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Genomic evaluations in Holstein dairy cattle have quickly become more reliable over the last two years in many countries as more animals have been genotyped for 50,000 markers. Evaluations can also include animals genotyped with more or fewer markers using new tools such as the 777,000 or 2,900 marker chips recently introduced for cattle. Gains from more markers can be predicted using simulation, whereas strategies to use fewer markers have been compared using subsets of actual genotypes. Thedoi:10.1186/1297-9686-43-10 pmid:21366914 pmcid:PMC3056758 fatcat:k5v2h6qf7jdzpch25cvcax5fwu
more »... erall cost of selection is reduced by genotyping most animals at less than the highest density and imputing their missing genotypes using haplotypes. Algorithms to combine different densities need to be efficient because numbers of genotyped animals and markers may continue to grow quickly. Methods: Genotypes for 500,000 markers were simulated for the 33,414 Holsteins that had 50,000 marker genotypes in the North American database. Another 86,465 non-genotyped ancestors were included in the pedigree file, and linkage disequilibrium was generated directly in the base population. Mixed density datasets were created by keeping 50,000 (every tenth) of the markers for most animals. Missing genotypes were imputed using a combination of population haplotyping and pedigree haplotyping. Reliabilities of genomic evaluations using linear and nonlinear methods were compared. Results: Differing marker sets for a large population were combined with just a few hours of computation. About 95% of paternal alleles were determined correctly, and > 95% of missing genotypes were called correctly. Reliability of breeding values was already high (84.4%) with 50,000 simulated markers. The gain in reliability from increasing the number of markers to 500,000 was only 1.6%, but more than half of that gain resulted from genotyping just 1,406 young bulls at higher density. Linear genomic evaluations had reliabilities 1.5% lower than the nonlinear evaluations with 50,000 markers and 1.6% lower with 500,000 markers. Conclusions: Methods to impute genotypes and compute genomic evaluations were affordable with many more markers. Reliabilities for individual animals can be modified to reflect success of imputation. Breeders can improve reliability at lower cost by combining marker densities to increase both the numbers of markers and animals included in genomic evaluation. Larger gains are expected from increasing the number of animals than the number of markers.
Accurate genotype imputation can greatly reduce costs and increase benefits by combining whole-genome sequence data of varying read depth and array genotypes of varying densities. For large populations, an efficient strategy chooses the two haplotypes most likely to form each genotype and updates posterior allele probabilities from prior probabilities within those two haplotypes as each individual's sequence is processed. Directly using allele read counts can improve imputation accuracy anddoi:10.1186/s12863-015-0243-7 pmid:26168789 pmcid:PMC4501077 fatcat:ut5vav3nn5bafpnsljt33smsiu
more »... ce computation compared with calling or computing genotype probabilities first and then imputing. Results: A new algorithm was implemented in findhap (version 4) software and tested using simulated bovine and actual human sequence data with different combinations of reference population size, sequence read depth and error rate. Read depths of ≥8× may be desired for direct investigation of sequenced individuals, but for a given total cost, sequencing more individuals at read depths of 2× to 4× gave more accurate imputation from array genotypes. Imputation accuracy improved further if reference individuals had both low-coverage sequence and high-density (HD) microarray data, and remained high even with a read error rate of 16 %. With read depths of ≤4×, findhap (version 4) had higher accuracy than Beagle (version 4); computing time was up to 400 times faster with findhap than with Beagle. For 10,000 sequenced individuals plus 250 with HD array genotypes to test imputation, findhap used 7 hours, 10 processors and 50 GB of memory for 1 million loci on one chromosome. Computing times increased in proportion to population size but less than proportional to number of variants. Conclusions: Simultaneous genotype calling from low-coverage sequence data and imputation from array genotypes of various densities is done very efficiently within findhap by updating allele probabilities within the two haplotypes for each individual. Accuracy of genotype calling and imputation were high with both simulated bovine and actual human genomes reduced to low-coverage sequence and HD microarray data. More efficient imputation allows geneticists to locate and test effects of more DNA variants from more individuals and to include those in future prediction and selection.
Detecting Genotyping Errors In practical applications, genotype elimination can also be useful for detection of genotype errors prior to likelihood computation (with programs such as Ped-Check [O'Connell ...doi:10.1086/302663 pmid:10577928 pmcid:PMC1288385 fatcat:jfmhuf4orzbk5l2v7rooiolzmm
When two genes interact to cause a clinically important phenotype, it would seem reasonable to expect that we could leverage genotypic information at one of the loci in order to improve our ability to detect the other. We were therefore interested in extending the posterior probability of linkage (PPL), a class of linkage statistics we have been developing over the past decade, in order to explicitly allow for gene × gene interaction. In this report we utilize a new implementation of the PPLdoi:10.1186/1753-6561-1-s1-s64 pmid:18466565 pmcid:PMC2367485 fatcat:gu76pwe7lbhrxpj76ndgrxqwva
more »... orporating liability classes (LCs), which provide a direct parameterization of gene × gene interaction by allowing the penetrances at the locus being evaluated to depend upon measured genotypes at a known locus. With knowledge of the generating model for the simulated rheumatoid arthritis (RA) data, we selected two loci for examination: Locus A, which in interaction with the HLA-DR antigen locus affects risk of the dichotomous RA phenotype; and Locus E, which in interaction with DR affects quantitative levels of the anti-CCP phenotype. The data comprised nuclear families of two parents and an affected sib pair (ASP). Our results confirm theoretical work suggesting that gene × gene interactions CANNOT be leveraged to improve linkage detection for dichotomous traits based on affecteds-only data structures. However, incorporation of DR-based LCs did lead to appreciably higher quantitative trait PPLs. This suggests that gene × gene interactions could be effectively used in quantitative trait analyses even when families have been ascertained as ASPs for a related dichotomous trait.
If any of the 350 variants on either side of a specific marker were correlated i.e. with an |r| higher than 0.95, editing based on LD retained one variant and removed all others that had an |r| higher ... Comparison of simulated and real selection Properties of the real sequence data from the 1000 Bull Genomes Project were similar to those of the simulated data by VanRaden and O'Connell  . ...doi:10.1186/s12711-017-0307-4 pmid:28270096 pmcid:PMC5339980 fatcat:s4y2lkathbgpnhom53hfmj74t4
VITESSE (O'Connell and Weeks 1995) will identify any errors due to an incorrect disease model. ...doi:10.1086/301904 pmid:9634505 pmcid:PMC1377228 fatcat:t53qqnwphjbanexrkwddif6yqq
Moreover, tag SNPs selected from the HapMap CEU sample captured a substantial portion of the common variation in the OOA ($88%) at r 2 Z0.8. ... allele frequency difference for autosomal SNPs was 0.05, with an inter-quartile range of 0.02-0.09, and for autosomal SNPs 10-20 kb apart with common alleles (minor allele frequencyZ0.05), the LD measure r ... quality control criteria in both samples: (1) r5% uncalled genotypes; (2) r5 and r1 Mendelian inconsistencies in OOA and CEU samples, respectively, using pedigree diagnostics as implemented in PedCheck [O'Connell ...doi:10.1002/gepi.20444 pmid:19697356 pmcid:PMC2811753 fatcat:fah4ezeo4ndipfxibzhr5a5pd4
Upper triangle, r 2 ; lower triangle, DЈ. ... r 2 ϭ0.45 to 0.84; DЈϭ0.94 to 1) and weaker between the exon 10 SNP and all others (r 2 ϭ0.02 to 0.12; DЈϭ0.35 to 1; see Table 3 ). ...doi:10.1161/01.atv.0000136384.53705.c9 pmid:15205219 fatcat:iffwys2bh5epla6hvqo4zntthq
Visualizations (boxplots, bar charts, pedigree charts) are written in R  . ... disequilibrium (LD) calculations relative to a user-specified reference variant as well as buttons for exploring LD with LocusZoom  and Haploview  (Figures 2 & 3) . for region in APOB. r^ ...doi:10.1101/2021.05.02.442370 fatcat:xhhkjtomqjhn3ivlwi3leb5qnq
O'Connell, MD, MSc, FRCSC 1E4, Walter C. ...doi:10.1097/gox.0000000000000740 pmid:27579242 pmcid:PMC4995721 fatcat:buro3ug5t5h6rgyv7equbymguq
0.6 -1.0) compared to their much lower correlation with GlcCer(d34:1) (r <0.2). ... R 69 was used to calculate the pairwise phenotypic Pearson correlations for lipidomics and traditional lipids. ...doi:10.1101/2021.05.21.445208 fatcat:czltckofkjbgrc6zyohzpxrl4a
Citation: Sun C, VanRaden PM, Cole JB, O'Connell JR (2014) Improvement of Prediction Ability for Genomic Selection of Dairy Cattle by Including Dominance Effects. PLoS ONE 9(8): e103934. ... Manhattan plots of the additive and dominance effects were created using ggplot2  , version 0.9.2, and R-2.15.1  . ... genomic relationship matrices, respectively; s 2 a , s 2 a 1 , s 2 a 2 and s 2 a 3 are additive variances; s 2 are dominance variances; s 2 e , s 2 e 1 , s 2 e 2 , and s 2 e 3 are residual variances, and R ...doi:10.1371/journal.pone.0103934 pmid:25084281 pmcid:PMC4118992 fatcat:vdwaapyt5jgijmed2jtcpj25se
None of the three SNPs were in significant linkage disequilibrium with each other (pairwise r 2 values were all Ͻ0.01). ...doi:10.1210/jc.2005-0549 pmid:16204371 fatcat:s7fmog5d45d7nknehiwmbqt2xu
BMC Research Notes
The numbers within each box of the correlation matrix represent pairwise r 2 value between SNPs. ... These SNPs captured 100% of common variants (>5%) in HapMap CEU in the SOCS7 gene at r 2 > 0.95 (HapMap Release 36). ...doi:10.1186/1756-0500-6-235 pmid:23767996 pmcid:PMC3686602 fatcat:tizarhtvhrdkzndku7igy5hexe
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