The prediction of single nucleotide polymorphisms and their utilization in mapping traits and determining population structure in production animals

Gorbach, Danielle M.

doi:10.31274/etd-180810-322

Cited by 2 publications

(3 citation statements)

References 219 publications

(222 reference statements)

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“…For instance, ERO1LB and ARID4B have been detected to be associated with residual feed intake in swines (Gorbach 2011). ARID4B encodes a subunit of the histone deacetylase-dependant SIN3A transcriptional corepressor complex, which functions in various cellular processes including proliferation, differentiation, apoptosis, oncogenesis, and cell fate determination (Wu et al 2006; Winter et al 2012).…”

Section: Discussionmentioning

confidence: 99%

Genome-Wide Patterns of Genetic Variation in Two Domestic Chickens

Fan

Chen

et al. 2013

Genome Biology and Evolution

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Domestic chickens are excellent models for investigating the genetic basis of phenotypic diversity, as numerous phenotypic changes in physiology, morphology, and behavior in chickens have been artificially selected. Genomic study is required to study genome-wide patterns of DNA variation for dissecting the genetic basis of phenotypic traits. We sequenced the genomes of the Silkie and the Taiwanese native chicken L2 at ∼23- and 25-fold average coverage depth, respectively, using Illumina sequencing. The reads were mapped onto the chicken reference genome (including 5.1% Ns) to 92.32% genome coverage for the two breeds. Using a stringent filter, we identified ∼7.6 million single-nucleotide polymorphisms (SNPs) and 8,839 copy number variations (CNVs) in the mapped regions; 42% of the SNPs have not found in other chickens before. Among the 68,906 SNPs annotated in the chicken sequence assembly, 27,852 were nonsynonymous SNPs located in 13,537 genes. We also identified hundreds of shared and divergent structural and copy number variants in intronic and intergenic regions and in coding regions in the two breeds. Functional enrichments of identified genetic variants were discussed. Radical nsSNP-containing immunity genes were enriched in the QTL regions associated with some economic traits for both breeds. Moreover, genetic changes involved in selective sweeps were detected. From the selective sweeps identified in our two breeds, several genes associated with growth, appetite, and metabolic regulation were identified. Our study provides a framework for genetic and genomic research of domestic chickens and facilitates the domestic chicken as an avian model for genomic, biomedical, and evolutionary studies.

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Section: Discussionmentioning

confidence: 99%

Genome-Wide Patterns of Genetic Variation in Two Domestic Chickens

Fan

Chen

et al. 2013

Genome Biology and Evolution

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“…This is, however, hampered by the typically limited power of GWA, which leads to many false negatives and, as a result, limited overlap between significant windows for two traits that may in fact have a high genetic correlation. In a more direct approach, Gorbach [64] used correlations and covariances of window GEBV of individuals from univariate genomic prediction analyses of two traits to identify pleiotropic regions. Applied to growth rate and feed intake in pigs, they identified several regions for which the correlation between window GEBV for these two traits was opposite to the expected undesirable positive genetic correlation between these two traits (i.e.…”

Section: Bayesian Gwa Models To Detect Pleiotropic Qtlmentioning

confidence: 99%

“…Using a similar approach, Bolormaa et al [65] used the covariance between window GEBV for two traits divided by the product of the phenotypic standard deviation for each trait. Such a criterion is preferred over the correlation between window GEBV used by Gorbach [64] because the latter could be high for windows that explain very little genetic variance for one or both traits and which, therefore, contribute little to the genome-wide genetic correlation between the traits. In general, a problem with these window GEBV approaches is that predictions of breeding values are affected by both genetic and random environmental effects (they are a linear function of phenotypes) and, therefore, their correlations and covariances are not proper estimates of genetic correlations or covariances.…”

Section: Bayesian Gwa Models To Detect Pleiotropic Qtlmentioning

confidence: 99%

Application of Bayesian genomic prediction methods to genome-wide association analyses

Wolc

Dekkers

2022

Genet Sel Evol

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Background Bayesian genomic prediction methods were developed to simultaneously fit all genotyped markers to a set of available phenotypes for prediction of breeding values for quantitative traits, allowing for differences in the genetic architecture (distribution of marker effects) of traits. These methods also provide a flexible and reliable framework for genome-wide association (GWA) studies. The objective here was to review developments in Bayesian hierarchical and variable selection models for GWA analyses. Results By fitting all genotyped markers simultaneously, Bayesian GWA methods implicitly account for population structure and the multiple-testing problem of classical single-marker GWA. Implemented using Markov chain Monte Carlo methods, Bayesian GWA methods allow for control of error rates using probabilities obtained from posterior distributions. Power of GWA studies using Bayesian methods can be enhanced by using informative priors based on previous association studies, gene expression analyses, or functional annotation information. Applied to multiple traits, Bayesian GWA analyses can give insight into pleiotropic effects by multi-trait, structural equation, or graphical models. Bayesian methods can also be used to combine genomic, transcriptomic, proteomic, and other -omics data to infer causal genotype to phenotype relationships and to suggest external interventions that can improve performance. Conclusions Bayesian hierarchical and variable selection methods provide a unified and powerful framework for genomic prediction, GWA, integration of prior information, and integration of information from other -omics platforms to identify causal mutations for complex quantitative traits.

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The prediction of single nucleotide polymorphisms and their utilization in mapping traits and determining population structure in production animals

Cited by 2 publications

References 219 publications

Genome-Wide Patterns of Genetic Variation in Two Domestic Chickens

Genome-Wide Patterns of Genetic Variation in Two Domestic Chickens

Application of Bayesian genomic prediction methods to genome-wide association analyses

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