Ultra Low-Coverage Whole-Genome Sequencing as an Alternative to Genotyping Arrays in Genome-Wide Association Studies

Abstract

An array-based genotyping approach has been the standard practice for genome-wide association studies (GWASs); however, as sequencing costs plummet over the past years, ultra low-coverage whole-genome sequencing (ulcWGS <0.5× coverage) has emerged as a promising alternative that provides superior genomic coverage with substantial reduction of genotyping cost. To evaluate the potential utility of ulcWGS, we performed a whole-genome sequencing (WGS) of 72 European individuals to a target coverage of 0.4× and compared its performance with the widely used Infinium Global Screening Multi-Disease Array (GSA-MD). We showed that the number of variants captured by ulcWGS is comparable with imputed GSA-MD platform, particularly for low-frequency (95.5%) and common variants (99.9%), with high imputation R2 accuracy (mean 0.93 for SNPs and 0.86 for indels). Using deep-coverage 30× WGS as the “truth” genotypes, we found that ulcWGS has higher overall nonreference genotype concordance compared with imputed GSA-MD for both SNPs (0.90 vs. 0.88) and indels (0.86 vs. 0.83). In addition, ulcWGS proved to be as sensitive as the genotyping-based method in sex imputation and ancestry prediction producing similar principal component (PC) scores. Our findings provide important evidence that the cost efficient ulcWGS of <0.5× generates high genotype accuracy, outperforming the standard genotyping arrays, making it an attractive alternative to the array-based method in next-generation GWAS design.

A comparison between low-cost library preparation kits for low coverage sequencing

“…Low coverage whole genome sequencing with imputation can be used in place of high-density SNP genotyping arrays and can provide additional insights and power for downstream analyses such as GWAS, PRS, and complex trait mapping (Chat et al 2022;Gilly et al (2019); Homburger et al 2019;Li et al 2021;Wasik et al 2021). One key decision when designing experiments involving whole genome sequencing is in determining the optimal library preparation method: maximizing output with low levels of duplicates, while keeping in mind factors such as time and cost per sample.…”

A genome-wide association study of germline variation and melanoma prognosis

“…As part of quality control (QC), with PLINK 1.9/2.0 ( 25 ), we computed principal component scores (PC scores detailed in ( 26 )), and excluded study participants of non-European ancestry (> ± 3SD from the mean PC scores). We also removed samples with reported vs. imputed sex discrepancy, or with enriched rate of heterozygosity (>3SD from the mean) as well as samples of cryptic relatedness (Pi-hat >0.25).…”

Long-range Hill-Robertson effect in adapting populations with recombination and standing variation

“…CC-BY 4.0 International license perpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted November 7, 2022. ; https://doi.org/10.1101/2022.11.07.515399 doi: bioRxiv preprint methods of full-genome DNA sequencing caused the emergence of the field of genomics and proteomics dedicated to the quantitative aspects of genetic diversity and gene expression at a large number of loci (2)(3)(4)(5)(6)(7). To describe and visualize the genetic complexity, various computational methods have been developed including phylogenetics, the principle-components analysis, the cluster analysis.…”

“…The average difference between an individual's genome and the consensus genome is estimated at 20 million base pairs, or 0.6% of the total of 3.2 billion base pairs (1). The invention of the new methods of full-genome DNA sequencing caused the emergence of the field of genomics and proteomics dedicated to the quantitative aspects of genetic diversity and gene expression at a large number of loci (2)(3)(4)(5)(6)(7). To describe and visualize the genetic complexity, various computational methods have been developed including phylogenetics, the principle-components analysis, the cluster analysis.…”