx2 /(+b xx2 ) = 0.44

54 Marker Assisted Selectio usig LD LD-MAS with sigle markers How may QTL to use i LD-MAS? Bias i QTL effects LD-MAS with marker haplotypes LD-MAS with the IBD approach Gee assisted selectio Optimisig the breedig scheme with marker iformatio

55 Model: LD-MAS with haplotypes MEBV + = u Xg g is a vector of haplotype effects, eg. Haplotype Effect

56 LD-MAS with haplotypes Accuracy of LD-MAS with haplotypes Depeds o Proportio of QTL variace explaied by haplotypes Number of haplotype effects to estimate Number of pheotypic records Accuracy of iferrig haplotypes

57 LD-MAS with haplotypes Accuracy of LD-MAS with haplotypes Depeds o Proportio of QTL variace explaied by haplotypes Number of haplotype effects to estimate Number of pheotypic records Accuracy of iferrig haplotypes

58 LD-MAS with haplotypes Accuracy of LD-MAS with haplotypes Depeds o Proportio of QTL variace explaied by haplotypes r 2 ( h, q) = 2 Di i = p i q q 2

59 LD-MAS with haplotypes Example: SNPs geotyped i 379 Agus aimals Select a SNP from the at radom to be a QTL determie Nearest marker 2, 4 or 6 marker haplotypes Haplotypes estimated usig PHASE program (Stephes et al. 200) This takes ito accout LD structure i the cattle populatios Calculate the proportio of QTL variace explaied by the marker haplotypes.

60 Results Number of markers i haplotype Proportio of QTL variace explaied by marker haplotypes

61 Results Q q Number of markers i haplotype Proportio of QTL variace explaied by marker haplotypes

62 Results Proportio of QTL variace explaied by marker haplotypes Q 2 Q Q q Number of markers i haplotype

63 LD-MAS with haplotypes Example: Proportio of QTL variace explaied Maximum umber of haplotypes Observed umber of haplotypes Nearest marker Best marker Marker haplotypes Marker haplotypes Marker haplotypes

64 2 LD-MAS with haplotypes Accuracy of estimatig QTL allele effects from haplotypes: = + = i i i T p p q h r h q r 2 / ), ( ), ( λ / h λ = σ e σ

65 LD-MAS with haplotypes Accuracy of estimatig QTL allele effects from haplotypes: 0.6 Accuracy of predictig QTL allele effect Number of pheotypic records 2 Nearest marker Best marker Two marker haplotype Four marker haplotype Six marker haplotype

66 LD-MAS with haplotypes Accuracy of LD-MAS with haplotypes Depeds o Proportio of QTL variace explaied by haplotypes Number of haplotype effects to estimate Number of pheotypic records Accuracy of iferrig haplotypes??

67 Marker Assisted Selectio usig LD LD-MAS with sigle markers How may QTL to use i LD-MAS? Bias i QTL effects LD-MAS with marker haplotypes LD-MAS with the IBD approach Gee assisted selectio Optimisig the breedig scheme with marker iformatio

68 LD-MAS with the IBD approach MEBVs: MEBV + = u v

69 LD-MAS with the IBD approach MEBVs: MEBV + = u v μ u g = ' Z' W' Z'Z + 'Z A W'Z λ 'W Z'W W'W + G 'y Z'y W'y Where W is a matrix allocatig records to QTL allele effects

70 LD-MAS with the IBD approach Has the potetial to be most accurate method for LD-MAS because ca capture likage iformatio as well Particularly with sub-optimal markers desities

71 Marker Assisted Selectio usig LD LD-MAS with sigle markers How may QTL to use i LD-MAS? Bias i QTL effects LD-MAS with marker haplotypes LD-MAS with the IBD approach Gee assisted selectio Optimisig the breedig scheme with marker iformatio

72 Gee Assisted Selectio Greatest icreases i respose (ot limited by LD) Simplest, cheapest to implemet i breedig program No eed to establish phase withi families Cost of discovery very high Number of examples ow (Dekkers 2004) May become apparet that mode of iheritace is ot additive Eg. IGF2 mutatio i pigs is imprited (oly expressed if mutated allele from father)

73 Marker Assisted Selectio usig LD LD-MAS with sigle markers How may QTL to use i LD-MAS? Bias i QTL effects LD-MAS with marker haplotypes LD-MAS with the IBD approach Gee assisted selectio Optimisig the breedig scheme with marker iformatio

74 Optimisig the breedig scheme with MAS Which traits Age at selectio?

75 Optimisig the breedig scheme with MAS Expected respose from MAS Traits measured o both sexes before selectio << traits measured o oe sex before selectio << traits measured after selectio << traits measured o relatives Traits measured before selectio Traits measured o oe sex before selectio Traits measured after selectio Traits measured o relatives Growth Feed itake Pigs bor alive Carcass quality Fatess Milk productio Fertility Disease resistace (fish) Disease resistace (cattle)

76 o-mas MAS Geeratio GI Geeratio NFI 0 Average value of ucleus ($) Average value of ucleus ($) PBA Geeratio Geeratio MQI Average value of ucleus ($) Average value of ucleus ($)

77 Optimisig the breedig scheme with MAS Which traits Age at selectio G=irσ g /L where G =geetic gai i is the itesity of selectio r is the accuracy of selectio σ g is the geetic stadard deviatio ad L is the geeratio legth

78 Optimisig the breedig scheme with MAS Age at selectio We have already discussed improvig r What about L? Accuracy of traditioal EBVs icrease as aimal ages ad it ad its relatives acquire pheotypic data. But aimals ca be typed for markers at ay age Gai i accuracy from markers greatest at youg age. So if selectio optimised, marker data should lead to a decrease i geeratio legth Eg. i dairy cattle selected for milk productio, MAS leads to greater gais if selectio of yearlig bulls ad cows is practiced tha if a traditioal progey testig system is adhered to Reproductive techologies?

79 Take home poits Markers i LD with QTL relatively easy to use i breedig programs Usig haplotypes may improve accuracy? IBD approach allows likage iformatio to be used as well Respose: Traits measured o both sexes before selectio << traits measured o oe sex before selectio << traits measured after selectio << traits measured o relatives Optimal use of marker iformatio with selectio at youger ages