EAAP abstract 26496 Paul VanRaden

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Presentation transcript:

Genomic tools to improve progress and preserve variation for future generations EAAP abstract 26496 Paul VanRaden Animal Genomics and Improvement Laboratory Agricultural Research Service, USDA, Beltsville, MD, USA paul.vanraden@ars.usda.gov

Topics Evaluations adjusted for pedigree inbreeding and expected future inbreeding (EFI) in USA since 2005 Average relationships across time Average relationships across countries Genomic mating programs Rare favorable alleles Crossbreeding and gene flow Genetic progress

Pedigree inbreeding trends by breed

Average relationships across time Future generations are more related than past The animal’s own inbreeding coefficient (F) is constant Average progeny F remains constant if no new progeny EBVs and PTAs are affected by inbreeding depression What progeny merit should EBV or PTA include? Average F of past progeny? Average F of current progeny? Expected F for future progeny (EFI)? YES! Examine progeny F and EFI of famous Holstein bulls

Daughter inbreeding (famous Holstein bulls)

Daughter EFI (famous Holstein bulls)

U.S. PTAs are adjusted for inbreeding Trait Inbreeding depression/1% Trait value in NM$ $ Value /1% F Milk –63.9 –0.004 0.3 Fat –2.37 3.56 –8.4 Protein –1.89 3.81 –7.2 Productive life –0.26 21 –5.5 Somatic cell score 0.004 –117 –0.5 Daughter pregnancy rate –0.13 11 –1.4 Cow conception rate –0.16 2.2 –0.4 Heifer conception rate –0.08 –0.2 Cow livability 12 –1.0 Net merit $ –25 1

Example EFI adjustment for OMan Difference of EFI – daughter F = 9.0 – 5.4 = 3.6% Economic loss (future – past daughters) = 3.6 ($25/1%F) = $90 OMan’s initial NM$ = +$426 before adjustment OMan’s official NM$ = +$336 after adjustment As the population becomes more related to an animal, its evaluations decrease Progeny, grandprogeny, etc., also adjusted because their EFIs tend to be higher than breed average

Foreign bull–U.S. Holstein cow relationships Country Bulls EFI NM$ USA 4,140 7.0 +308 GBR 279 6.5 +145 NLD 1,143 6.4 +179 AUS 271 5.9 +27 DEU 999 6.6 +175 ESP 234 6.8 +98 FRA 940 +180 IRL 186 3.9 –44 ITA 870 6.7 +127 ISR 130 4.5 +118 NZL 762 3.2 –34 EST 89 5.1 –26 DFS 744 5.8 +232 CHE 84 6.3 –18 CAN 719 7.1 +210 CZE 72 POL 646 6.1 +74 KOR 61 +41 JPN 470 +101 BEL 43 +211 Correlation (EFI, NM$) = 0.72 (proven bulls born since 2009)

Bull inbreeding vs. relationship to population (Holstein)

Bull inbreeding vs. relationship to population (Jersey)

Mating programs Pedigree and “corrective” mating programs popular Since 2015, the G matrix is provided in a bulk file for >1.5 million females  3,000 marketed males Relationships (G and A) are displayed on web for about 100,000 animal requests per year via free public query: http://www.holsteinusa.com/services?action=HBK_GetInbreedSpecs Reports economic loss and inbreeding depression per trait

Recessive defects Lethal recessives contribute only a small fraction of the additive genetic variance for fertility Many were detected by lack of homozygous haplotypes Further DNA sequencing revealed the exact mutations for many of these Defects common in one breed are often absent or rare in other breeds (recent mutations within a breed)

Causal variants for recently discovered haplotypes Breed Chromosome Location Gene AH1 Ayrshire 17 65,921,497 UBE3B AH2 Ayrshire2 3 51,267,548 RPAP2 BH1 Brown Swiss 7 ? BH2 19 11,063,520 TUBD1 HH0 Holstein 21 21,184,869 – 21,188,198 FANCI HH1 5 63,150,400 APAF1 HH2 1 HH3 8 95,410,507 SMC2 HH4 1,277,227 GART HH5 9 93,223,651 – 93,370,998 TFB1M HCD 11 77,958,995 APOB JH1 Jersey 15 15,707,169 CWC15 JH2 26 Causal variant found in 10 of 13 haplotypes BH1, HH2 and JH2 still left to find 2AH2 is not yet officially released

Selection for rare favorable alleles How to increase genetic variance? Genetic variance increases as allele frequency increases to 0.5; variance declines after that Rare favorable alleles can be accidentally lost via drift Genomic selection can increase both genetic mean and variance by placing more weight on rare, favorable alleles

Rare favorable alleles Source: Sun & VanRaden, 2013, Interbull annual meeting presentation Further information: Sun & VanRaden, 2014, Increasing long-term response by selecting for favorable minor alleles, PLoS ONE 9:e88510

Rare favorable alleles Source: Sun & VanRaden, 2013, Interbull annual meeting presentation Further information: Sun & VanRaden, 2014, Increasing long-term response by selecting for favorable minor alleles, PLoS ONE 9:e88510

Genomic breed composition Breed base representation (BBR) reported since 2016 for all 1.9 million genotyped animals, including >60,000 crossbreds, of which >35,000 are not yet evaluated BBR estimates contributions of 5 breeds Breed percentages forced to sum to 100% Reported as purebred if any breed >94% Ayrshires and Scandinavian Reds treated as 1 breed Pedigree breed composition (reported since 2007) often less accurate and complete than genomic BBR

Top young Jersey bulls (before) Name Breed composition1 % Unknown Ped % Jersey % Holstein BBR NM$ Cespedes 92 83 8 2 16 777 Familia 93 87 7 13 759 Marlo 89 75 11 25 744 Bauer 81 3 737 Tyrion 736 1BBR = Genomic breed base representation, Ped = Pedigree breed composition After filling missing ancestors/breed codes in pedigree to match reported BBR Ranking based on April 2017 NM$

Top young Jersey bulls (after) Name Breed composition1 % Unknown Ped % Jersey % Holstein BBR NM$ Cespedes 92 91 8 1 777 Familia 93 94 7 6 759 Marlo 89 87 11 13 744 Bauer 88 9 3 737 Tyrion 736 1BBR = Genomic breed base representation, Ped = Pedigree breed composition After filling missing ancestors/breed codes in pedigree to match reported BBR Ranking based on April 2017 NM$

Nongenotyped cow* in top 50 Holsteins Generation Breed Cow, Sire, MGS, MGGS, etc. NM$ 1 HOL VTF CABRIOLET 5128 5392 +821 2 CO-OP ROBUST CABRIOLET +875 3 LADYS-MANOR PL SHAMROCK +602 4 HIDDEN-VIEW POMEROY +171 5 RDC PETERSLUND +524 6 PAULO-BRO CEL JASPER –116 7 JER MVF BOLD VENTURE DANIEL +62 8 SOONER CENTURION –114 *Cow HOUSA000073484907 owned by Virginia Tech research herd

Purebred Definition I was taught All known ancestors are from the same breed All pedigree paths trace back to importation I am not purebred (many ancestors are unknown) Human genotyping companies mainly report “breed composition” (where your genes are from)

Genomic evaluations on all-breed scale Convert MACE and foreign dam data to all-breed scale Estimate SNP effects for each breed on all-breed scale Traits: Milk, fat, protein, productive life, somatic cell score, daughter pregnancy rate, cow conception rate, heifer conception rate, and livability Conformation and calving traits still within breed Jersey SNP effects used for crossbred conformation To compute evaluations for crossbreds, SNP effects for each breed blended by BBR

Introgression, gene editing, transgenics Move a few haplotypes from 1 breed into another Editing “Fast” introgression of easily predicted gene effects Transgenics Introgression of haplotypes or genes from another species Breed preservation or replacement Largest breeds benefit most from genomic tools

Reviews/summaries/other ideas Howard, Pryce, Baes, & Maltecca. 2017. Invited review: Inbreeding in the genomics era: Inbreeding, inbreeding depression, and management of genomic variability. JDS 100:6009–6024. 2017 ADSA symposium: Inbreeding in the genomics era Optimal contribution theory Balance progress and inbreeding depression in a closed population if 1 organization has full control Other measures of homozygosity and inbreeding

Conclusions Adjusting EBVs for differences between past and future inbreeding (EFI or GFI) selects for outcrosses Selecting for rare alleles or against GFI give less short term but more long term response (>10 generations) Choosing bulls that are less related to the population usually also reduces average net merit Weighting SNP effects by breed composition can accurately evaluate crossbred animals Using genomic tools can preserve variation while increasing progress

Acknowledgements Mel Tooker (USDA), Gary Fok (USDA), Jay Megonigal (CDCB), and Chuanyu Sun (STgenetics) for assistance with computing and for slides Council on Dairy Cattle Breeding (CDCB) for data USDA-ARS project 1265-31000-101-00, “Improving Genetic Predictions in Dairy Animals Using Phenotypic and Genomic Information”