New tools for genomic s - PowerPoint Presentation

1. New tools for genomic selection in dairy cattle

2. Why genomic selection works in dairyExtensive historical data available Well-developed genetic evaluation programWidespread use of AI siresProgeny test programsHigh-valued animals, worth the cost of genotypingLong generation interval which can be reduced substantially by genomics

3. Illumina genotyping arraysBovineSNP5054,001 SNPs (version 1)54,609 SNPs (version 2)45,187 SNPs used in evaluationBovineHD777,962 SNPsOnly BovineSNP50 SNPs used >1,700 SNPs in databaseBovineLD6,909 SNPsAllows for additional SNPs BovineSNP50 v2 BovineLDBovineHD

4. Genotyped animals (April 2013)ChipTraditional evaluation?Animal sexHolsteinJerseyBrown SwissAyrshire50KYesBulls 21,904 2,855  5,381 639Cows 16,0621,054110 3NoBulls45,5373,8841,031 325Cows32,892660102 110<50KYesBulls1911289Cows21,9809,1324650NoBulls14,0261,355902Cows158,62218,722658105ImputedYesCows2,71323710312NoCows1,183321128All314,93837,9428,0801,213362,173

5. Marketed Holstein bulls

6. What’s a SNP genotype worth?For the protein yield (h2=0.30), the SNP genotype provides information equivalent to an additional 34 daughtersPedigree is equivalent to information on about 7 daughters

7. And for daughter pregnancy rate (h2=0.04), SNP = 131 daughtersWhat’s a SNP genotype worth?

8. Genotypes and haplotypesGenotypes indicate how many copies of each allele were inheritedHaplotypes indicate which alleles are on which chromosome Observed genotypes partitioned into the two unknown haplotypesPedigree haplotyping uses relativesPopulation haplotyping finds matching allele patterns

9. Haplotyping program – findhap.f90Begin with population haplotypingDivide chromosomes into segments, ~250 to 75 SNP / segmentList haplotypes by genotype matchSimilar to fastPhase, IMPUTE End with pedigree haplotypingDetect crossover, fix noninheritanceImpute nongenotyped ancestors

10. Example Bull: O-Style (USA137611441)Read genotypes and pedigrees Write haplotype segments foundList paternal / maternal inheritanceList crossover locations

11. O-Style Haplotypes Chromosome 15

12. Loss-of-function mutationsAt least 100 LoF per human genome surveyed (MacArthur et al., 2010)Of those genes ~20 are completely inactivatedUncharacterized LoF variants likely to have phenotypic effectsHow should mating programs deal with this?Can we find them?

13. Recessive defect discoveryCheck for homozygous haplotypes7 to 90 expected but none observed 5 of top 11 are potentially lethal936 to 52,449 carrier sire by carrier MGS fertility records3.1% to 3.7% lower conception ratesSome slightly higher stillbirth ratesConfirmed Brachyspina same way

14. Haplotypes affecting fertility & stillbirthNameChromosomeLocationHaplotype FreqEarliest Known AncestorHH15631504001.9Pawnee Farm Arlinda ChiefHH2194.8-96.51.6Willowholme Mark AnthonyHH38954105072.9Glendell Arlinda Chief,Gray View SkylinerHH411,277,2270.37Besne Buck HH5992-942.22Thornlea Texal Supreme JH11511-1612.1Observer Chocolate SoldierBH1742-476.67West Lawn Stretch ImproverBH21910-127.78Rancho Rustic My Design AH11765.9-66.211.8Selwood Betty’s Commander

15. Precision matingEliminate undesirable haplotypesDetection at low allele frequenciesAvoid carrier-to-carrier matingsEasy with few recessives, difficult with many recessivesInclude in selection indicesRequires many inputsUse a selection strategy for favorable minor alleles (Sun & VanRaden, 2013)

16. Sequencing successes at AIPL/BFGLSimple loss-of-function mutationsAPAF1 (HH1) – Spontaneous abortions in Holstein cattle (Adams et al., 2012)CWC15 (JH1) – Early embryonic death in Jersey cattle (Sonstegard et al., 2013)Weaver syndrome – Neurological degeneration and death in Brown Swiss cattle (McClure et al., 2013)

17. Modified pedigree & haplotype design Bull A (1968)AA, SCE: 8Bull B (1962)AA, SCE: 7MGSBull H (1989)Aa, SCE: 14Bull I (1994)Aa, SCE: 18Bull E (1982)Aa, SCE: 8Bull F (1987)Aa, SCE: 15Bull C (1975)AA, SCE: 8δ = 10Bull E (1974)Aa, SCE: 10MGSBull J (2002)Aa, SCE: 6Bull K (2002)Aa, SCE: 15Bull K (2002)aa, SCE: 15These bulls carrythe haplotype withthe largest, negativeeffect on SCE:Bull D (1968)??, SCE: 7Couldn’t obtain DNA:

18. Things can move quickly!Dead calves will begenotyped for BH2statusIf homozygous, wewill sequence in afamily-based designAustrian group alsoworking on BH2(Schwarzenbacheret al., 2012)Strong industrysupport!Semenin CDDRTissue samples (ears)being processed for DNAOwner will collect bloodsamples when bornOwner will collectblood samplesAI firmsending10 unitsof semenBrown Swiss family with possible BH2 homozygotes (dead)

19. Our industry wants new genomic tools

20. We already have some toolshttps://www.cdcb.us/Report_Data/Marker_Effects/marker_effects.cfm`

21. Chromosomal DGV queryhttps://www.cdcb.us/CF-queries/Bull_Chromosomal_EBV/bull_chromosomal_ebv.cfm

22. Now we have a new haplotype queryhttps://www.cdcb.us/CF-queries/Bull_Chromosomal_EBV/bull_chromosomal_ebv.cfm

23. Paternal and maternal DGVShows the DGV for the paternal and maternal haplotylesImputed from 50K using findhap.f90 v.2Can we use them to make mating decisions?People are going to do it – we need to help them!Who is actually making planned matings?

24. Top net merit bull August 2013COOKIECUTTER PETRON HALOGEN (HO840003008710387, PTA NM$ +926, Rel 68%)

25. Pluses and minuses23 positive chromosomes19 negative chromosomes

26. Breeders need MS variance

27. The good and the bad Chromosome 1

28. The best we can do DGV for NM$ = +2,314

29. The worst we can do DGV for NM$ = -2,139

30. Dominance in mating programsQuantitative modelMust solve equation for each mate pairGenomic modelCompute dominance for each locusHaplotype the populationCalculate dominance for mate pairsMost genotyped cows do not yet have phenotypes

31. Inbreeding effectsInbreeding alters transcription levels and gene expression profiles (Kristensen et al., 2005).Moderate levels of inbreeding among active bulls (7.9 to 18.2)Are inbreeding effects distributed uniformly across the genome?Can we find genomic regions where heterozygosity is necessary or not using the current population?

32. Precision inbreedingRuns of homozygosity may indicate genomic regions where inbreeding is acceptableCan we target those regions by selecting among haplotypes?DominanceRecessivesUnder-dominance

33. Challenges with new phenotypesLack of informationInconsistent trait definitionsOften no database of phenotypesMany have low heritabilitiesLots of records are needed for accurate evaluationGenetic improvement can be slowGenomics may help with this

34. Reliability with and without genomicsEventEBV ReliabilityGEBV ReliabilityGainDisplaced abomasum0.300.40+0.10Ketosis 0.280.35+0.07Lameness0.280.37+0.09Mastitis0.300.41+0.11Metritis0.300.41+0.11Retained placenta0.290.38+0.09Average reliabilities of sire PTA computed with pedigree information and genomic information, and the gain in reliability from including genomics.Example: Dairy cattle health (Parker Gaddis et al., 2013)

35. Some novel phenotypes being studiedAge at first calving (Cole et al., 2013)Dairy cattle health (Parker Gaddis et al., 2013)Methane production (de Haas et al., 2011)Milk fatty acid composition (Bittante et al., 2013)Persistency of lactation (Cole et al., 2009)Rectal temperature (Dikmen et al., 2013)Residual feed intake (Connor et al., 2013)

36. What do we do with novel traits?Put them into a selection indexCorrelated traits are helpfulApply selection for a long timeThere are no shortcutsCollect phenotypes on many daughtersRepeated records of limited valueGenomics can increase accuracy

37. TraitRelative value (%)Net meritCheesemeritFluid meritMilk (lb)0–1519Fat (lb)191320Protein (lb)16250Productive life (PL, mo)221522Somatic cell score (SCS, log2)–10–9–5Udder composite (UC)757Feet/legs composite (FLC)434Body size composite (BSC)–6–4–6Daughter pregnancy rate (DPR, %)11812Calving ability (CA$, $)535Genetic-economic indexes 2010 revision

38. TraitRelative emphasis on traits in index (%)PD$1971MFP$1976CY$1984NM$1994NM$2000NM$2003NM$2006NM$2010Milk5227–265000Fat4846452521222319Protein…27534336332316PL………2014111722SCS………–6–9–9–9–10UDC…………7767FLC…………4434BDC…………–4–3–4–6DPR……………7911SCE……………–2……DCE……………–2……CA$………………65Index changes

39. What does it mean to be the worst?Large body sizeEats a lot of expensive feedAverage fertility…or worse!Begin first lactation with dystociaBull calf (sexed semen?)Retained placenta, metritis, etc.Mediocre productionUses many resources, produces very little

40. Dissecting genetic correlationsCompute DGV for 75-SNP segmentsCalculate correlations of DGV for traits of interest for each segmentIs there interesting biology associated with favorable correlations?…and what about linkage disequilibrium?

41. SNP segment correlations Milk with DPRUnfavorable associationsUnfavorable associationsFavorable associationsFavorable associations

42. SNP segment correlations Dist’n over genome

43. Highest correlations for milk and DPRObs chrome seg tloc corr 1 18 449 1890311910 0.53090 2 18 438 1845503211 0.51036 3 8 233 990810677 0.49199 4 26 557 2331662169 0.47173 5 2 60 239796003 0.46507 6 29 596 2483178230 0.45252 7 14 366 1544999648 0.43817 8 2 65 269016505 0.41022 9 11 298 1255667282 0.39734 10 20 469 1971347760 0.3919

44. ConclusionsNon-additive effects may be useful for increasing selection intensity while conserving important heterozygosityWhole-genome sequencing has been very successful at helping economically important loss-of-function mutationsNovel phenotypes are necessary to address global food security and a changing climate

45. AcknowledgmentsPaul VanRaden, George Wiggans, Derek Bickhart, Dan Null, and Tabatha CooperAnimal Improvement Programs Laboratory, ARS, USDA Beltsville, MDTad Sonstegard, Curt Van Tassell, and Steve SchroederBovine Functional Genomics Laboratory, ARS, USDA, Beltsville, MDChuanyu SunNational Association of Animal BreedersBeltsville, MDDan GilbertNew Generation Genetics Inc., Fort Atkinson, WI

46. Questions?http://gigaom.com/2012/05/31/t-mobile-pits-its-math-against-verizons-the-loser-common-sense/shutterstock_76826245/