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ABSTRACT: Call rates on both a single nucleotide polymorphism (SNP) basis and an animal basis are used as measures of data quality and as screening tools for genomic studies and evaluations of dairy cattle. To investigate the relationship of SNP call rate and genotype accuracy for individual SNP, the correlation between percentages of missing genotypes and parent-progeny conflicts for each SNP was calculated for 103,313 Holsteins. Correlations ranged from 0.14 to 0.38 for the BovineSNP50 and BovineLD (Illumina Inc., San Diego, CA) and GeneSeek Genomic Profiler (Neogen Corp., Lincoln, NE) chips, with lower correlations for newer chips. For US genomic evaluations, genotypes are excluded for animals with a call rate of <90% across autosomal SNP or <80% across X-specific SNP. Mean call rate for 220,175 Holstein, Jersey, and Brown Swiss genotypes was 99.6%. Animal genotypes with a call rate of ≤99% were examined from the US Department of Agriculture genotype database to determine how genotype call rate is related to accuracy of calls on an animal basis. Animal call rate was determined from SNP used in genomic evaluation and is the number of called autosomal and X-specific SNP genotypes divided by the number of SNP from that type of chip. To investigate the relationship of animal call rate and parentage validation, conflicts between a genotyped animal and its sire or dam were determined through a duo test (opposite homozygous SNP genotypes between sire and progeny; 1,374 animal genotypes) and a trio test (also including conflicts with dam and heterozygous SNP genotype for the animal when both parents are the same homozygote; 482 animal genotypes). When animal call rate was ≤80%, parentage validation was no longer reliable with the duo test. With the trio test, parentage validation was no longer reliable when animal call rate was ≤90%. To investigate how animal call rate was related to genotyping accuracy for animals with multiple genotypes, concordance between genotypes for 1,216 animals that had a genotype with a call rate of ≤99% (low call rate) as well as a genotype with a call rate of >99% (high call rate) were calculated by dividing the number of identical SNP genotype calls by the number of SNP that were called for both genotypes. Mean concordance between low- and high-call genotypes was >99% for a low call rate of >90% but decreased to 97% for a call rate of 86 to 90% and to 58% for a call rate of <60%. Edits on call rate reduce the use of incorrect SNP genotypes to calculate genomic evaluations.
Journal of Dairy Science 03/2013; · 2.56 Impact Factor
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ABSTRACT: Genetic parameters were estimated for mobility score and 16 linear type traits of Brown Swiss dairy cattle. Mobility is an overall assessment trait that measures a cow's ability to move, as well as the structure of her feet, pasterns, and legs. Scores from 50 to 99 were assigned by appraisers for the Brown Swiss Cattle Breeders' Association of the USA beginning in June 2007. Only scores made before 69 mo of age were used. After edits, 32,710 records were available for 19,472 cows in 819 herds. The model included fixed effects for the interaction of herd and appraisal date (2,109 groups), appraisal age within parity (46 groups), and lactation stage within parity (21 groups), as well as random effects for animal, permanent environment, and residual error. A multi-trait analysis was conducted using canonical transformation, multiple diagonalization, and a decelerated expectation-maximization REML algorithm. Heritability was estimated to be 0.21 for mobility and ranged from 0.06 to 0.37 for the other 16 type traits. The traits with the highest genetic correlation with mobility were final score (0.78), rear legs (rear view; 0.74), rear udder width (0.52), and foot angle (0.51). Predicted transmitting ability (PTA) for mobility was calculated using the Brown Swiss multi-trait type evaluation system but included only appraisals for which all traits had been scored. For the 1,868 bulls evaluated, PTA for mobility ranged from 1.6 to -1.8 with a standard deviation of 0.5 and was most highly correlated with PTA for final score (0.88), rear legs (rear view; 0.77), rear udder height (0.70), and rear udder width (0.69), as expected from the trait correlations. When matched with official US national evaluations from August 2011, PTA mobility had moderately high correlations with PTA for milk, fat, and protein yields, as well as productive life (0.31-0.41). The mobility trait may provide a more accurate assessment of the structural soundness required for longevity than does the feet-legs composite.
Journal of Dairy Science 02/2013; · 2.56 Impact Factor
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ABSTRACT: Selection, mating, and improvement of dairy animals have required accurate pedigrees. Genomic tools allow paternal ancestors to be easily confirmed or discovered because most sires are genotyped for many markers, but maternal ancestors are more difficult to discover because most female ancestors are not genotyped. Three methods to discover maternal grandsires (MGS) were developed and compared. Conflicts were counted one single nucleotide polymorphism (SNP) at a time between genotypes of the animal and potential MGS (duo method) or also using the sire's genotype (trio method). Alternatively, haplotypes of a potential MGS were matched to the animal's maternal haplotype, obtained by using linkage across loci (HAP method). The duo and trio methods can be performed as soon as a genotype is received because no imputation is required. The HAP method improved accuracy because genotypes with 2,683 (3K) SNP were imputed to the 45,187 (50K) SNP used for genomic evaluation. The HAP method was tested using modified pedigrees with 5% of true MGS replaced by a random genotyped bull from the same birth year and 5% of MGS set to missing for 4,134 Holsteins, 552 Jerseys, and 142 Brown Swiss that had confirmed, genotyped sires. Those same animals were used to test the duo and trio methods, except that some animals had multiple genotypes and imputed dams were excluded. Accuracy measured how often the correct MGS was selected from among 12,152 genotyped Holstein, 2,265 Jersey, and 1,605 Brown Swiss potential MGS. Accuracies were 61, 60, and 65%, respectively, with the duo method; 95, 91, and 94% with the trio method; and 97, 95, and 97% with the HAP method. Accuracy of the duo method was poor (only 52% for animals genotyped with 3K and 65% with 50K) because additional information from the paternal genotype is not used. Accuracy of the trio method was 97% with 50K but only 78% with 3K because the missing SNP were not imputed. Accuracy of the HAP method was 94% with 3K genotypes, 98% with 50K, and 92% with nongenotyped, imputed dams. When the HAP method was extended to great-grandsires, the accuracy of maternal great-grandsire discovery was 92% for 652 Holsteins, 95% for 33 Jerseys, and 85% for 20 Brown Swiss. Accuracy was even higher using simulated genotypes. Because most dairy bulls over several generations have been genotyped, percentages of haplotypes shared with candidate males can accurately confirm, correct, or discover the sires, MGS, and even more distant ancestors of most animals.
Journal of Dairy Science 01/2013; · 2.56 Impact Factor
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ABSTRACT: The GeneSeek Genomic Profiler (GGP) BeadChip (GeneSeek, Lincoln, NE), which became available commercially in February 2012, is based on the Illumina BovineLD Genotyping BeadChip (Illumina Inc., San Diego, CA), with 1,745 additional single nucleotide polymorphisms (SNP) for genomic evaluation and SNP for proprietary single-gene tests. The BovineLD chip with 6,909 SNP, which replaced the Illumina GoldenGate Bovine3K Genotyping BeadChip, has been available since October 2011. The GGP's additional SNP for genomic evaluation were selected to improve imputation by filling SNP gaps on chromosomes and including more Bovine3K SNP than were on the BovineLD chip and to impute microsatellite alleles to facilitate parentage validation. The SNP for single-gene tests were included to minimize the number of separate tests required for those genes, particularly for bulls. The September 2012 US national genomic evaluation included genotypes from BovineLD and GGP chips for 82,510 animals. For those data, BovineLD and GGP performance was similar. The call rate for SNP on these chips that were used in genomic evaluation was 99.6%. The 9 Y-chromosome SNP in common on the BovineLD and GGP chips were highly effective in sex validation (call rate of 99% for males and 0.01% for females). For both chips, the rate of parent-progeny conflicts on a SNP basis (≤0.004%) was similar to that for SNP on the Illumina BovineSNP50 Genotyping BeadChip. Imputation accuracy for 45,187 BovineSNP50 SNP averaged 99.4% for Holsteins. Imputation accuracy was slightly higher for the GGP chip compared with the BovineLD chip because of its additional SNP. Reliability for genomic evaluations using BovineLD and GGP genotypes was 3 percentage points higher than that for Bovine3K genotypes.
Journal of Dairy Science 12/2012; · 2.56 Impact Factor
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P M Vanraden,
D J Null,
M Sargolzaei, G R Wiggans,
M E Tooker,
J B Cole,
T S Sonstegard,
E E Connor,
M Winters,
J B C H M van Kaam,
A Valentini,
B J Van Doormaal,
M A Faust,
G A Doak
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ABSTRACT: Genomic evaluations for 161,341 Holsteins were computed by using 311,725 of 777,962 markers on the Illumina BovineHD Genotyping BeadChip (HD). Initial edits with 1,741 HD genotypes from 5 breeds revealed that 636,967 markers were usable but that half were redundant. Holstein genotypes were from 1,510 animals with HD markers, 82,358 animals with 45,187 (50K) markers, 1,797 animals with 8,031 (8K) markers, 20,177 animals with 6,836 (6K) markers, 52,270 animals with 2,683 (3K) markers, and 3,229 nongenotyped dams (0K) with >90% of haplotypes imputable because they had 4 or more genotyped progeny. The Holstein HD genotypes were from 1,142 US, Canadian, British, and Italian sires, 196 other sires, 138 cows in a US Department of Agriculture research herd (Beltsville, MD), and 34 other females. Percentages of correctly imputed genotypes were tested by applying the programs findhap and FImpute to a simulated chromosome for an earlier population that had only 1,112 animals with HD genotypes and none with 8K genotypes. For each chip, 1% of the genotypes were missing and 0.02% were incorrect initially. After imputation of missing markers with findhap, percentages of genotypes correct were 99.9% from HD, 99.0% from 50K, 94.6% from 6K, 90.5% from 3K, and 93.5% from 0K. With FImpute, 99.96% were correct from HD, 99.3% from 50K, 94.7% from 6K, 91.1% from 3K, and 95.1% from 0K genotypes. Accuracy for the 3K and 6K genotypes further improved by approximately 2 percentage points if imputed first to 50K and then to HD instead of imputing all genotypes directly to HD. Evaluations were tested by using imputed actual genotypes and August 2008 phenotypes to predict deregressed evaluations of US bulls proven after August 2008. For 28 traits tested, the estimated genomic reliability averaged 61.1% when using 311,725 markers vs. 60.7% when using 45,187 markers vs. 29.6% from the traditional parent average. Squared correlations with future data were slightly greater for 16 traits and slightly less for 12 with HD than with 50K evaluations. The observed 0.4 percentage point average increase in reliability was less favorable than the 0.9 expected from simulation but was similar to actual gains from other HD studies. The largest HD and 50K marker effects were often located at very similar positions. The single-breed evaluation tested here and previous single-breed or multibreed evaluations have not produced large gains. Increasing the number of HD genotypes used for imputation above 1,074 did not improve the reliability of Holstein genomic evaluations.
Journal of Dairy Science 10/2012; · 2.56 Impact Factor
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ABSTRACT: Traditional evaluations of cows with genotypes have been adjusted since April 2010 to be comparable with evaluations of bulls so that their value for estimation of single nucleotide polymorphism effects in genomic evaluation programs would be improved. However, that adjustment made them not comparable with traditional evaluations of nongenotyped cows. To create an adjustment for all cows with an evaluation based on US data, Mendelian sampling, which is the difference between predicted transmitting ability (PTA) and parent average (PA), was calculated for milk, fat, and protein yields and divided by a deregression factor. Standard deviations for the deregressed Mendelian sampling (DMS) were grouped by reliability with PA contribution removed (REL(no PA)). A multiplicative adjustment to reduce the DMS standard deviation for cows so that it would be the same as for bulls with similar REL(no PA) was represented as a linear function of REL(no PA). Mean cow PA by birth year was subtracted from individual bull and cow PA to create within-year PA deviation groups, and mean DMS was calculated by PA deviation group. Means decreased for bulls and increased for cows with increasing deviation. The differences were fit by linear regression on PA deviation and used to adjust cow DMS. The adjustment reduced PTA of cows with a high PA and increased PTA of cows with a low PA but did not change estimated genetic trend because adjustment was within birth year. The adjustment also reduced variance of cow evaluations within birth year. Traditional evaluations of genotyped cows with a REL(no PA) of ≥55% were further adjusted so that the difference between those evaluations and direct genomic values calculated using only bulls as predictors was similar to that for bulls. The second adjustment was small compared with a 2010 adjustment and, therefore, had little effect on the comparability of evaluations for genotyped and nongenotyped cows. Cows with converted evaluations from other countries were excluded from the predictor population, and their converted evaluations were adjusted so that the difference between their mean PTA and direct genomic value was the same as the corresponding difference for bulls. For cows with converted evaluations, the adjustment amount differed depending on REL(no PA) (<55% or ≥55%). The new adjustment was implemented by USDA in April 2011 and permits a fairer comparison of estimated genetic merit between nongenotyped and genotyped cows.
Journal of Dairy Science 06/2012; 95(6):3444-7. · 2.56 Impact Factor
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ABSTRACT: Genomic evaluations using genotypes from the Illumina Bovine3K BeadChip (3K) became available in September 2010 and were made official in December 2010. The majority of 3K-genotyped animals have been Holstein females. Approximately 5% of male 3K genotypes and between 3.7 and 13.9%, depending on registry status, of female genotypes had sire conflicts. The chemistry used for the 3K is different from that of the Illumina BovineSNP50 BeadChip (50K) and causes greater variability in the accuracy of the genotypes. Approximately 2% of genotypes were rejected due to this inaccuracy. A single nucleotide polymorphism (SNP) was determined to be not usable for genomic evaluation based on percentage missing, percentage of parent-progeny conflicts, and Hardy-Weinberg equilibrium discrepancies. Those edits left 2,683 of the 2,900 3K SNP for use in genomic evaluations. The mean minor allele frequencies (MAF) for Holstein, Jersey, and Brown Swiss were 0.32, 0.28, and 0.29, respectively. Eighty-one SNP had both a large number of missing genotypes and a large number of parent-progeny conflicts, suggesting a correlation between call rate and accuracy. To calculate a genomic predicted transmitting ability (GPTA) the genotype of an animal tested on a 3K is imputed to the 45,187 SNP included in the current genomic evaluation based on the 50K. The accuracy of imputation increases as the number of genotyped parents increases from none to 1 to both. The average percentage of imputed genotypes that matched the corresponding actual 50K genotypes was 96.3%. The correlation of a GPTA calculated from a 3K genotype that had been imputed to 50K and GPTA from its actual 50K genotype averaged 0.959 across traits for Holsteins and was slightly higher for Jerseys at 0.963. The average difference in GPTA from the 50K- and 3K-based genotypes across trait was close to 0. The evaluation system has been modified to accommodate the characteristics of the 3K. The low cost of the 3K has greatly increased genotyping of females. Prior to the availability of the 3K (August 2010), female genotyping accounted for 38.7% of the genotyped animals. In the past year, the portion of total genotypes from females across all chip types rose to 59.0%.
Journal of Dairy Science 03/2012; 95(3):1552-8. · 2.56 Impact Factor
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ABSTRACT: Genomic evaluations are calculated using deregressed predicted transmitting abilities (PTA) from traditional evaluations to estimate effects of single nucleotide polymorphisms. The direct genomic value (sum of an animal's marker effects) should be consistent with traditional PTA, which is the case for bulls. However, traditional PTA of yield traits (milk, fat, and protein) for genotyped cows are higher than their direct genomic values. To ensure that characteristics of cow PTA for yield traits were more similar to those for bull PTA, mean and variance of cow Mendelian sampling (PTA minus parent average) were adjusted to be similar to those of bulls. The same adjustments were used for all genotyped cows in a breed. To determine gains in reliabilities, predictions were made for bulls with August 2010 evaluations that did not have traditional evaluations in August 2006. By adjusting cow PTA and parent averages of genotyped animals, Holstein and Jersey regressions of August 2010 deregressed PTA on genomic evaluations based on August 2006 data became closer to 1 for the adjusted predictor population compared with the unadjusted predictor population. Evaluation bias was decreased for Holsteins when the predictor population was adjusted. Mean gain in reliability over parent average increased 3.5 percentage points across yield traits for Holsteins and 0.9 percentage points for Jerseys when the predictor population was adjusted. The accuracy of genomic evaluations for Holsteins and Jerseys was increased through better use of information from cows.
Journal of Dairy Science 12/2011; 94(12):6188-93. · 2.56 Impact Factor
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ABSTRACT: Genomic measures of relationship and inbreeding within and across breeds were compared with pedigree measures using genotypes for 43,385 loci of 25,219 Holsteins, 3,068 Jerseys, and 872 Brown Swiss. Adjustment factors allow genomic and pedigree relationships to match more closely within breeds and in multibreed populations and were estimated using means and regressions of genomic on pedigree relationships and allele frequencies in base populations. Correlations of genomic relationships with pedigree inbreeding were higher within each breed when an allele frequency of 0.5, rather than base population frequencies, was used, whereas correlations of average genomic relationships with average pedigree relationships and also reliabilities of genomic evaluations were higher using base population frequencies. Allele frequencies differed in the 3 breeds and were correlated by 0.65 to 0.67 when estimated from genotyped animals compared with 0.72 to 0.74 when estimated from breed base populations. The largest difference in allele frequency was between Holstein and the other breeds on chromosome Bos taurus autosome 4 near a gene affecting appearance of white skin patches (vitiligo) in humans. Each animal's breed composition was predicted very accurately with a standard deviation of <3% using regressions on genotypes at all loci or less accurately with a standard deviation of <6% using subsets of loci. Genomic future inbreeding (half an animal's mean genomic relationship to current animals of the same breed) was correlated by 0.75 to 0.94 with expected future inbreeding (half the average pedigree relationship). Correlations of both were slightly higher with parent averages than with genomic evaluations for net merit of young Holstein bulls. Thus, rates of increase in genomic and pedigree inbreeding per generation should be slightly reduced with genomic selection, in agreement with previous simulations. Genomic inbreeding and future inbreeding have been provided with individual genomic predictions since 2008. New methods to adjust pedigree and genomic relationship matrices so that they match may provide an improved basis for multibreed genomic evaluation. Positive definite matrices can be obtained by adjusting pedigree relationships for covariances among base animals within breed, whereas adjusting genomic relationships to match pedigree relationships can introduce negative eigenvalues. Pedigree relationship matrices ignore common ancestry shared by base animals within breed and may not approximate genomic relationships well in multibreed populations.
Journal of Dairy Science 11/2011; 94(11):5673-82. · 2.56 Impact Factor
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ABSTRACT: Implementation of genomic evaluation has caused profound changes in dairy cattle breeding. All young bulls bought by major artificial insemination organizations now are selected based on such evaluation. Evaluation reliability can reach approximately 75% for yield traits, which is adequate for marketing semen of 2-yr-old bulls. Shortened generation interval from using genomic evaluations is the most important factor in increasing the rate of genetic improvement. Genomic evaluations are based on 42,503 single nucleotide polymorphisms (SNP) genotyped with technology that became available in 2007. The first unofficial USDA genomic evaluations were released in 2008 and became official for Holsteins, Jerseys, and Brown Swiss in 2009. Evaluation accuracy has increased steadily from including additional bulls with genotypes and traditional evaluations (predictor animals). Some of that increase occurs automatically as young genotyped bulls receive a progeny test evaluation at 5 yr of age. Cow contribution to evaluation accuracy is increased by decreasing mean and variance of their evaluations so that they are similar to bull evaluations. Integration of US and Canadian genotype databases was critical to achieving acceptable initial accuracy and continues to benefit both countries. Genotype exchange with other countries added predictor bulls for Brown Swiss. In 2010, a low-density chip with 2,900 SNP and a high-density chip with 777,962 SNP were released. The low-density chip has increased greatly the number of animals genotyped and is expected to replace microsatellites in parentage verification. The high-density chip can increase evaluation accuracy by better tracking of loci responsible for genetic differences. To integrate information from chips of various densities, a method to impute missing genotypes was developed based on splitting each genotype into its maternal and paternal haplotypes and tracing their inheritance through the pedigree. The same method is used to impute genotypes of nongenotyped dams based on genotyped progeny and mates. Reliability of resulting evaluations is discounted to reflect errors inherent in the process. Further increases in evaluation accuracy are expected because of added predictor animals and more SNP. The large population of existing genotypes can be used to evaluate new traits; however, phenotypic observations must be obtained for enough animals to allow estimation of SNP effects with sufficient accuracy for application to the general population.
Journal of Dairy Science 06/2011; 94(6):3202-11. · 2.56 Impact Factor
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ABSTRACT: A national data set of artificial inseminations in US Holsteins was used to obtain genetic evaluations for conception rate (CR). The objective of this study was to investigate the feasibility and resulting accuracy from using all available phenotypic, pedigree, and genomic information. Evaluations were performed by regular BLUP or by BLUP with the traditional pedigree and genomic relationships combined in a unified single-step procedure (SSP). Genetic parameters of CR in the first 3 parities were estimated with data from New York State only. Heritability estimates were around 2% and genetic correlations between CR in different parities were >0.73. The R(2) obtained with the SSP were almost twice as large as those achieved with regular BLUP. Computing the SSP took 2h, and it was 33% slower than a regular BLUP. A multiple-trait evaluation of CR using the SSP is both possible and advantageous.
Journal of Dairy Science 05/2011; 94(5):2621-4. · 2.56 Impact Factor
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ABSTRACT: To facilitate routine genomic evaluation, a database was constructed to store genotypes for 50,972 single nucleotide polymorphisms (SNP) from the Illumina BovineSNP50 BeadChip (Illumina Inc., San Diego, CA). Multiple samples per animal are allowed. All SNP genotypes for a sample are stored in a single row. An indicator specifies whether the genotype for a sample was selected for use in genomic evaluation. Samples with low call rates or pedigree conflicts are designated as unusable. Among multiple samples that qualify for use in genomic evaluation, the one with the highest call rate is designated as usable. When multiple samples are stored for an animal, a composite is formed during extraction by using SNP genotypes from other samples to replace missing genotypes. To increase the number of SNP available, scanner output for approximately 19,000 samples was reprocessed. Any SNP with a minor allele frequency of > or = 1% for Holsteins, Jerseys, or Brown Swiss was selected, which was the primary reason that the number of SNP used for USDA genomic evaluations increased. Few parent-progeny conflicts (< or = 1%) and a high call rate (> or = 90%) were additional requirements that eliminated 2,378 SNP. Because monomorphic SNP did not degrade convergence during estimation of SNP effects, a single set of 43,385 SNP was adopted for all breeds. The use of a database for genotypes, detection of conflicts as genotypes are stored, online access for problem resolution, and use of a single set of SNP for genomic evaluations have simplified tracking of genotypes and genomic evaluation as a routine and official process.
Journal of Dairy Science 05/2010; 93(5):2287-92. · 2.56 Impact Factor
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ABSTRACT: The availability of dense single nucleotide polymorphism (SNP) genotypes for dairy cattle has created exciting research opportunities and revolutionized practical breeding programs. Broader application of this technology will lead to situations in which genotypes from different low-, medium-, or high-density platforms must be combined. In this case, missing SNP genotypes can be imputed using family- or population-based algorithms. Our objective was to evaluate the accuracy of imputation in Jersey cattle, using reference panels comprising 2,542 animals with 43,385 SNP genotypes and study samples of 604 animals for which genotypes were available for 1, 2, 5, 10, 20, 40, or 80% of loci. Two population-based algorithms, fastPHASE 1.2 (P. Scheet and M. Stevens; University of Washington TechTransfer Digital Ventures Program, Seattle, WA) and IMPUTE 2.0 (B. Howie and J. Marchini; Department of Statistics, University of Oxford, UK), were used to impute genotypes on Bos taurus autosomes 1, 15, and 28. The mean proportion of genotypes imputed correctly ranged from 0.659 to 0.801 when 1 to 2% of genotypes were available in the study samples, from 0.733 to 0.964 when 5 to 20% of genotypes were available, and from 0.896 to 0.995 when 40 to 80% of genotypes were available. In the absence of pedigrees or genotypes of close relatives, the accuracy of imputation may be modest (generally <0.80) when low-density platforms with fewer than 1,000 SNP are used, but population-based algorithms can provide reasonably good accuracy (0.80 to 0.95) when medium-density platforms of 2,000 to 4,000 SNP are used in conjunction with high-density genotypes (e.g., >40,000 SNP) from a reference population. Accurate imputation of high-density genotypes from inexpensive low- or medium-density platforms could greatly enhance the efficiency of whole-genome selection programs in dairy cattle.
Journal of Dairy Science 05/2010; 93(5):2229-38. · 2.56 Impact Factor
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ABSTRACT: Nearly 57,000 single-nucleotide polymorphisms (SNP) genotyped with the Illumina BovineSNP50 BeadChip (Illumina Inc., San Diego, CA) were investigated to determine usefulness of the associated SNP for genomic prediction. Genotypes were obtained for 12,591 bulls and cows, and SNP were selected based on 5,503 bulls with genotypes from a larger set of SNP. The following SNP were deleted: 6,572 that were monomorphic, 3,213 with scoring problems (primarily because of poor definition of clusters and excess number of clusters), and 3,649 with a minor allele frequency of <2%. Number of SNP for each minor allele frequency class (> or =2%) was fairly uniform (777 to 1,004). For 5 contiguous SNP assigned to chromosome 7, no bulls were heterozygous, which indicated that those SNP are actually on the nonpseudoautosomal portion of the X chromosome. Another 178 SNP that were not assigned to a chromosome but that had many fewer heterozygotes than expected were also assigned to the X chromosome. Existence of Hardy-Weinberg equilibrium was investigated by comparing observed with expected heterozygosity. For 11 SNP, the observed percentage of heterozygous individuals differed from the expected by >15%; therefore, those SNP were deleted. For 2,628 SNP, the genotype at another SNP was highly correlated (i.e., genotypes were identical for >99.5% of bulls), and those were deleted. After edits, 40,874 SNP remained. A parent-progeny conflict was declared when the genotypes were alternate homozygotes. Mean number of conflicts was 2.3 when pedigree was correct and 2,411 when it was incorrect. The sire was genotyped for >93% of animals. Maternal grandsire genotype was similarly checked; however, because alternate homozygotes could be valid, a conflict threshold of 16% was used to indicate a need for further investigation. Genotyping consistency was investigated for 21 bulls genotyped twice with differences primarily from SNP that were not scored in one of the genotypes. Concordance for readable SNP was extremely high (99.96-100%). Thousands of SNP that were polymorphic in Holsteins were monomorphic in Jerseys or Brown Swiss, which indicated that breed-specific SNP sets are required or that all breeds need to be considered in the SNP selection process. Genotypes from the Illumina BovineSNP50 BeadChip are of high accuracy and provide the basis for genomic evaluations in the United States and Canada.
Journal of Dairy Science 08/2009; 92(7):3431-6. · 2.56 Impact Factor
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ABSTRACT: Genetic effects for many dairy traits and for total economic merit are evenly distributed across all chromosomes. A high-density scan using 38,416 single nucleotide polymorphism markers for 5,285 bulls confirmed 2 previously known major genes on Bos taurus autosomes (BTA) 6 and 14 but revealed few other large effects. Markers on BTA18 had the largest effects on calving ease, several conformation traits, longevity, and total merit. Prediction accuracy was highest using a heavy-tailed prior assuming that each marker had an effect on each trait, rather than assuming a normal distribution of effects as in a linear model, or that only some loci have nonzero effects. A prior model combining heavy tails with finite alleles produced results that were intermediate compared with the individual models. Differences between models were small (1 to 2%) for traits with no major genes and larger for heavy tails with traits having known quantitative trait loci (QTL; 6 to 8%). Analysis of bull recessive codes suggested that marker effects from genomic selection may be used to identify regions of chromosomes to search in detail for candidate genes, but individual single nucleotide polymorphisms were not tracking causative mutations with the exception of diacylglycerol O-acyltransferase 1. Additive genetic merits were constructed for each chromosome, and the distribution of BTA14-specific estimated breeding value (EBV) showed that selection primarily for milk yield has not changed the distribution of EBV for fat percentage even in the presence of a known QTL. Such chromosomal EBV also may be useful for identifying complementary mates in breeding programs. The QTL affecting dystocia, conformation, and economic merit on BTA18 appear to be related to calf size or birth weight and may be the result of longer gestation lengths. Results validate quantitative genetic assumptions that most traits are due to the contributions of a large number of genes of small additive effect, rather than support the finite locus model.
Journal of Dairy Science 07/2009; 92(6):2931-46. · 2.56 Impact Factor
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ABSTRACT: Genetic progress will increase when breeders examine genotypes in addition to pedigrees and phenotypes. Genotypes for 38,416 markers and August 2003 genetic evaluations for 3,576 Holstein bulls born before 1999 were used to predict January 2008 daughter deviations for 1,759 bulls born from 1999 through 2002. Genotypes were generated using the Illumina BovineSNP50 BeadChip and DNA from semen contributed by US and Canadian artificial-insemination organizations to the Cooperative Dairy DNA Repository. Genomic predictions for 5 yield traits, 5 fitness traits, 16 conformation traits, and net merit were computed using a linear model with an assumed normal distribution for marker effects and also using a nonlinear model with a heavier tailed prior distribution to account for major genes. The official parent average from 2003 and a 2003 parent average computed from only the subset of genotyped ancestors were combined with genomic predictions using a selection index. Combined predictions were more accurate than official parent averages for all 27 traits. The coefficients of determination (R(2)) were 0.05 to 0.38 greater with nonlinear genomic predictions included compared with those from parent average alone. Linear genomic predictions had R(2) values similar to those from nonlinear predictions but averaged just 0.01 lower. The greatest benefits of genomic prediction were for fat percentage because of a known gene with a large effect. The R(2) values were converted to realized reliabilities by dividing by mean reliability of 2008 daughter deviations and then adding the difference between published and observed reliabilities of 2003 parent averages. When averaged across all traits, combined genomic predictions had realized reliabilities that were 23% greater than reliabilities of parent averages (50 vs. 27%), and gains in information were equivalent to 11 additional daughter records. Reliability increased more by doubling the number of bulls genotyped than the number of markers genotyped. Genomic prediction improves reliability by tracing the inheritance of genes even with small effects.
Journal of Dairy Science 02/2009; 92(1):16-24. · 2.56 Impact Factor
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ABSTRACT: A method used to approximate reliabilities for animal models was modified to estimate reliabilities for sire-maternal grandsire (MGS) models. Accuracy of the approximation was tested on a calving-ease data set for 2,968 bulls for which the inverse of the coefficient matrix could be obtained. Correlations between estimated and true reliabilities ranged from 0.984 to 0.998 for first- and later-parity calving ease for sire and MGS effects. With no modification of the animal-model procedure, MGS identification was treated as if it were dam identification, which resulted in overestimated reliability. When pedigree information was ignored, reliability was underestimated. Correlations with true values were lower for both of those cases when compared with correct processing of MGS information. The modification provided a slight improvement over assuming MGS to be unknown and will be used for routine USDA evaluation of calving traits.
Journal of Dairy Science 11/2008; 91(10):4058-61. · 2.56 Impact Factor
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ABSTRACT: Evaluations that analyze first and later parities as correlated traits were developed separately for calving ease (CE) from over 15 million calving records of Holsteins, Brown Swiss, and Holstein-Brown Swiss crossbreds and for stillbirth (SB) from 7.4 million of the Holstein CE records. Calving ease was measured on a scale of 1 (no difficulty) to 5 (difficult birth); SB status was designated as live or dead within 48 h. Scores for CE and SB were transformed separately for each trait by parity (first or later) and calf sex (male or female) and converted to a unit standard deviation scale. For variance component estimation, Holstein data were selected for the 2,968 bulls with the most records as sire or maternal grandsire (MGS). Six samples were selected by herd; samples ranged in size from 97,756 to 146,138 records. A multiparity sire-MGS model was used to calculate evaluations separately for CE and for SB with first and later parities as correlated traits. Fixed effects were year-season, calf sex, and sire and MGS birth years; random effects were herd-year interaction, sire, and MGS. For later parities, sex effects were separated by parity. The genetic correlation between first and later parities was 0.79 for sire and 0.81 for MGS for CE, and 0.83 for sire and 0.74 for MGS for SB. For national CE evaluations, which also include Brown Swiss, a fixed effect for breed was added to the model. Correlations between solutions on the underlying scale from the January 2008 USDA CE evaluation with those from the multiparity analysis for CE were 0.89 and 0.91 for first- and later-parity sire effects and 0.71 and 0.88 for first- and later-parity MGS effects; the larger value for later parity reflects that later parities comprised 64% of the data. Corresponding correlations for SB were 0.81 and 0.82 for first- and later-parity sire effects and 0.46 and 0.83 for first- and later-parity MGS effects, respectively. Correlations were higher when only bulls with a multiparity reliability of >65% were included. The multiparity analysis accounted for genetic differences in calving performance between first and later parities. Evaluations should become more stable as the portion of a bull's observations from different parities changes over his lifetime. Accuracy of the net merit index can be improved by adjusting weights to use evaluations for separate parities optimally.
Journal of Dairy Science 08/2008; 91(8):3173-8. · 2.56 Impact Factor
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ABSTRACT: A sire-maternal grandsire threshold model was used for genetic evaluation of stillbirth in US Holsteins. Calving ease and stillbirth records for herds reporting at least 10 dead calves were extracted from the Animal Improvement Programs Laboratory database. About half of the 14 million calving ease records in the database had a known livability score, mostly from herds processed by Dairy Records Management Systems (Raleigh, NC). Calf livability scores of 2 and 3, representing calves born dead and calves that died within 48 h of parturition, respectively, were combined into a single category. The model included effects of herd-year, year-season, parity-sex, sire, birth year group of sire, maternal grandsire (MGS), and birth year group of MGS. Herd-year, sire, and MGS were random effects. Mean predicted transmitting abilities, expressed as the expected percentage of stillbirths, were 7.9 and 8.6 for direct and maternal stillbirths, respectively. Mean reliabilities for both the direct and maternal effects were 45%. Correlations among domestic and Interbull stillbirth solutions on the underlying scale for bulls with at least 90% reliability ranged from 0.63 to 0.90 across countries for direct stillbirths and from 0.69 to 0.96 for maternal stillbirths, indicating that results were generally consistent with those from other countries. There was no evidence of a genetic trend for either trait. More complete recording of stillbirth scores would improve reliabilities and could allow for evaluations of other breeds.
Journal of Dairy Science 06/2007; 90(5):2480-8. · 2.56 Impact Factor
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ABSTRACT: (Co)variance components for stillbirth in US Holsteins were estimated under a sire-maternal grandsire threshold model using subsets of data from the national calving ease database, which includes over 6 million calving records with associated stillbirth scores. Stillbirth was coded as a binomial trait indicating whether the calf was alive 48 h postpartum. Records were selected for calves whose sire and maternal grandsire (MGS) were among the 2,600 most frequently appearing bulls (2,578 sires and 2,586 MGS). Herd-years were required to contain at least 20 records and only single births were used. After editing, the data set included 2,083,979 calving records from 5,765 herds and 33,304 herd-years. Six sample datasets of approximately 250,000 records each were created by randomly selecting herd codes. Quasi-REML and Bayesian approaches were used to estimate (co)variance components from each sample. The model included fixed year-season, parity-sex, birth year group of sire, and birth year group of MGS effects and random herd-year, sire, MGS, and residual effects. Quasi-REML and Bayesian analyses produced similar results, although the Bayesian estimates were slightly larger. Marginal posterior means (and standard deviations) from the Bayesian analysis averaged 0.0085 (0.0015), 0.0181 (0.0020), 0.0872 (0.0538), and 0.00410 (0.0001) for sire, MGS, and herd-year variances and the sire-MGS covariance, respectively. Mean direct and maternal heritabilities were 0.030 (0.003) and 0.058 (0.005), respectively, and the mean genetic correlation between the 2 effects was -0.02 (0.16). A calving ability index combining stillbirth (SB) and calving ease (CE) was developed for inclusion in the Lifetime Net Merit index. The index was calculated as -4(sire CE)-3(daughter CE)-4(sire SB) -8(daughter SB).
Journal of Dairy Science 06/2007; 90(5):2489-96. · 2.56 Impact Factor
