Distribution and location of genetic effects for dairy traits
TL;DR: 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 as discussed by the authors.
About: This article is published in Journal of Dairy Science.The article was published on 2009-06-01 and is currently open access. It has received 224 citations till now. The article focuses on the topics: Quantitative trait locus & Allele.
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TL;DR: For yield traits, including additive and dominance effects fit the data better than including only additive effects; average correlations between estimated genetic effects and phenotypes showed that prediction accuracy increased when both effects rather than just additive effects were included.
Abstract: Dominance may be an important source of non-additive genetic variance for many traits of dairy cattle. However, nearly all prediction models for dairy cattle have included only additive effects because of the limited number of cows with both genotypes and phenotypes. The role of dominance in the Holstein and Jersey breeds was investigated for eight traits: milk, fat, and protein yields; productive life; daughter pregnancy rate; somatic cell score; fat percent and protein percent. Additive and dominance variance components were estimated and then used to estimate additive and dominance effects of single nucleotide polymorphisms (SNPs). The predictive abilities of three models with both additive and dominance effects and a model with additive effects only were assessed using ten-fold cross-validation. One procedure estimated dominance values, and another estimated dominance deviations; calculation of the dominance relationship matrix was different for the two methods. The third approach enlarged the dataset by including cows with genotype probabilities derived using genotyped ancestors. For yield traits, dominance variance accounted for 5 and 7% of total variance for Holsteins and Jerseys, respectively; using dominance deviations resulted in smaller dominance and larger additive variance estimates. For non-yield traits, dominance variances were very small for both breeds. For yield traits, including additive and dominance effects fit the data better than including only additive effects; average correlations between estimated genetic effects and phenotypes showed that prediction accuracy increased when both effects rather than just additive effects were included. No corresponding gains in prediction ability were found for non-yield traits. Including cows with derived genotype probabilities from genotyped ancestors did not improve prediction accuracy. The largest additive effects were located on chromosome 14 near DGAT1 for yield traits for both breeds; those SNPs also showed the largest dominance effects for fat yield (both breeds) as well as for Holstein milk yield.
88 citations
TL;DR: The results indicate that posterior SNP variance estimated from a Bayesian mixture model is a good alternative weighting factor, and common weights on group markers with a size of 30 markers is aGood strategy when using markers of the 50,000-marker (50K) chip.
88 citations
TL;DR: The aims of this review are to describe the principal effects of natural/artificial selection on livestock genomes, to present the main methods used to detect selection signatures and to discuss some recent results in this area.
Abstract: The identification of regions that have undergone selection is one of the principal goals of theoretical and applied evolutionary genetics. Such studies can also provide information about the evolutionary processes involved in shaping genomes, as well as physical and functional information about genes/genomic regions. Domestication followed by breed formation and selection schemes has allowed the formation of very diverse livestock breeds adapted to a wide variety of environments and with special characteristics. The advances in genomics in the last five years have enabled the development of several methods to detect selection signatures and have resulted in the publication of a considerable number of studies involving livestock species. The aims of this review are to describe the principal effects of natural/artificial selection on livestock genomes, to present the main methods used to detect selection signatures and to discuss some recent results in this area. This review should be useful also to research scientists working with wild animals/non-domesticated species and plant biologists working with breeding and evolutionary biology.
87 citations
TL;DR: An overview of the current understanding of the role of maternal genetics and modifiable factors, such as diet and environmental exposures, on reproductive endocrinology, lactation physiology, and the ability to successfully produce milk is provided.
Abstract: Lactation is a dynamic process that has evolved to produce a complex biological fluid that provides nutritive and nonnutritive factors to the nursing offspring. It has long been assumed that once lactation is successfully initiated, the primary factor regulating milk production is infant demand. Thus, most interventions have focused on improving breastfeeding education and early lactation support. However, in addition to infant demand, increasing evidence from studies conducted in experimental animal models, production animals, and breastfeeding women suggests that a diverse array of maternal factors may also affect milk production and composition. In this review, we provide an overview of our current understanding of the role of maternal genetics and modifiable factors, such as diet and environmental exposures, on reproductive endocrinology, lactation physiology, and the ability to successfully produce milk. To identify factors that may affect lactation in women, we highlight some information gleaned from studies in experimental animal models and production animals. Finally, we highlight the gaps in current knowledge and provide commentary on future research opportunities aimed at improving lactation outcomes in breastfeeding women to improve the health of mothers and their infants.
86 citations
TL;DR: Expansion of HWE and PennCNV analyses allowed estimating LOH/CNV frequencies, and combining the two methods yielded more sensitive detection of inherited CNVs and better estimation of their possible effects on cattle genetics.
Abstract: Copy number variation (CNV) has been recently identified in human and other mammalian genomes, and there is a growing awareness of CNV's potential as a major source for heritable variation in complex traits. Genomic selection is a newly developed tool based on the estimation of breeding values for quantitative traits through the use of genome-wide genotyping of SNPs. Over 30,000 Holstein bulls have been genotyped with the Illumina BovineSNP50 BeadChip, which includes 54,001 SNPs (~SNP/50,000 bp), some of which fall within CNV regions. We used the BeadChip data obtained for 912 Israeli bulls to investigate the effects of CNV on SNP calls. For each of the SNPs, we estimated the frequencies of occurrence of loss of heterozygosity (LOH) and of gain, based either on deviation from the expected Hardy-Weinberg equilibrium (HWE) or on signal intensity (SI) using the PennCNV "detect" option. Correlations between LOH/CNV frequencies predicted by the two methods were low (up to r = 0.08). Nevertheless, 418 locations displayed significantly high frequencies by both methods. Efficiency of designating large genomic clusters of olfactory receptors as CNVs was 29%. Frequency values for copy loss were distinguishable in non-autosomal regions, indicating misplacement of a region in the current BTA7 map. Analysis of BTA18 placed major quantitative trait loci affecting net merit in the US Holstein population in regions rich in segmental duplications and CNVs. Enrichment of transporters in CNV loci suggested their potential effect on milk-production traits. Expansion of HWE and PennCNV analyses allowed estimating LOH/CNV frequencies, and combining the two methods yielded more sensitive detection of inherited CNVs and better estimation of their possible effects on cattle genetics. Although this approach was more effective than methodologies previously applied in cattle, it has severe limitations. Thus the number of CNVs reported here for the Holstein breed may represent as little as one-tenth of inherited common structural variation.
84 citations
References
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Book•
01 Jan 1981
TL;DR: The genetic constitution of a population: Hardy-Weinberg equilibrium and changes in gene frequency: migration mutation, changes of variance, and heritability are studied.
Abstract: Part 1 Genetic constitution of a population: Hardy-Weinberg equilibrium. Part 2 Changes in gene frequency: migration mutation. Part 3 Small populations - changes in gene frequency under simplified conditions. Part 4 Small populations - less simplified conditions. Part 5 Small populations - pedigreed populations and close inbreeding. Part 6 Continuous variation. Part 7 Values and means. Part 8 Variance. Part 9 Resemblance between relatives. Part 10 Heritability. Part 11 Selection - the response and its prediction. Part 12 Selection - the results of experiments. Part 13 Selection - information from relatives. Part 14 Inbreeding and crossbreeding - changes of mean value. Part 15 Inbreeding and crossbreeding - changes of variance. Part 16 Inbreeding and crossbreeding - applications. Part 17 Scale. Part 18 Threshold characters. Part 19 Correlated characters. Part 20 Metric characters under natural selection.
20,288 citations
TL;DR: It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.
Abstract: Recent advances in molecular genetic techniques will make dense marker maps available and genotyping many individuals for these markers feasible. Here we attempted to estimate the effects of ∼50,000 marker haplotypes simultaneously from a limited number of phenotypic records. A genome of 1000 cM was simulated with a marker spacing of 1 cM. The markers surrounding every 1-cM region were combined into marker haplotypes. Due to finite population size (Ne = 100), the marker haplotypes were in linkage disequilibrium with the QTL located between the markers. Using least squares, all haplotype effects could not be estimated simultaneously. When only the biggest effects were included, they were overestimated and the accuracy of predicting genetic values of the offspring of the recorded animals was only 0.32. Best linear unbiased prediction of haplotype effects assumed equal variances associated to each 1-cM chromosomal segment, which yielded an accuracy of 0.73, although this assumption was far from true. Bayesian methods that assumed a prior distribution of the variance associated with each chromosome segment increased this accuracy to 0.85, even when the prior was not correct. It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.
6,036 citations
TL;DR: Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and to estimate thousands of marker effects simultaneously, and a blend of first- and second-order Jacobi iteration using 2 separate relaxation factors converged well for allele frequencies and effects.
4,196 citations
TL;DR: 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 and genomic prediction improves reliability by tracing the inheritance of genes even with small effects.
1,166 citations