Comparing low-pass sequencing and genotyping for trait mapping in pharmacogenetics
Kaja A. WasikiD, Tomaz Berisa, Joseph K. Pickrell, Jeremiah H. LiiD, Dana J. FraseriD, Karen S. KingiD, Charles J. Cox
Abstract
Low pass sequencing has been proposed as a cost-effective alternative to genotyping arrays to identify genetic variants that influence multifactorial traits in humans. For common diseases this typically has required both large sample sizes and comprehensive variant discovery. Genotyping arrays are also routinely used to perform pharmacogenetic (PGx) experiments where sample sizes are likely to be significantly smaller, but clinically relevant effect sizes likely to be larger. To assess how low pass sequencing would compare to array based genotyping for PGx we compared a low-pass assay (in which 1x coverage or less of a target genome is sequenced) along with software for genotype imputation to standard approaches. We sequenced 79 individuals to 1x genome coverage and genotyped the same samples on the Affymetrix Axiom Biobank Precision Medicine Research Array (PMRA). We then down-sampled the sequencing data to 0.8x, 0.6x, and 0.4x coverage, and performed imputation. Both the genotype data and the sequencing data were further used to impute human leukocyte antigen (HLA) genotypes for all samples. We compared the sequencing data and the genotyping array data in terms of four metrics: overall concordance, concordance at single nucleotide polymorphisms in pharmacogenetics-related genes, concordance in imputed HLA genotypes, and imputation r2. Overall concordance between the two assays ranged from 98.2% (for 0.4x coverage sequencing) to 99.2% (for 1x coverage sequencing), with qualitatively similar numbers for the subsets of variants most important in pharmacogenetics. At common single nucleotide polymorphisms (SNPs), the mean imputation r2 from the genotyping array was 90%, which was comparable to the imputation r2 from 0.4x coverage sequencing, while the mean imputation r2 from 1x sequencing data was 96%. These results indicate that low-pass sequencing to a depth above 0.4x coverage attains higher power for trait mapping when compared to the PMRA.
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 70
- FWCI
- 6.78
- Field percentile
- top 2%
- Organism
- Assay
- Research Focus
- Approach
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
- Comparative evaluation of genotyping and low-pass sequencing for pharmacogenetic variant and phenotype inference
Flavia Hodel, Christian Wandall Thorball, … François R. Girardin·medRxiv·Aug 19, 2026
- Low-pass sequencing increases the power of GWAS and decreases measurement error of polygenic risk scores compared to genotyping arrays
Jeremiah H. Li, Chase A. Mazur, … Joseph K. Pickrell·Genome ResearchPeer-reviewed·Apr 1, 2021
- Imputation and polygenic score performance of low coverage whole-genome sequencing and genotyping arrays in diverse human populations
Phi Truong Nguyen, Vy Nguyen, … Thuy Duong Ho Huynh·bioRxiv·Jul 18, 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
- 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
- Imputation and polygenic score performances of human genotyping arrays in diverse populations
Dat Nguyen, Trang Tran, … Nam S. Vo·bioRxiv·Jun 14, 2022