An Introduction to Genomics

1. An Introduction to Genomics

2. Genomic Goals • Predict young bulls and cows more accurately • Compare actual DNA inherited • Use exact relationship matrix G instead of expected values in A • Trace chromosome segments • Locate genes with large effects

3. Short history • Illumina BovineSNP50™ BeadChip developed • Accuracy of genomic information assessed by using 2004 evaluations of bulls born before 2000 to predict 2009 evaluations of young bulls • Unofficial genomic evaluations of bull calves provided to industry beginning in April 2008 • Jersey results released in October 2008 • Over 23,000 animals genotyped through Mar. 2009

4. Source of genomic evaluations • DNA extracted from blood, hair, or semen • ~40,000 genetic markers (SNPs) evaluated • For each SNP, difference in PTA estimated between animals with 1 allele compared to the other allele • Genomic data contribute ~11 daughter equivalents to reliability

5. How Related are Relatives? • Example: Full sibs • are expected to share 50% of their DNA on average • may actually share 45% or 55% of their DNA because each inherits a different mixture of chromosome segments from the two parents. • Combine genotype and pedigree data to determine exact fractions

6. Genomic Relationships • Measures of genetic similarity • A = Expected % genes identical by descent from pedigree (Wright, 1922) • G = Actual % of DNA shared (using genotype data) • T = % genes shared that affect a given trait (using genotype and phenotype)

7. Markers vs QTLs • Models contain markers, not QTLs • M is markers inherited minus freq • M M’ / ∑ p(1-p) = G • List all QTL affecting a trait • Q is alleles inherited minus freq • q contains effects of alleles • u = Q q , var(q) = Vq • var(u) = E(u u’) = Q Vq Q’ = T

8. QTL Relationship Matrix (T) • Three bulls have +50 PTA protein. • Do they have the same genes? • Extremely unlikely. • Bull A could have 10 positive genes. • Bull B could have 10 positive genes, but on different chromosomes. • Bull C could have 20 positive and 10 negative genes.

9. Genes in Common at One Locus w = gene from sire of sire x = gene from dam of sire y = gene from sire of dam z = gene from dam of dam

10. Unrelated Individuals? • No known common ancestors • Many unknown common ancestors born before the known pedigree • Relationships in base • 0 ± x.x% due to earlier ancestors • Called linkage disequilibrium (LD) • Poor terminology, genes may not be physically linked

11. Traditional Pedigree

12. Genomic Pedigree

13. Example of a SNP haplotype SNP SNP SNP Chr1 caacgtat … atccgcat … tctaggat … Chr2 caacggat … atccgaat … tctcggat … Haplotype 1 tca Haplotype 2 gac Haplotype is a set of single nucleotide polymorphisms (SNPs) associated on a single chromosome. Identification of a few alleles of a haplotype block can identify other polymorphic sites in the region.

14. SNP Pedigree atagatcgatcg ctgtagcgatcg ctgtagcttagg agatctagatcg agggcgcgcagt cgatctagatcg ctgtctagatcg atgtcgcgcagt cggtagatcagt agagatcgcagt agagatcgatct atgtcgctcacg atggcgcgaacg ctatcgctcagg

15. Haplotype Pedigree atagatcgatcg ctgtagcgatcg ctgtagcttagg agatctagatcg agggcgcgcagt cgatctagatcg ctgtctagatcg atgtcgcgcagt cggtagatcagt agagatcgcagt agagatcgatct atgtcgctcacg atggcgcgaacg ctatcgctcagg

16. Translate Haplotype to Genotype ctgtagcgatcg agatctagatcg 111211120200

17. Genotype PedigreeCount number of second allele 0 = homozygous for first allele (alphabetically) 1 = heterozygous 2 = homozygous for second allele (alphabetically)

18. Genotype Data for ElevationChromosome 1 1000111220020012111011112111101111001121100020122002220111 1202101200211122110021112001111001011011010220011002201101 1200201101020222121122102010011100011220221222112021120120 2010020220200002110001120201122111211102201111000021220200 0221012020002211220111012100111211102112110020102100022000 2201000201100002202211022112101121110122220012112122200200 0200202020122211002222222002212111121002111120011011101120 0202220001112011010211121211102022100211201211001111102111 2110211122000101101110202200221110102011121111011202102102 1211011022122001211011211012022011002220021002110001110021 1021101110002220020221212110002220102002222121221121112002 0110202001222222112212021211210110012110110200220002001002 0001111011001211021212111201010121202210101011111021102112 2111111212111210110120011111021111011111220121012121101022 202021211222120222002121210121210201100111222121101

19. Genotype Data from Inbred BullChromosome 24 of Megastar 1021222101021021011102110112112211211002202000222020002020220 0000220020222202202000020020222222000020222200000220200002002 2002000000222200022220000000000020222022002000222020222220002 2022222222200002002202022202000200022000000002202220000002200 2020002222002020020020202220222222220222020002022022022220202 2202020202200022002220220022200000220200002002002000200222220 0022220202002220022202000020200000022222020200002002002222000 2022022220022000222202200222202020002202202222002220022000200 2202000002200220222000022000022000222202002222000220020020202 2020002220002220022202202200000220220020020020220002000222202 2002220020220200222202220000020220002020020202000220022000002 2022200202220200022002000200022002002000200220222220022022000 2000020002000020220020220200200002220000222002000200222000022 0220020022002202202020202020200022202000220200202202220220000 2020200002020200022222200222200020022022220000020220020200202 022022020200002000200220220002200

20. Genotype Parents and Grandparents

21. Expected Relationship Matrix11HO9167 O-Style 1Calculated assuming that all grandparents are unrelated

22. Pedigree Relationship Matrix1HO9167 O-Style

23. Genomic Relationship Matrix 1HO9167 O-Style

24. Difference (Genomic – Pedigree) 1HO9167 O-Style

25. Conclusions • Relationships can be defined as: • A = expected genes in common • G = actual DNA in common • T = QTL alleles in common for a trait • Full sibs share 50% ± 3.5% of DNA. • “Unrelated” animals share more or fewer unknown ancestors than average. • Reliability can increase if genomic (G) replace traditional (A) relationships

26. Where are the Major Genes?

27. Distribution of Marker Effects (Net Merit)

28. Distribution of Marker Effects (DPR)

29. Marker Effects on Website

30. Marker Effects on Website

31. Marker Effects on Website

32. Positive or Negative Traits

33. From whom did the bad allele come?Round Oak Rag Apple Elevation (7HO00058)

34. Net Merit by ChromosomeFreddie (1HO08784) - highest Net Merit bull

35. Net Merit by ChromosomeO Man (7HO06417) – Sire of Freddie

36. Net Merit by ChromosomeDie-Hard (29HO08538) - maternal grandsire

37. New Chromosomal PTA Query

38. Chromosomal PTA Query Example

39. Acknowledgments • Genotyping and DNA extraction: • USDA Bovine Functional Genomics Lab, U. Missouri, U. Alberta, GeneSeek, Genetics & IVF Institute, Genetic Visions, and Illumina • Computing: • AIPL staff (Mel Tooker, Leigh Walton, Jay Megonigal) • Funding: • National Research Initiative grants • 2006-35205-16888, 2006-35205-16701 • Agriculture Research Service • Holstein, Jersey & Brown Swiss breed associations • Contributors to Cooperative Dairy DNA Repository (CDDR)