The distribution of the effects of genes affecting quantitative traits
in livestock

Hayes, Ben J.; Goddard, Michael E.

doi:10.1051/gse:2001117

Cited by 89 publications

(85 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multiplying the r 2 by 4 to approximate the within-family variance explained by the joint QTL would give very unrealistic values, thus illustrating that the variances explained by the joint QTL are overestimated. Hayes & Goddard (2001) quantified the level of upward bias using empirical pig and dairy cattle data and the present results agree with their trend. The total overestimation of the QTL variances increases with the number of QTL that are detected.…”

Section: (Iii) Qtl Effectssupporting

confidence: 89%

Mapping QTLs for traits measured as percentages

Yongcai

2004

Genet. Res.

View full text Add to dashboard Cite

Many quantitative traits are measured as percentages. As a result, the assumption of a normal distribution for the residual errors of such percentage data is often violated. However, most quantitative trait locus (QTL) mapping procedures assume normality of the residuals. Therefore, proper data transformation is often recommended before statistical analysis is conducted. We propose the probit transformation to convert percentage data into variables with a normal distribution. The advantage of the probit transformation is that it can handle measurement errors with heterogeneous variance and correlation structure in a statistically sound manner. We compared the results of this data transformation with other transformations and found that this method can substantially increase the statistical power of QTL detection. We develop the QTL mapping procedure based on the maximum likelihood methodology implemented via the expectation-maximization algorithm. The efficacy of the new method is demonstrated using Monte Carlo simulation.

show abstract

Section: (Iii) Qtl Effectssupporting

confidence: 89%

Mapping QTLs for traits measured as percentages

Yongcai

2004

Genet. Res.

View full text Add to dashboard Cite

show abstract

“…In each replicate of the first phenotype 50 markers were randomly selected as causal loci considering gene density, to guide causal loci to gene-dense regions. We assigned an additive effect randomly drawn from a γ-distribution35 (shape=0.5, scale=1) to a random allele of each of these markers. In addition, we added a random environmental term so that h 2 of the simulated traits was 0.7.…”

Section: Methodsmentioning

confidence: 99%

A multi-marker association method for genome-wide association studies without the need for population structure correction

Klasen

Barbez

Meier³

et al. 2016

Nat Commun

View full text Add to dashboard Cite

All common genome-wide association (GWA) methods rely on population structure correction, to avoid false genotype-to-phenotype associations. However, population structure correction is a stringent penalization, which also impedes identification of real associations. Using recent statistical advances, we developed a new GWA method, called Quantitative Trait Cluster Association Test (QTCAT), enabling simultaneous multi-marker associations while considering correlations between markers. With this, QTCAT overcomes the need for population structure correction and also reflects the polygenic nature of complex traits better than single-marker methods. Using simulated data, we show that QTCAT clearly outperforms linear mixed model approaches. Moreover, using QTCAT to reanalyse public human, mouse and Arabidopsis GWA data revealed nearly all known and some previously undetected associations. Following up on the most significant novel association in the Arabidopsis data allowed us to identify a so far unknown component of root growth.

show abstract

“…Additive effects of the QTL alleles were sampled from a gamma distribution whose shape parameter was 0.42 and scale parameter was 5.4, based on the results of Hayes and Goddard (2001). The QTL effects were assumed to be either positive or negative, with a probability of 0.5.…”

Section: Modelmentioning

confidence: 99%

A novel genomic selection method combining GBLUP and LASSO

Wang

Bao

2015

Genetica

View full text Add to dashboard Cite

Genetic prediction of quantitative traits is a critical task in plant and animal breeding. Genomic selection is an accurate and efficient method of estimating genetic merits by using high-density genome-wide single nucleotide polymorphisms (SNP). In the framework of linear mixed models, we extended genomic best linear unbiased prediction (GBLUP) by including additional quantitative trait locus (QTL) information that was extracted from high-throughput SNPs by using least absolute shrinkage selection operator (LASSO). GBLUP was combined with three LASSO methods-standard LASSO (SLGBLUP), adaptive LASSO (ALGBLUP), and elastic net (ENGBLUP)-that were used for detecting QTLs, and these QTLs were fitted as fixed effects; the remaining SNPs were fitted using a realized genetic relationship matrix. Simulations performed under distinct scenarios revealed that (1) the prediction accuracy of SLGBLUP was the lowest; (2) the prediction accuracies of ALGBLUP and ENGBLUP were equivalent to or higher than that of GBLUP, except under scenarios in which the number of QTLs was large; and (3) the persistence of prediction accuracy over generations was strongest in the case of ENGBLUP. Building on the favorable computational characteristics of GBLUP, ENGBLUP enables robust modeling and efficient computation to be performed for genomic selection.

show abstract

The distribution of the effects of genes affecting quantitative traits in livestock

Cited by 89 publications

References 8 publications

Mapping QTLs for traits measured as percentages

Mapping QTLs for traits measured as percentages

A multi-marker association method for genome-wide association studies without the need for population structure correction

A novel genomic selection method combining GBLUP and LASSO

Contact Info

Product

Resources

About