Statistical and Computational Challenges in Whole Genome Prediction and Genome-Wide Association Analyses for Plant and Animal Breeding

Tempelman, Robert J.

doi:10.1007/s13253-015-0225-2

Cited by 12 publications

(8 citation statements)

References 142 publications

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“…For example, disability or the need for hip replacement surgery could be prevented if an increased risk of developmental dysplasia of the hip could be determined at a young age (Kurtz et al 2007 ; Andersson and American Academyof Orthopaedic Surgeons 2015 ). Genomic prediction, also named as genomic selection in crops and livestock, has the potential to reduce breeding costs by eliminating individuals with less potential at an early stage (Heffner et al 2011 ; Tempelman 2015 ; Wolc et al 2016 ; Yu et al 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits

et al. 2018

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Improvement of statistical methods is crucial for realizing the potential of increasingly dense genetic markers. Bayesian methods treat all markers as random effects, exhibit an advantage on dense markers, and offer the flexibility of using different priors. In contrast, genomic best linear unbiased prediction (gBLUP) is superior in computing speed, but only superior in prediction accuracy for extremely complex traits. Currently, the existing variety in the BLUP method is insufficient for adapting to new sequencing technologies and traits with different genetic architectures. In this study, we found two ways to change the kinship derivation in the BLUP method that improve prediction accuracy while maintaining the computational advantage. First, using the settlement under progressively exclusive relationship (SUPER) algorithm, we substituted all available markers with estimated quantitative trait nucleotides (QTNs) to derive kinship. Second, we compressed individuals into groups based on kinship, and then used the groups as random effects instead of individuals. The two methods were named as SUPER BLUP (sBLUP) and compressed BLUP (cBLUP). Analyses on both simulated and real data demonstrated that these two methods offer flexibility for evaluating a variety of traits, covering a broadened realm of genetic architectures. For traits controlled by small numbers of genes, sBLUP outperforms Bayesian LASSO (least absolute shrinkage and selection operator). For traits with low heritability, cBLUP outperforms both gBLUP and Bayesian LASSO methods. We implemented these new BLUP alphabet series methods in an R package, Genome Association and Prediction Integrated Tool (GAPIT), available at http://zzlab.net/GAPIT.

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Section: Introductionmentioning

confidence: 99%

Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits

et al. 2018

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“…In 80 particular, the use of variable selection procedures facilitate the determination of posterior 81 probabilities of association (PPA), whose control may be far more effective in maximizing both 82 sensitivity and specificity of GWA (Fernando et al 2014) compared to frequentist based 83 inferences which require adjustments for multiple testing such as with EMMAX. Another 84 common inferential strategy in GWA is to report the percent of variance explained by a marker 85 or marker region (Fernando and Garrick 2013 Tempelman (2015), the latter adapting the average information restricted maximum likelihood 95 5 (AIREML) algorithm for estimating hyperparameters in BayesA and SSVS specifications. 96 These MAP implementations should also be assessed for their efficacy in GWA studies.…”

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confidence: 99%

Genome Wide Association Analyses Based on Broadly Different Specifications for Prior Distributions, Genomic Windows, and Estimation Methods

Chen

Steibel

Tempelman

2017

Preprint

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17A popular strategy (EMMAX) for genome wide association (GWA) analysis fits all marker 18 effects as classical random effects (i.e., Gaussian prior) by which association for the specific 19 marker of interest is inferred by treating its effect as fixed. It seems more statistically coherent to 20 specify all markers as sharing the same prior distribution, whether it is Gaussian, heavy-tailed 21 (BayesA), or has variable selection specifications based on a mixture of, say, two Gaussian 22 SUMMARY 37 Genome wide association (GWA) analyses strategies have been improved by 38 simultaneously fitting all marker effects when inferring upon any single marker effect, with the 39 most popular distributional assumption being normality. Using data generated from 43,266 40 genotypes on 922 Duroc-Pietrain F2 cross pigs, we demonstrate that GWA studies could 41 particularly benefit from more flexible heavy-tailed or variable selection distributional 42 assumptions. Furthermore, these associations should not just be based on single markers or even 43 genomic windows of markers of fixed physical distances (0.5 -3.0 Mb) but based on adaptively 44 determined genomic windows using linkage disequilibrium information. 45

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“…The specification of hyperparameters may have a great impact on the outcome of Bayesian approaches (e.g. Tempelman, ). For the analysis of simulated data, the parameter γ specifying the proportion of mixing a zero and nonzero distribution of SNP effect was fixed near its true value and

λ = \sqrt{2 p γ}

like in Wittenburg et al.…”

Section: Discussionmentioning

confidence: 99%

An approximate Bayesian significance test for genomic evaluations

Wittenburg

Liebscher

2018

Biometrical J

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Genomic information can be used to study the genetic architecture of some trait. Not only the size of the genetic effect captured by molecular markers and their position on the genome but also the mode of inheritance, which might be additive or dominant, and the presence of interactions are interesting parameters. When searching for interacting loci, estimating the effect size and determining the significant marker pairs increases the computational burden in terms of speed and memory allocation dramatically. This study revisits a rapid Bayesian approach (fastbayes). As a novel contribution, a measure of evidence is derived to select markers with effect significantly different from zero. It is based on the credibility of the highest posterior density interval next to zero in a marginalized manner. This methodology is applied to simulated data resembling a dairy cattle population in order to verify the sensitivity of testing for a given range of type‐I error levels. A real data application complements this study. Sensitivity and specificity of fastbayes were similar to a variational Bayesian method, and a further reduction of computing time could be achieved. More than 50% of the simulated causative variants were identified. The most complex model containing different kinds of genetic effects and their pairwise interactions yielded the best outcome over a range of type‐I error levels. The validation study showed that fastbayes is a dual‐purpose tool for genomic inferences – it is applicable to predict future outcome of not‐yet phenotyped individuals with high precision as well as to estimate and test single‐marker effects. Furthermore, it allows the estimation of billions of interaction effects.

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Statistical and Computational Challenges in Whole Genome Prediction and Genome-Wide Association Analyses for Plant and Animal Breeding

Cited by 12 publications

References 142 publications

Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits

Expanding the BLUP alphabet for genomic prediction adaptable to the genetic architectures of complex traits

Genome Wide Association Analyses Based on Broadly Different Specifications for Prior Distributions, Genomic Windows, and Estimation Methods

An approximate Bayesian significance test for genomic evaluations

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