1 Domestic animal genomes meet animal breeding translational biology in the ag-biotech sector Jerry Taylor University of Missouri-Columbia
2 Worldwide distribution and use of cattle A brief history of cattle Genome wide selection (molecular breeding) Illumina BovineSNP BeadChip SNP discovery Design Prediction of genetic merit in Angus Characterization of US Angus Gene mapping Molecular breeding Outline
3 Worldwide Distribution and Use of Cattle >B animals Meat, milk, draft, leather, investment India, Brazil & China have largest inventories US is the largest beef producer.m animals - ¾ beef; ¼ dairy (July, ) Beef production is the th largest manufacturing industry, with $B retail equivalent value in India, Brazil & China have largest cattle inventories
4 Brief History of Cattle Cattle domesticated ~ Kyr Several sites of domestication Ancestral Bos primigenius (auroch) extinction in Poland Two subspecies (Bos( taurus taurus & Bos taurus indicus) ) diverged ~ Kyr Two biological types (Beef vs Dairy) Breed development is very recent ~ yr Genetic Improvement Selective breeding based on progeny testing, phenotypes and pedigrees:
5 Years of Gene Mapping Don t t know how many genes underlie variation Limited design and power of studies to date Have tests for very few genes and they: Explain little of the genetic variation in a trait Single marker tests = EPDs with Accuracies ~. (or in some cases) Don t t exist for many important traits Feed efficiency, fertility, longevity, disease resistance Are not used by Breed Associations for genetic evaluation Cost too much!!!
6 Evolution of Genetic Improvement Can we predict genetic merit without phenotypes or pedigrees & when we don t know the identity of the genes?
7 Genome Wide Selection
8 Genome Wide Selection Bull s Genome pairs of chromosomes Bull s Genome, genes Marbling genes Blocks of SNPs span marbling genes and predict genetic merit The concept of GWS is to use high resolution map (K SNPs) to partition the genome into small segments and estimate the contribution of each chromosomal segment and haplotype to genetic merit a form of GWA analysis REQUIRES HIGH DENSITY SNP GENOTYPING ASSAY
9 Status of Bovine Genomics.X sequence assembly BCM (Btau.) Inbred (F=.) Hereford cow K high quality SNPs in K reads Shotgun reads from breeds.m low quality SNPs in Hereford assembly Low conversion rates and not uniformly distributed Genotyping reagents Affymetrix K and K MIPs platform assays Insufficient SNPs to design K assay!!! Genomic distribution major issue
10 SNP Discovery Sequenced reduced representation libraries using Solexa (Illumina) Genome Analyzer M sequences K randomly distributed SNPs Known genomic coordinates % designable % real Allele frequency estimated Cost $. per SNP Approach will work for ANY species Current applications in Swine, Sheep and Soybeans K SNPs for $K
11 BovineSNP Design Beadchip real estate (beadtypes), No. SNPs designed,,, Infinium I (A/T or G/C beadtypes/snp;.%) Infinium II ( beadtype/snp;.%) SNPs passing decode, SNPs with Illumina genotype calls, Polymorphic SNPs.,. Illumina BovineSNP. Affymetrix Targeted Genotyping Bovine K SNP Panel..... Frequency.
12 Chip Metrics Assay repeatability: >.% Mendelian inheritance: >.% Call rate: >.% Average MAF = % in Bos taurus taurus
13 Angus Cattle in the US, animals in generation pedigree, Bulls with DNA in generations sl a m i n A. o N Birth Year
14 Angus Pedigree
15 Angus Cattle in the US Angus Pedigree. AI Bulls Angus imported to Ne =.±.. t n e ic if. f e o C g n i d. e e r b n I e ga. r e v A. y =.x -. R² =.. Birth Year
16 Angus Cattle in the US
17 All Traits Are Under Selection B ir t h W e ig h t W e a n in g W e ig h t M ilk y =. x -. R ² =. y =. x -. R ² =. y =. x -. R ² =. lb lb lb B ir t h Y e a r - B ir t h Y e a r Ye a r lin g W e ig h t B ir t h Y e a r M a t u r e W e ig h t C a r c a s s W e ig h t y =. x -. R ² =. y =. x -. R ² =. y =. x -. R ² =. lb lb lb B ir t h Y e a r - B ir t h Y e a r M a t u r e H e ig h t B ir t h Y e a r C a lv in g E a se M a t e rn a l S c r o t a l C ir c u m fe r e n c e... y =. x -. R ² =.. in s h t ir b d e sti ss an u %.. y =. x -. R ² =. - y =. x -. R ² =... in B ir t h Y e a r -. B ir t h Y e a r F a t T h ic k n e s s B ir t h Y e a r M a r b lin g R ib e y e M u sc le A r e a.... y = -. x +. R ² =.... y =. x -. R ² =. y =. x -. R ² =... in -. e r co S. -. in B ir t h Y e a r B ir t h Y e a r -. B ir t h Y e a r
18 BovineSNP Genotyping BTA Unassigned Total #SNP Mb N/A N/A. #SNP/Mb N/A N/A. Mean call rate =. % Mean MAF =.% Mean heterozygosity =.%
19 Genome Wide Association Analyses Yearling Weight.... )P. ( g. o l Cumulative Mb Genomewide P<. significance
20 Genome Wide Association Analyses Marbling... )P (. g o l-.... Cumulative Mb
21 How Many SNP Associations? BTA AF Unassigned Total BWT WWT MILK YW YH CW MB REA FT MW MH SC UIMF UREA UFAT GV QG YG GV BV CED CEM EN$ WV$ These do not represent unique genes multiple SNPs are associated with each gene
22 How Many Genes? Genomewide P<. Genomewide P<. Genomewide SNPs (QTL) explain.% of Vg for marbling
23 Why Is This Important? Commercialization Model Assay cost ~US$ (volume dependent) Test sales price ~US$ High penetration in worldwide dairy AI industry for progeny testing (~, animals/yr) Moderate penetration in beef industry Cost must be reduced to allow deep market penetration Technology options:, SNP GoldenGate (~US$) SNP assay (~$) How do we select SNPs for these assays?
24 Conclusions High density SNP assays will revolutionize mapping and association studies in livestock Allow the localization (but not identification) of the major genes underlying genetic variation Enable Genome Wide Selection in animal species However There are many genes underlying livestock traits and many SNPs will have to be tested to predict merit Can we make GWS cost effective? Do we still need to identify the genes? Probably not for GWS Yes if we want to understand (and manipulate) the pathways underlying animal physiology NextNext-gen sequencing?
25 Conclusions Potentially huge range of assay applications: Phylogenetic analysis Detection of hybridization Estimation of relationship and inbreeding coefficients for genetic evaluation Mate selection for heterosis maximization