Very low-depth whole-genome sequencing in complex trait association studies

Area of Science:

• Genomics
• Population Genetics
• Bioinformatics

Background:

• Very low-depth sequencing is proposed for cost-effective capture of low-frequency and rare genetic variations in complex trait association studies.
• A comprehensive characterization of genotype quality and association power for very low-depth sequencing designs is currently lacking.

Purpose of the Study:

• To evaluate the genotype quality and association power of very low-depth whole-genome sequencing (WGS).
• To establish a robust bioinformatics pipeline for calling and imputing genotypes from very low-depth WGS data.
• To compare the performance of very low-depth WGS with existing genotyping methods for complex trait association studies.

Main Methods:

• Performed cohort-wide whole-genome sequencing (WGS) at 1× and 4× depth in 1239 individuals from an isolated population.
• Developed and utilized a bioinformatics pipeline for genotype calling and imputation from low-depth WGS data.
• Validated findings using genotyping chip, whole-exome sequencing (75×), and high-depth (22×) WGS data from the same samples.

Main Results:

• Imputed 1× WGS successfully recapitulated 95.2% of variants found by imputed Genome-Wide Association Studies (GWAS), with 97% average minor allele concordance for common and low-frequency variants.
• Identified 140,844 additional true low-frequency variants using 1× WGS, achieving 73% genotype concordance compared to high-depth WGS.
• Demonstrated that very low-depth WGS identified up to twice as many true association signals for 57 quantitative traits compared to classical imputed GWAS chip designs.

Conclusions:

• Very low-depth WGS, particularly at 1× coverage, provides a robust and cost-effective approach for genetic variation discovery.
• The developed imputation pipeline enables accurate genotype calling from low-depth WGS data, enhancing its utility in association studies.
• Very low-depth WGS is a powerful alternative to traditional imputed GWAS chip designs, significantly increasing the power to detect genetic associations for complex traits.