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Journal ArticleDOI

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.
Citations
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Journal ArticleDOI
TL;DR: The aim of this paper was to review on the application of genomics in animal production for different characteristics associated with both milk and meat production and the current availability of tools of molecular genetics and genomics to predict more precision breeding values of animals from birth, decreasing the generation interval and increasing the intensity of selection.
Abstract: Genetic improvement evaluates and uses genetic variation to maintain and improve quality and quantity of animal production. The identification of high genetic merit animals is complicated by the fact that most traits of economic importance, being quantitative in nature, has controlled continuous variations which several genes interact with the environment. The aim of this paper was to review on the application of genomics in animal production for different characteristics associated with both milk and meat production and the current availability of tools of molecular genetics and genomics to predict more precision breeding values of animals from birth, decreasing the generation interval and increasing the intensity of selection.

5 citations

Journal ArticleDOI
TL;DR: Regions identified in the genome were in the proximity of previously described quantitative trait loci that would most likely affect calving difficulty by altering the feto-pelvic proportion and did not outperform regular GBLUP models.
Abstract: Calving difficulty or dystocia has a great economic impact in the US dairy industry. Reported risk factors associated with calving difficulty are feto-pelvic disproportion, gestation length and conformation. Different dairy cattle breeds have different incidence of calving difficulty, with Holstein having the highest dystocia rates and Jersey the lowest. Genomic selection becomes important especially for complex traits with low heritability, where the accuracy of conventional selection is lower. However, for complex traits where a large number of genes influence the phenotype, genome-wide association studies showed limitations. Biological networks could overcome some of these limitations and better capture the genetic architecture of complex traits. In this paper, we characterize Holstein, Brown Swiss and Jersey breed-specific dystocia networks and employ them in genomic predictions. Marker association analysis identified single nucleotide polymorphisms explaining the largest average proportion of genetic variance on BTA18 in Holstein, BTA25 in Brown Swiss, and BTA15 in Jersey. Gene networks derived from the genome-wide association included 1272 genes in Holstein, 1454 genes in Brown Swiss, and 1455 genes in Jersey. Furthermore, 256 genes in Holstein network, 275 genes in the Brown Swiss network, and 253 genes in the Jersey network were within previously reported dystocia quantitative trait loci. The across-breed network included 80 genes, with 9 genes being within previously reported dystocia quantitative trait loci. The gene-gene interactions in this network differed in the different breeds. Gene ontology enrichment analysis of genes in the networks showed Regulation of ARF GTPase was very significant (FDR ≤ 0.0098) on Holstein. Neuron morphogenesis and differentiation was the term most enriched (FDR ≤ 0.0539) on the across-breed network. Genomic prediction models enriched with network-derived relationship matrices did not outperform regular GBLUP models. Regions identified in the genome were in the proximity of previously described quantitative trait loci that would most likely affect calving difficulty by altering the feto-pelvic proportion. Inclusion of identified networks did not increase prediction accuracy. The approach used in this paper could be extended to any instance with asymmetric distribution of phenotypes, for example, resistance to disease data.

5 citations

01 Jan 2017
TL;DR: A genome wide association study was conducted to identify regions of the genome associated with milk production trait in Iranian water buffaloes, and the most important gene was SCOS2 gene on chromosome BTA5.
Abstract: A genome wide association study (GWAS) was conducted to identify regions of the genome associated with milk production trait in Iranian water buffaloes. In order to do this study, 303 water buffalo from Azeri (N=207), Khuzestani (N=96) breeds were genotyped with Axiom® Buffalo Genotyping 90K Array. Average of monthly recording of one lactation period were used as phenotypic data for each individual. After adjustments of records for fixed effects, phenotypes were regressed on each single nucleotide polymorphisms (SNP) using a linear regression model by GenABEL package in R. In total, 10 SNPs were identified on BTAs 4, 5, 7, 13, 14, 15 and X as probably associated regions with milk production. These selected SNPs with 400 Kbp neighboring genomic regions were surveyed to find probable candidate genes by annotation. In total, 11 genes identified from the annotation of selected SNPs on the cattle genome (UMD3.1 Bos Taurus). Among the candidate genes identified in these regions, the most important gene that associated with milk production was SCOS2 gene on chromosome BTA5. Further research with additional individual and records is essential to detect associated regions with milk production and to suggestion for commercial application to the genetic improvement

5 citations

Journal ArticleDOI
12 Jun 2020-Heredity
TL;DR: This study presents a novel approach where mortality risk probabilities from polygenic and lethal allele components are predicted separately to compute the total risk probability of an individual for its future offspring as a basis for selection and indicates that this approach can greatly increase the accuracy of selection for mortality traits.
Abstract: The genetic underpinnings of calf mortality can be partly polygenic and partly due to deleterious effects of recessive lethal alleles. Prediction of the genetic merits of selection candidates should thus take into account both genetic components contributing to calf mortality. However, simultaneously modeling polygenic risk and recessive lethal allele effects in genomic prediction is challenging due to effects that behave differently. In this study, we present a novel approach where mortality risk probabilities from polygenic and lethal allele components are predicted separately to compute the total risk probability of an individual for its future offspring as a basis for selection. We present methods for transforming genomic estimated breeding values of polygenic effect into risk probabilities using normal density and cumulative distribution functions and show computations of risk probability from recessive lethal alleles given sire genotypes and population recessive allele frequencies. Simulated data were used to test the novel approach as implemented in probit, logit, and linear models. In the simulation study, the accuracy of predicted risk probabilities was computed as the correlation between predicted mortality probabilities and observed calf mortality for validation sires. The results indicate that our novel approach can greatly increase the accuracy of selection for mortality traits compared with the accuracy of predictions obtained without distinguishing polygenic and lethal gene effects.

5 citations

Book ChapterDOI
TL;DR: Beyond calculating parameter estimates to characterize the distribution of genetic features of populations, proper statistical analyses of human data must incorporate epidemiologic approaches to examining sets of families or unrelated individuals with information available on individuals' disease status or related traits.
Abstract: Beyond calculating parameter estimates to characterize the distribution of genetic features of populations (frequencies of mutations in various regions of the genome, allele frequencies, measures of Hardy-Weinberg disequilibrium), genetic epidemiology aims to identify correlations between genetic variants and phenotypic traits, with considerable emphasis placed on finding genetic variants that increase susceptibility to disease and disease-related traits. However, determining correlation alone does not suffice: genetic variants common in an isolated ethnic group with a high burden of a given disease may show relatively high correlation with disease but, as markers of ethnicity, these may not necessarily have any functional role in disease. To establish a causal relationship between genetic variants and disease (or disease-related traits), proper statistical analyses of human data must incorporate epidemiologic approaches to examining sets of families or unrelated individuals with information available on individuals' disease status or related traits.Through different analytical approaches, statistical analysis of human data can answer several important questions about the relationship between genes and disease: 1. Does the disease tend to cluster in families more than expected by chance alone? 2. Does the disease appear to follow a particular genetic model of transmission in families? 3. Do variants at a particular genetic marker tend to cosegregate with disease in families? 4. Do specific genetic markers tend to be carried more frequently by those with disease than by those without, in a given population (or across families)? The first question can be examined using studies of familial aggregation or correlation. An ancillary question: "how much of the susceptibility to disease (or variation in disease-related traits) might be accounted for by genetic factors?" is typically answered by estimating heritability, the proportion of disease susceptibility or trait variation attributable to genetics. The second question can be formally tested using pedigrees for which disease affection status or trait values are available through a modeling approach known as segregation analysis. The third question can be answered with data on pedigrees with affected members and genotype information at markers of interest, using linkage analysis. The fourth question is answerable using genotype information at markers on unrelated affected and unaffected individuals and/or families with affected and unaffected members. All of these questions can also be explored for quantitative (or continuously distributed) traits by examining variation in trait values between family members or between unrelated individuals. While each of these questions and the analytical approaches for answering them is explored extensively in subsequent chapters (heritability in Chapters 9 and 10, segregation in Chapter 12, linkage in Chapters 13-17, and association in Chapters 18-21 and 23), this chapter focuses on statistical methods to answer questions of familial aggregation.

5 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

Journal ArticleDOI
01 Apr 2001-Genetics
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

Journal ArticleDOI
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

Journal ArticleDOI
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