Abstract
Genetic studies have shifted to sequencing-based rare variants discovery after decades of success in identifying common disease variants by Genome-Wide Association Studies using Single Nucleotide Polymorphism chips. Sequencing-based studies require large sample sizes for statistical power and therefore often inadvertently introduce batch effects because samples are typically collected, processed, and sequenced at multiple centers. Conventionally, batch effects are first detected and visualized using Principal Components Analysis and then controlled by including batch covariates in the disease association models. For sequencing-based genetic studies, because all variants included in the association analyses have passed sequencing-related quality control measures, this conventional approach treats every variant as equal and ignores the substantial differences still remaining in variant qualities and characteristics such as genotype quality scores, alternative allele fractions (fraction of reads supporting alternative allele at a variant position) and sequencing depths. In the Alzheimer’s Disease Sequencing Project (ADSP) exome dataset of 9,904 cases and controls, we discovered hidden variant-level differences between sample batches of three sequencing centers and two exome capture kits. Although sequencing centers were included as a covariate in our association models, we observed differences at the variant level in genotype quality and alternative allele fraction between samples processed by different exome capture kits that significantly impacted both the confidence of variant detection and the identification of disease-associated variants. Furthermore, we found that a subset of top disease-risk variants came exclusively from samples processed by one exome capture kit that was more effective at capturing the alternative alleles compared to the other kit. Our findings highlight the importance of additional variant-level quality control for large sequencing-based genetic studies. More importantly, we demonstrate that automatically filtering out variants with batch differences may lead to false negatives if the batch discordances come largely from quality differences and if the batch-specific variants have better quality.
Highlights
Genetic studies have shifted from Single Nucleotide Polymorphism (SNP) chip-based genome-wide association study (GWAS) of common variants to exome and whole-genome sequencing-based associations of rare variants
Of the 1,584,609 variants detected in 9,904 Alzheimer’s Disease Sequencing Project (ADSP) exomes, 166,947 variants passed Variant Quality Score Recalibration (VQSR) and the additional filtering steps detailed in the methods section
As the Principal Components Analysis (PCA) plot of the 29 novel SNPs showed significant batch differences of genotypes between samples processed by two exome capture kits, we examined variant-level factors that could explain why exclusively the Illumina kit-captured exomes yielded this set of highly significant novel SNPs
Summary
Genetic studies have shifted from Single Nucleotide Polymorphism (SNP) chip-based genome-wide association study (GWAS) of common variants to exome and whole-genome sequencing-based associations of rare variants. The large samples required for statistical power in sequencing-based searches for rare disease-associated variants often inadvertently introduce batch effects and systematic biases. Batch effects refer to sources of variation arising not from the targeted biological differences between sample classes but from differences between experimental or technological groups of samples [1]. Practices in large sequencing studies that commonly introduce batch effects include dividing samples among multiple sequencing centers [2, 3], collecting or preparing samples under different protocols [4], and extracting exomes using different target capture kits [4, 5]. The Alzheimer’s Disease Sequencing Project (ADSP) sequenced exomes of more than 10,000 cases and controls to identify genetic factors associated with Alzheimer’s disease (AD) [6]. Center 1 prepared sequencing libraries using the Illumina Rapid Capture Exome kit, while Center 2 and Center 3 used the Roche NimbleGen VCRome v2.1 kit
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