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Effects of Allele Frequency Estimation on Genomic Predictions and Inbreeding Coefficients

Effects of Allele Frequency Estimation on Genomic Predictions and Inbreeding Coefficients

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Effects of Allele Frequency Estimation on Genomic Predictions and Inbreeding Coefficients

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  1. Effects of Allele Frequency Estimation on Genomic Predictions and Inbreeding Coefficients

  2. Experimental Design • Predict April 2008 PTA from August 2003 PTA • 3,576 older Holstein bulls • 1,759 younger bulls (total = 5,335) • Using 38,416 SNP from Illumina Bovine SNP50TM Chip

  3. Goals • Compare genomic to traditional relationships and inbreeding • Formulas to compute G and A • G – A differences for 5,335 bulls • Compare genomic predictions using different estimates of frequency • Estimate 38,416 allele frequencies • Simple estimates vs. base population • Or ignore frequency, use 0.5 instead

  4. Differences in G and A • Detected clones, identical twins, and duplicate samples • Detected incorrect DNA samples • Detected incorrect pedigrees • Identified correct source of DNA by genomic relationships with other animals

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

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

  7. Close Inbreeding (F=14.7%): Double Grandson of Aerostar Aerostar Megastar Aerostar Chromosome 24

  8. 3 Formulas to Compute G • Sum products of genotypes (g) adjusted for allele frequency (p) • G1jk = ∑ (gij-pi) (gik-pi) / [2 ∑ pi(1-pi)] • Or individually weighted by p • G2jk = ∑ (gij-pi) (gik-pi) / 2pi(1-pi) • Or scaled by intercept (b0) and regression (b1) on A, using p = 0.5 • G3jk = [∑ (gij - 0.5) (gik - 0.5) – b0] / b1

  9. Compare A with 3 formulas for GSimulated Data 1Diagonal = 1 + Inbreeding

  10. Compare A with 3 formulas for GActual Data 1Diagonal = 1 + Inbreeding

  11. Summary of G Formulasfor Genomic Inbreeding • Correlations ranked G3 > G1 > G2 in simulation vs. G2 > G1 > G3 with real data (opposite) • G2 and G1 biased down, G3 up • G1 and G2 can be adjusted toward A using b0 and b1, similar to G3 formula • After adjusting, mean G1 = 1.08 and G2 = 1.09 compared to G3 = 1.13 and A = 1.05 • G1 was unbiased in simulation using true rather than estimated frequencies

  12. Allele Frequency Estimation • Base population frequencies • Combine genotypes and pedigrees • Efficient algorithm (Gengler, 2007) • Simple frequency estimates • pi = ∑ gij / 2n • Extra simple estimates (p = 0.5) • Z = 0.5, 0, -0.5 in mixed model

  13. Effects of Frequency on G1

  14. R2 of Genomic Predictions

  15. Conclusions • Genomic relationships and inbreeding are more useful than Wright’s 1922 pedigree formulas • Formulas to compute G have • Large effects on inbreeding coefficients • Small effects on reliability of predictions • Estimates of allele frequencies • For base population better than simple • Not needed using regression of G on A

  16. Acknowledgments • Funding: • National Research Initiative grants • CDDR Contributors (NAAB, Semex) • Genotyping and DNA extraction: • BFGL, U. Missouri, U. Alberta, GeneSeek, GIFV, and Illumina • Computing from AIPL staff • George Wiggans, Leigh Walton