Dataset Information

A Likelihood-Based Approach for Missing Genotype Data.

ABSTRACT: Missing genotype data in a candidate gene association study can make it difficult to model the effects of multiple genetic variants simultaneously. In particular, when regression models are used to model phenotype as a function of SNP genotypes in several different genes, the most common approach is a complete case analysis, in which only individuals with no missing genotypes are included. But this can lead to substantial reduction in sample size and thus potential bias and loss in efficiency. A number of other methods for handling missing data are applicable, but have rarely been used in this context. The purpose of this paper is to describe how several standard methods for handling missing data can be applied or adapted to this problem, and to compare their performance using a simulation study. We demonstrate these techniques using an Alzheimer's disease association study. We show that the expectation-maximization algorithm and multiple imputation with a bootstrapped expectation-maximization sampling algorithm have the best properties of all the estimators studied.

SUBMITTER: D'Angelo GM

PROVIDER: S-EPMC7077088 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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A Likelihood-Based Approach for Missing Genotype Data.

D'Angelo Gina M GM Kamboh M Ilyas MI Feingold Eleanor E

Human heredity 20100101 3

Missing genotype data in a candidate gene association study can make it difficult to model the effects of multiple genetic variants simultaneously. In particular, when regression models are used to model phenotype as a function of SNP genotypes in several different genes, the most common approach is a complete case analysis, in which only individuals with no missing genotypes are included. But this can lead to substantial reduction in sample size and thus potential bias and loss in efficiency. A ...[more]

PMID: 20068333

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Project description:BackgroundGenomic regions of autozygosity (ROA) arise when an individual is homozygous for haplotypes inherited identical-by-descent from ancestors shared by both parents. Over the past decade, they have gained importance for understanding evolutionary history and the genetic basis of complex diseases and traits. However, methods to infer ROA in dense genotype data have not evolved in step with advances in genome technology that now enable us to rapidly create large high-resolution genotype datasets, limiting our ability to investigate their constituent ROA patterns.MethodsWe report a weighted likelihood approach for inferring ROA in dense genotype data that accounts for autocorrelation among genotyped positions and the possibilities of unobserved mutation and recombination events, and variability in the confidence of individual genotype calls in whole genome sequence (WGS) data.ResultsForward-time genetic simulations under two demographic scenarios that reflect situations where inbreeding and its effect on fitness are of interest suggest this approach is better powered than existing state-of-the-art methods to infer ROA at marker densities consistent with WGS and popular microarray genotyping platforms used in human and non-human studies. Moreover, we present evidence that suggests this approach is able to distinguish ROA arising via consanguinity from ROA arising via endogamy. Using subsets of The 1000 Genomes Project Phase 3 data we show that, relative to WGS, intermediate and long ROA are captured robustly with popular microarray platforms, while detection of short ROA is more variable and improves with marker density. Worldwide ROA patterns inferred from WGS data are found to accord well with those previously reported on the basis of microarray genotype data. Finally, we highlight the potential of this approach to detect genomic regions enriched for autozygosity signals in one group relative to another based upon comparisons of per-individual autozygosity likelihoods instead of inferred ROA frequencies.ConclusionsThis weighted likelihood ROA inference approach can assist population- and disease-geneticists working with a wide variety of data types and species to explore ROA patterns and to identify genomic regions with differential ROA signals among groups, thereby advancing our understanding of evolutionary history and the role of recessive variation in phenotypic variation and disease.

Dataset Information

A Likelihood-Based Approach for Missing Genotype Data.

Publications

A Likelihood-Based Approach for Missing Genotype Data.

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