Statistical Analysis and Experimental Design

Ball, Roderick D.

doi:10.1007/978-0-387-36011-9_8

Cited by 14 publications

(17 citation statements)

References 70 publications

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“…power curves in Figures 5-9 below). Results are qualitatively similar to the results from simulations (Ball 2007a), which indicated a slightly higher sample size of 1200 for power of 0.95 to obtain B = 10 6 .…”

Section: Resultssupporting

confidence: 84%

“…There have been many published spurious associations (reviewed in Terwilliger and Weiss 1998;Altshuler et al 2000;Emahazion et al 2001;Ball 2007a). In many of these, p-values were used, and it would have been apparent from a Bayesian analysis that the evidence was weak, e.g., with a Bayes factor ,1 meaning that the data were more likely to be observed under the null hypothesis, H 0 , of no effect than under the alternative hypothesis, H 1 , that there is a real effect.…”

Section: Spurious Associationsmentioning

confidence: 99%

“…The key to the power calculation is a closed-form expression for an (asymptotic) approximate Bayes factor, which we derive using the SavageDickey density ratio, i.e., the ratio of prior to posterior densities at zero. Approximate Bayes factors of this type for testing genetic associations were first given in Ball (2007a) and subsequently by Wakefield (2007) …”

Section: Powermentioning

confidence: 99%

“…This may not be the case. A number of authors have pointed out the problems with using p-value for testing scientific hypotheses (e.g., Berger and Berry 1988;Ball 2007a). …”

mentioning

confidence: 99%

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Experimental Designs for Robust Detection of Effects in Genome-Wide Case–Control Studies

Ball

2011

Genetics

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In genome-wide association studies hundreds of thousands of loci are scanned in thousands of cases and controls, with the goal of identifying genomic loci underpinning disease. This is a challenging statistical problem requiring strong evidence. Only a small proportion of the heritability of common diseases has so far been explained. This "dark matter of the genome" is a subject of much discussion. It is critical to have experimental design criteria that ensure that associations between genomic loci and phenotypes are robustly detected. To ensure associations are robustly detected we require good power (e.g., 0.8) and sufficiently strong evidence [i.e., a high Bayes factor (e.g., 10 6 , meaning the data are 1 million times more likely if the association is real than if there is no association)] to overcome the low prior odds for any given marker in a genome scan to be associated with a causal locus. Power calculations are given for determining the sample sizes necessary to detect effects with the required power and Bayes factor for biallelic markers in linkage disequilibrium with causal loci in additive, dominant, and recessive genetic models. Significantly stronger evidence and larger sample sizes are required than indicated by traditional hypothesis tests and power calculations. Many reported putative effects are not robustly detected and many effects including some large moderately low-frequency effects may remain undetected. These results may explain the dark matter in the genome. The power calculations have been implemented in R and will be available in the R package ldDesign.T HE goal of genome-wide association studies (GWAS) is to understand the genetic basis of quantitative traits and complex diseases, by relating genotypes of large numbers of SNP markers to observed phenotypes. Results of GWAS to date suggest that the traits of interest are governed by many small effects (e.g., Wellcome Trust Case Control Consortium 2007; Diabetes Genetics Initiative of Broad Institute of Harvard and MIT 2007;Gudbjartsson et al. 2008;Lettre et al. 2008;Weedon et al. 2008;Lango Allen et al. 2010). Previously, we gave Bayesian power calculations for genome-wide association studies for quantitative traits (Ball 2005). This approach ensures power to detect effects of a specified size with a given Bayes factor, where the Bayes factor is chosen large enough to overcome the low prior odds for effects. These were the first results showing that much larger sample sizes were needed (e.g., thousands, even for a modest Bayes factor) than had been used at the time (cf. Luo 1998). However, these calculations do not apply to a binary phenotype such as presence or absence of a disease, e.g., coronary artery disease or type II diabetes, or case-control studies. Case-control studies, where a fixed (e.g., approximately equal) number of cases and controls are sampled, are used to study diseases that may not have high prevalence in the general population. This approach has higher power than a random population sample that may other...

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Section: Resultssupporting

confidence: 84%

Section: Spurious Associationsmentioning

confidence: 99%

Section: Powermentioning

confidence: 99%

“…This may not be the case. A number of authors have pointed out the problems with using p-value for testing scientific hypotheses (e.g., Berger and Berry 1988;Ball 2007a). …”

mentioning

confidence: 99%

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Experimental Designs for Robust Detection of Effects in Genome-Wide Case–Control Studies

Ball

2011

Genetics

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“…Various reasons have been postulated for the lack of repeatability, including the following: (1) the effect of genes was small; (2) Fig. 4 Interaction plot of SNP effects for stem straightness across sites in three trial series populations used in these studies were different or of limited sample size or (3) analytical methods and criteria for assessing associations were different (Ball 2007b;Tabor et al 2002). The current study has used multiple populations to investigate association between SNPs and economic important traits within each single trial series.…”

Section: Discussionmentioning

confidence: 99%

Association of single nucleotide polymorphisms with form traits in three New Zealand populations of radiata pine in the presence of genotype by environment interactions

Wilcox

Telfer

et al. 2016

Tree Genetics & Genomes

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Associations of single nucleotide polymorphisms (SNP) with the form traits, branch cluster frequency and stem straightness, were studied in three radiata pine (Pinus radiata D. Don) breeding populations. For branch cluster frequency, high genotype by environment (G×E) interactions were found between two sites of the POP1 trial series (Tarawera and Glenledi) and between two sites of the POP2 trial series (Tarawera and Woodhill). For stem straightness, high G×E interactions were found between two sites (Tarawera and Woodhill) in both the POP2 and POP3 trial series. Thirtytwo and 26 SNPs were associated with branch cluster frequency and stem straightness respectively, across a number of sites and trial series in a genetic model assuming heterogeneous additive genetic variances across sites. These SNPs had significant associations with branch cluster frequency or stem straightness either in one site only, across sites within a trial series or across different trial series. Seven SNPs had significant associations with branch cluster frequency and seven SNPs with stem straightness across two of three trial series. One SNP had significant association with stem straightness across all three trial series. Strong G×E interactions resulted in fewer significant SNPs in common across the sites among which there were large effect differences. The percentage of genetic variance explained by SNPs showing significant associations ranged from 0.23 to 8.76 % for branch cluster frequency and from 0.37 to 12.75 % for stem straightness. This study showed that SNP effects for a trait change across environments if G×E interactions exist for that trait.

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Association Mapping

Singh

2015

Marker-Assisted Plant Breeding: Principles and Practices

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Statistical Analysis and Experimental Design

Cited by 14 publications

References 70 publications

Experimental Designs for Robust Detection of Effects in Genome-Wide Case–Control Studies

Experimental Designs for Robust Detection of Effects in Genome-Wide Case–Control Studies

Association of single nucleotide polymorphisms with form traits in three New Zealand populations of radiata pine in the presence of genotype by environment interactions

Association Mapping

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