http://nce.ads.uga.edu/~ignacy/genomic-blupf90/Papers/ignacio_one_step.pdf

Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score.

A common problem for genome-wide association analysis (GWAS) is lack of power for detection of quantitative trait loci (QTLs) and precision for fine mapping. Here, we present a statistical method, termed single-step GBLUP (ssGBLUP), which increases both power and precision without increasing genotyping costs by taking advantage of phenotypes from other related and unrelated subjects. The procedure achieves these goals by blending traditional pedigree relationships with those derived from genetic markers, and by conversion of estimated breeding values (EBVs) to marker effects and weights. Additionally, the application of mixed model approaches allow for both simple and complex analyses that involve multiple traits and confounding factors, such as environmental, epigenetic or maternal environmental effects. Efficiency of the method was examined using simulations with 15 800 subjects, of which 1500 were genotyped. Thirty QTLs were simulated across genome and assumed heritability was 0·5. Comparisons included ssGBLUP applied directly to phenotypes, BayesB and classical GWAS (CGWAS) with deregressed proofs. An average accuracy of prediction 0·89 was obtained by ssGBLUP after one iteration, which was 0·01 higher than by BayesB. Power and precision for GWAS applications were evaluated by the correlation between true QTL effects and the sum of m adjacent single nucleotide polymorphism (SNP) effects. The highest correlations were 0·82 and 0·74 for ssGBLUP and CGWAS with m=8, and 0·83 for BayesB with m=16. Standard deviations of the correlations across replicates were several times higher in BayesB than in ssGBLUP. The ssGBLUP method with marker weights is faster, more accurate and easier to implement for GWAS applications without computing pseudo-data.

/pdf/genome-wide-association-mapping-including-phenotypes-from-oz5b5u7lhg.pdf

Genome-wide association mapping including phenotypes from relatives without genotypes.

The effects of heat stress in Italian Holstein dairy cattle

Michel Georges and colleagues report fine mapping and functional characterization of a quantitative trait locus with a large effect on bovine stature. They show that the likely causal variants at this locus modulate expression of a chromosomal domain encompassing PLAG1 and several other genes.

Variants modulating the expression of a chromosome domain encompassing PLAG1 influence bovine stature

Genomic evaluation models can fit additive and dominant SNP effects. Under quantitative genetics theory, additive or "breeding" values of individuals are generated by substitution effects, which involve both "biological" additive and dominant effects of the markers. Dominance deviations include only a portion of the biological dominant effects of the markers. Additive variance includes variation due to the additive and dominant effects of the markers. We describe a matrix of dominant genomic relationships across individuals, D, which is similar to the G matrix used in genomic best linear unbiased prediction. This matrix can be used in a mixed-model context for genomic evaluations or to estimate dominant and additive variances in the population. From the "genotypic" value of individuals, an alternative parameterization defines additive and dominance as the parts attributable to the additive and dominant effect of the markers. This approach underestimates the additive genetic variance and overestimates the dominance variance. Transforming the variances from one model into the other is trivial if the distribution of allelic frequencies is known. We illustrate these results with mouse data (four traits, 1884 mice, and 10,946 markers) and simulated data (2100 individuals and 10,000 markers). Variance components were estimated correctly in the model, considering breeding values and dominance deviations. For the model considering genotypic values, the inclusion of dominant effects biased the estimate of additive variance. Genomic models were more accurate for the estimation of variance components than their pedigree-based counterparts.

https://www.genetics.org/content/genetics/195/4/1223.full.pdf

On the Additive and Dominant Variance and Covariance of Individuals Within the Genomic Selection Scope

COMPUTING METHODS BGF90 is a collection of software in Fortran 90 useful for breeding & genetics applications. The package consists of library modules and application programs. The modules are designed with overloading and optional parameters so that their use is simple. Whenever applicable, the modules use well-tested low-level software in Fortran 77 or Fortran 90. The application programs deliberately avoid low-level optimization for simplicity but support a wide range of models, including those with multiple-correlated effects, multiple animal models and dominance, and they can be modified to support new features. For general description of the package, see Misztal (1999).

/pdf/blupf90-and-related-programs-bgf90-3idqp8n718.pdf

D. H. Lee

Papers

BLUPF90 and related programs (BGF90)

Proceedings from the 7th World Congress on Genetics Applied to Livestock Production