Dataset Information

Non-random error in genotype calling procedures: implications for family-based and case-control genome-wide association studies.

ABSTRACT: The considerable data-handling requirements for genome wide association studies (GWAS) prohibit individual calling of genotypes and create a reliance on sophisticated "genotype-calling algorithms." Despite their obvious utility, the current genotyping platforms and calling-algorithms used are not without their limitations. Specifically, some genotypes are not called due to the ambiguity of the data. Any bias in the missing data could create spurious results. Using data from the Genetic Analysis Information Network (GAIN) we observed that missing genotypes are not randomly distributed throughout the homozygous and heterozygous groups. Using simulation, we examined whether the level and type of missingness observed might influence deviation from the null-hypothesis under common case-control and family-based statistical approaches. Under a case-control model, where missingness is present in a case group but not the controls, we observed bias giving rise to genome-wide significant type-I error for missingness as low as 3%. The family-based association simulations show close to nominal type-I error at 4% genotype missingness. These findings have important implications to study design, quality-control procedures and reporting of findings in GWAS.

SUBMITTER: Anney RJ

PROVIDER: S-EPMC2921075 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Non-random error in genotype calling procedures: implications for family-based and case-control genome-wide association studies.

Anney Richard J L RJ Kenny Elaine E O'Dushlaine Colm T CT Lasky-Su Jessica J Franke Barbara B Morris Derek W DW Neale Benjamin M BM Asherson Philip P Faraone Stephen V SV Gill Michael M

American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics 20081201 8

The considerable data-handling requirements for genome wide association studies (GWAS) prohibit individual calling of genotypes and create a reliance on sophisticated "genotype-calling algorithms." Despite their obvious utility, the current genotyping platforms and calling-algorithms used are not without their limitations. Specifically, some genotypes are not called due to the ambiguity of the data. Any bias in the missing data could create spurious results. Using data from the Genetic Analysis ...[more]

PMID: 18663727

Similar Datasets

Project description:Imaging traits are thought to have more direct links to genetic variation than diagnostic measures based on cognitive or clinical assessments and provide a powerful substrate to examine the influence of genetics on human brains. Although imaging genetics has attracted growing attention and interest, most brain-wide genome-wide association studies focus on voxel-wise single-locus approaches, without taking advantage of the spatial information in images or combining the effect of multiple genetic variants. In this paper we present a fast implementation of voxel- and cluster-wise inferences based on the random field theory to fully use the spatial information in images. The approach is combined with a multi-locus model based on least square kernel machines to associate the joint effect of several single nucleotide polymorphisms (SNP) with imaging traits. A fast permutation procedure is also proposed which significantly reduces the number of permutations needed relative to the standard empirical method and provides accurate small p-value estimates based on parametric tail approximation. We explored the relation between 448,294 single nucleotide polymorphisms and 18,043 genes in 31,662 voxels of the entire brain across 740 elderly subjects from the Alzheimer's disease neuroimaging initiative (ADNI). Structural MRI scans were analyzed using tensor-based morphometry (TBM) to compute 3D maps of regional brain volume differences compared to an average template image based on healthy elderly subjects. We find method to be more sensitive compared with voxel-wise single-locus approaches. A number of genes were identified as having significant associations with volumetric changes. The most associated gene was GRIN2B, which encodes the N-methyl-d-aspartate (NMDA) glutamate receptor NR2B subunit and affects both the parietal and temporal lobes in human brains. Its role in Alzheimer's disease has been widely acknowledged and studied, suggesting the validity of the approach. The various advantages over existing approaches indicate a great potential offered by this novel framework to detect genetic influences on human brains.

Project description:A fundamental challenge in analyzing next-generation sequencing (NGS) data is to determine an individual's genotype accurately, as the accuracy of the inferred genotype is essential to downstream analyses. Correctly estimating the base-calling error rate is critical to accurate genotype calls. Phred scores that accompany each call can be used to decide which calls are reliable. Some genotype callers, such as GATK and SAMtools, directly calculate the base-calling error rates from phred scores or recalibrated base quality scores. Others, such as SeqEM, estimate error rates from the read data without using any quality scores. It is also a common quality control procedure to filter out reads with low phred scores. However, choosing an appropriate phred score threshold is problematic as a too high threshold may lose data, while a too low threshold may introduce errors. We propose a new likelihood-based genotype-calling approach that exploits all reads and estimates the per-base error rates by incorporating phred scores through a logistic regression model. The approach, which we call PhredEM, uses the expectation-maximization (EM) algorithm to obtain consistent estimates of genotype frequencies and logistic regression parameters. It also includes a simple, computationally efficient screening algorithm to identify loci that are estimated to be monomorphic, so that only loci estimated to be nonmonomorphic require application of the EM algorithm. Like GATK, PhredEM can be used together with a linkage-disequilibrium-based method such as Beagle, which can further improve genotype calling as a refinement step. We evaluate the performance of PhredEM using both simulated data and real sequencing data from the UK10K project and the 1000 Genomes project. The results demonstrate that PhredEM performs better than either GATK or SeqEM, and that PhredEM is an improved, robust, and widely applicable genotype-calling approach for NGS studies. The relevant software is freely available.

Dataset Information

Non-random error in genotype calling procedures: implications for family-based and case-control genome-wide association studies.

Publications

Non-random error in genotype calling procedures: implications for family-based and case-control genome-wide association studies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets