BioinformaticsGeneticsPeer-reviewed journal article
Very low depth whole genome sequencing in complex trait association studies
Arthur GillyiD, Lorraine SouthamiD, Dániel SüvegesiD, Karoline B. KuchenbaeckeriD, Rachel MooreiD, Giorgio E. M. MelloniiD, Konstantinos HatzikotoulasiD, Aliki‐Eleni FarmakiiD, Graham R. S. RitchieiD, Jeremy SchwartzentruberiD, Petr DanecekiD, Britt KilianiD, Martin PollardiD, Xiangyu Jack GeiD, Emmanouil Tsafantakis, George V. DedoussisiD, Eleftheria ZegginiiD
Abstract
MotivationVery low depth sequencing has been proposed as a cost-effective approach to capture low-frequency and rare variation in complex trait association studies. However, a full characterisation of the genotype quality and association power for very low depth sequencing designs is still lacking.\n\nResultsWe perform cohort-wide whole genome sequencing (WGS) at low depth in 1,239 individuals (990 at 1x depth and 249 at 4x depth) from an isolated population, and establish a robust pipeline for calling and imputing very low depth WGS genotypes from standard bioinformatics tools. Using genotyping chip, whole-exome sequencing (WES, 75x depth) and high-depth (22x) WGS data in the same samples, we examine in detail the sensitivity of this approach, and show that imputed 1x WGS recapitulates 95.2% of variants found by imputed GWAS with an average minor allele concordance of 97% for common and low-frequency variants. In our study, 1x further allowed the discovery of 140,844 true low-frequency variants with 73% genotype concordance when compared to high-depth WGS data. Finally, using association results for 57 quantitative traits, we show that very low depth WGS is an efficient alternative to imputed GWAS chip designs, allowing the discovery of up to twice as many true association signals than the classical imputed GWAS design.\n\nSupplementary DataSupplementary Data are appended to this manuscript.
- organism: human
- assay: whole-genome sequencing
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 93
- FWCI
- 5.67
- Field percentile
- top 3%
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- A Novel Efficient Algorithm for Common Variants Genotyping from Low-Coverage Sequencing Data
Shixuan Zhang, Kanglong Xiao, … Yi Wang·Computational and Structural Biotechnology ReportsPeer-reviewed·Jan 1, 2025
- Low coverage whole genome sequencing enables accurate assessment of common variants and calculation of genome-wide polygenic scores
Julian R. Homburger, Cynthia L. Neben, … Amit V. Khera·Genome MedicinePeer-reviewed·Nov 26, 2019
- Sequencing and Imputation in GWAS: Cost-Effective Strategies to Increase Power and Genomic Coverage Across Diverse Populations
Corbin Quick, Pramod Anugu, … Christian Fuchsberger·Genetic EpidemiologyPeer-reviewed·Jun 9, 2020
- Low-coverage sequencing cost-effectively detects known and novel variation in underrepresented populations
Alicia R. Martin, Elizabeth G. Atkinson, … Zukiswa Zingela·The American Journal of Human GeneticsPeer-reviewed·Mar 25, 2021
- High performance imputation of structural and single nucleotide variants using low-coverage whole genome sequencing
Manu Kumar Gundappa, Diego Robledo, … Daniel J. Macqueen·Genetics Selection EvolutionPeer-reviewed·Mar 28, 2025
- Boosting the power of rare variant association studies by imputation using large-scale sequencing population
Jinglan Dai, Yixin Zhang, … Sipeng Shen·Genomics Proteomics & BioinformaticsPeer-reviewed·Sep 17, 2025