Efficiency of genomic selection using Bayesian multi-marker models for traits selected to reflect a wide range of heritabilities and frequencies of detected quantitative traits loci in mice

Kapell, D. N. R. G.; Sörensen, Daniel; Su, Guosheng; Janss, Luc; Ashworth, Cheryl; Roehe, R.

doi:10.1186/1471-2156-13-42

Cited by 10 publications

(12 citation statements)

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“…Genomic information alone (SNP model) explained less variance, i.e., 36%, 28%, and 24% for BW, FI, and FE respectively. It is a common finding that SNPs explain less variance than the classical heritability estimates [27,28], which is attributed to causal variants having lower minor allele frequency than the genotyped SNPs [15], insufficient modeling of Identity By Descent by SNPs [16], and incomplete linkage disequilibrium (LD) between causal variants and genotyped SNPs [15]. Combining pedigree and SNP data (PED + SNP model) increased the explained variance above that of using pedigree only, i.e., for BW the PED + SNP model obtained an explained variance of 59%, compared to 42% for the PED model.…”

Section: Resultsmentioning

confidence: 99%

Inferring genetic architecture of complex traits using Bayesian integrative analysis of genome and transcriptome data

et al. 2012

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BackgroundTo understand the genetic architecture of complex traits and bridge the genotype-phenotype gap, it is useful to study intermediate -omics data, e.g. the transcriptome. The present study introduces a method for simultaneous quantification of the contributions from single nucleotide polymorphisms (SNPs) and transcript abundances in explaining phenotypic variance, using Bayesian whole-omics models. Bayesian mixed models and variable selection models were used and, based on parameter samples from the model posterior distributions, explained variances were further partitioned at the level of chromosomes and genome segments.ResultsWe analyzed three growth-related traits: Body Weight (BW), Feed Intake (FI), and Feed Efficiency (FE), in an F2 population of 440 mice. The genomic variation was covered by 1806 tag SNPs, and transcript abundances were available from 23,698 probes measured in the liver. Explained variances were computed for models using pedigree, SNPs, transcripts, and combinations of these. Comparison of these models showed that for BW, a large part of the variation explained by SNPs could be covered by the liver transcript abundances; this was less true for FI and FE. For BW, the main quantitative trait loci (QTLs) are found on chromosomes 1, 2, 9, 10, and 11, and the QTLs on 1, 9, and 10 appear to be expression Quantitative Trait Locus (eQTLs) affecting gene expression in the liver. Chromosome 9 is the case of an apparent eQTL, showing that genomic variance disappears, and that a tri-modal distribution of genomic values collapses, when gene expressions are added to the model.ConclusionsWith increased availability of various -omics data, integrative approaches are promising tools for understanding the genetic architecture of complex traits. Partitioning of explained variances at the chromosome and genome-segment level clearly separated regulatory and structural genomic variation as the areas where SNP effects disappeared/remained after adding transcripts to the model. The models that include transcripts explained more phenotypic variance and were better at predicting phenotypes than a model using SNPs alone. The predictions from these Bayesian models are generally unbiased, validating the estimates of explained variances.

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Section: Resultsmentioning

confidence: 99%

Inferring genetic architecture of complex traits using Bayesian integrative analysis of genome and transcriptome data

et al. 2012

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“…Overall, the low and moderate heritability estimates of increment traits indicate that it is a big challenge to achieve our expected goals by traditional breeding methods. Some approaches such as marker-assisted selection may be a good alternative for selection of these traits, especially early increment traits (Kapell et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

Genetic analysis for dynamic changes of egg weight in 2 chicken lines

Liu

et al. 2014

Poultry Science

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One of the main concerns for poultry producers is how to maintain egg uniformity and stability in size and weight following the rapid growth during the early laying period. In this study, we aimed to investigate the increase in egg weight with advancing hen age, and to estimate genetic parameters of these increment traits in 2 pure lines of chickens (i.e., 2,010 White Leghorn and 1,200 brown-egg dwarf hens), using the restricted maximum likelihood method with the DMU procedure. We collected age at first egg (AFE), first egg weight (FEW) and kept records of egg weight per 10 wk from 30 to 60 wk of age. Meanwhile, the increments of egg weight were calculated for the evaluation of age-dependent dynamic changes. The increment of egg weight gained dramatically before 30 wk of age and became slower with the advance of age. Heritability estimates of AFE were larger than 0.32, and the low to moderate genetic correlations between AFE and FEW were observed in the 2 lines. The FEW showed high variation level compared with egg weights at later ages in the 2 lines, and had moderate heritability estimates in White Leghorns (0.20) and dwarf hens (0.33). Egg weights at different ages were highly heritable in the 2 lines (h(2) ≥ 0.35), and had strong genetic and phenotypic correlations among different ages. The estimates of heritability for most increment traits were low to moderate, especially those increments for 10-wk intervals ranging from 0.00 to 0.14. The genetic correlations among 3 consecutive egg weight increments for 10-wk intervals were low to moderate. Our results in the 2 lines should provide important insights into the genetic architecture of increment traits and offer some suggestions for producing uniform and stable eggs in response to advancing age.

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“…Genomic estimated breeding values (GEBV) were estimated using a Bayesian SNP best linear unbiased prediction (BLUP) model as implemented in the Bayz software [35], using only the 50 K data or the 50 K data and a second marker component with QTL marker components. In the models using only the 50 K data, all SNP effects were assumed to come from a single normal distribution.…”

Section: Methodsmentioning

confidence: 99%

“…These variances were estimated using the Gbayz programme that is part of the Bayz software [35]. For each MCMC iteration, and were estimated, where is a design matrix and and are vectors of the regression coefficients of 50 K and QTL marker effects, respectively.…”

Section: Methodsmentioning

confidence: 99%

Sequence variants selected from a multi-breed GWAS can improve the reliability of genomic predictions in dairy cattle

2016

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BackgroundSequence data can potentially increase the reliability of genomic predictions, because such data include causative mutations instead of relying on linkage disequilibrium (LD) between causative mutations and prediction variants. However, the location of the causative mutations is not known, and the presence of many variants that are in low LD with the causative mutations may reduce prediction reliability. Our objective was to investigate whether the use of variants at quantitative trait loci (QTL) that are identified in a multi-breed genome-wide association study (GWAS) for milk, fat and protein yield would increase the reliability of within- and multi-breed genomic predictions in Holstein, Jersey and Danish Red cattle. A wide range of scenarios that test different strategies to select prediction markers, for both within-breed and multi-breed prediction, were compared.ResultsFor all breeds and traits, the use of variants selected from a multi-breed GWAS resulted in substantial increases in prediction reliabilities compared to within-breed prediction using a 50 K SNP array. Reliabilities depended highly on the choice of the prediction markers, and the scenario that led to the highest reliability varied between breeds and traits. While genomic correlations across breeds were low for genome-wide sequence variants, the effects of the QTL variants that yielded the highest reliabilities were highly correlated across breeds.ConclusionsOur results show that the use of sequence variants, which are located near peaks of QTL that are detected in a multi-breed GWAS, can increase reliability of genomic predictions.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0259-0) contains supplementary material, which is available to authorized users.

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Efficiency of genomic selection using Bayesian multi-marker models for traits selected to reflect a wide range of heritabilities and frequencies of detected quantitative traits loci in mice

Cited by 10 publications

References 29 publications

Inferring genetic architecture of complex traits using Bayesian integrative analysis of genome and transcriptome data

Inferring genetic architecture of complex traits using Bayesian integrative analysis of genome and transcriptome data

Genetic analysis for dynamic changes of egg weight in 2 chicken lines

Sequence variants selected from a multi-breed GWAS can improve the reliability of genomic predictions in dairy cattle

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