Evaluating the impact of genotype errors on rare variant tests of association

Cook, Kaitlyn; Benitez, Alejandra; Fu, Casey L.; Tintle, Nathan L.

doi:10.3389/fgene.2014.00062

Cited by 11 publications

(8 citation statements)

References 42 publications

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“…they are not affected by ascertainment bias [ 38 ] and therefore give a lot more information on rare variants. Such rare variants are often ignored because they may lead to genotyping errors [ 39 , 40 ]. In this study, quality controls were applied in the analysis to reduce the risk of using apparent segregating variants that are in fact induced by genotyping errors.…”

Section: Discussionmentioning

confidence: 99%

Whole-genome sequence data uncover loss of genetic diversity due to selection

2016

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BackgroundWhole-genome sequence (WGS) data give access to more complete structural genetic information of individuals, including rare variants, not fully covered by single nucleotide polymorphism chips. We used WGS to investigate the amount of genetic diversity remaining after selection using optimal contribution (OC), considering different methods to estimate the relationships used in OC. OC was applied to minimise average relatedness of the selection candidates and thus miminise the loss of genetic diversity in a conservation strategy, e.g. for establishment of gene bank collections. Furthermore, OC was used to maximise average genetic merit of the selection candidates at a given level of relatedness, similar to a genetic improvement strategy. In this study, we used data from 277 bulls from the 1000 bull genomes project. We measured genetic diversity as the number of variants still segregating after selection using WGS data, and compared strategies that targeted conservation of rare (minor allele frequency <5 %) versus common variants.ResultsWhen OC without restriction on the number of selected individuals was applied, loss of variants was minimal and most individuals were selected, which is often unfeasible in practice. When 20 individuals were selected, the number of segregating rare variants was reduced by 29 % for the conservation strategy, and by 34 % for the genetic improvement strategy. The overall number of segregating variants was reduced by 30 % when OC was restricted to selecting five individuals, for both conservation and genetic improvement strategies. For common variants, this loss was about 15 %, while it was much higher, 72 %, for rare variants. Fewer rare variants were conserved with the genetic improvement strategy compared to the conservation strategy.ConclusionsThe use of WGS for genetic diversity quantification revealed that selection results in considerable losses of genetic diversity for rare variants. Using WGS instead of SNP chip data to estimate relationships slightly reduced the loss of rare variants, while using 50 K SNP chip data was sufficient to conserve common variants. The loss of rare variants could be mitigated by a few percent (up to 8 %) depending on which method is chosen to estimate relationships from WGS data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0210-4) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Whole-genome sequence data uncover loss of genetic diversity due to selection

2016

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“…Imperfect data are used presently to answer a variety of research questions, including: genotype errors (Cook et al., 2014); evaluating the yearly estimates of the number of strokes in a country given the declining hospitalizations for stroke in that country where “estimating the number of strokes in a county can be highly variable depending on the recency of the data, the type of data available, and the methods used” (Cadilhac et al., 2014); and designing and validating epidemiologic surveillance in uncounted populations (Byass et al., 2011). The search for high quality data today is costly and requires human study volunteers willing to assume the risks of taking part in a study.…”

Section: Discussionmentioning

confidence: 99%

Analyzing and interpreting “imperfect” Big Data in the 1600s

Mazur

2016

Big Data & Society

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One of the characteristics of Big Data is that it often involves “imperfect” information. This paper examines the work of John Graunt (1620–1674) in the tabulation of diseases in London and the development of a life table using the “imperfect data” contained in London’s Bills of Mortality in the 1600s. London’s Bills of Mortality were Big Data for the 1600s, as they included information collected over time, the depth and accuracy of which improved gradually. The main shortcoming of the data available at the time was its nonuniform upkeep and the lack of depth of variables included at its outset. Due to these characteristics, it provides a perfect model for the examination of imperfect Big Data, as it has been analyzed, criticized, and interpreted repeatedly since the 1600s.

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“…In other words, the genotyping error rates in the case group and the control group are assumed to be the same in those approaches. However, differential genotyping error, which means the genotyping error rates in the case group and the control group are different, is more problematic because it causes inflation in type I error (Cook, Benitez, Fu, & Tintle, ; Moskvina, Craddock, Holmans, Owen, & O'Donovan, ). Differential genotyping error is more protruding in large‐scale studies, where cases and controls are likely to be genotyped at various sites with imbalanced proportion.…”

Section: Introductionmentioning

confidence: 99%

Impact of genotyping errors on statistical power of association tests in genomic analyses: A case study

Hou

Sun

Mane

et al. 2016

Genetic Epidemiology

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A key step in genomic studies is to assess high throughput measurements across millions of markers for each participant’s DNA, either using microarrays or sequencing techniques. Accurate genotype calling is essential for downstream statistical analysis of genotype-phenotype associations, and next generation sequencing (NGS) has recently become a more common approach in genomic studies. How the accuracy of variant calling in NGS-based studies affects downstream association analysis has not, however, been studied using empirical data in which both microarrays and NGS were available. In this article, we investigate the impact of variant calling errors on the statistical power to identify associations between single nucleotides and disease, and on associations between multiple rare variants and disease. Both differential and nondifferential genotyping errors are considered. Our results show that the power of burden tests for rare variants is strongly influenced by the specificity in variant calling, but is rather robust with regard to sensitivity. By using the variant calling accuracies estimated from a substudy of a Cooperative Studies Program project conducted by the Department of Veterans Affairs, we show that the power of association tests is mostly retained with commonly adopted variant calling pipelines. An R package, GWAS.PC, is provided to accommodate power analysis that takes account of genotyping errors (http://zhaocenter.org/software/).

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Evaluating the impact of genotype errors on rare variant tests of association

Cited by 11 publications

References 42 publications

Whole-genome sequence data uncover loss of genetic diversity due to selection

Whole-genome sequence data uncover loss of genetic diversity due to selection

Analyzing and interpreting “imperfect” Big Data in the 1600s

Impact of genotyping errors on statistical power of association tests in genomic analyses: A case study

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