Show Notes
Li Z et al., Human Genetics and Genomics Advances - LASI-DAD 30× whole-genome sequencing of 2,680 Indian participants produced a 69.5M-variant LD panel that improves genotype imputation accuracy and PRS performance for Indian populations. Key terms: LASI-DAD, linkage disequilibrium, genotype imputation, whole-genome sequencing, polygenic risk scores.
Study Highlights:
Using 30× WGS of 2,680 LASI-DAD participants, the authors constructed an LD lookup panel (69.5 million variants), phased with Eagle2.4, and identified LD structure with LDetect and Big-LD. They compared regional varLD to 1000G super-populations and evaluated imputation with Minimac4 and meta-imputation against TOPMed and GAsP. LASI-DAD increased imputation accuracy (aggregated r2) by a mean 38% versus TOPMed and 27% versus GAsP across allele frequencies and improved PRS predictive performance by 2.1%–35.1% across traits and studies. Finer-scale stronger LD and regional LD differences in LASI-DAD translate into more accurate LD estimates and better imputation and PRS transferability for Indian sub-populations.
Conclusion:
LASI-DAD is the largest nationally representative Indian WGS reference panel to date and it improves LD estimation, genotype imputation accuracy, and PRS construction for Indian and South Asian populations.
Music:
Enjoy the music based on this article at the end of the episode.
Article title:
A reference panel for linkage disequilibrium and genotype imputation using whole-genome sequencing data from 2,680 participants across India
First author:
Li Z
Journal:
Human Genetics and Genomics Advances
DOI:
10.1016/j.xhgg.2026.100579
Reference:
Li Z, Zhao W, Zhou X, Leung YY, Schellenberg GD, Wang L-S, Schönherr S, Forer L, Fuchsberger C, Dey S, Lee J, Smith JA, Dey AB, Kardia SLR. A reference panel for linkage disequilibrium and genotype imputation using whole-genome sequencing data from 2,680 participants across India. Human Genetics and Genomics Advances. 7 (2026) 100579. https://doi.org/10.1016/j.xhgg.2026.100579.
License:
This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) - https://creativecommons.org/licenses/by/4.0/
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QC:
This episode was checked against the original article PDF and publication metadata for the episode release published on 2026-03-06.
QC Scope:
- article metadata and core scientific claims from the narration
- excludes analogies, intro/outro, and music
- transcript coverage: Substantive auditing of the transcript’s scientific content covered the LASI-DAD cohort design and sequencing depth; LD/imputation methodology (LD blocks, LDetect/Big-LD, varLD); LD panel variant counts; subpopulation structure (ANI/ASI) and PRS transferability; imputation performance and meta-imputation; and data avai
- transcript topics: LASI-DAD cohort design and 30× whole-genome sequencing; LD reference panel construction and variant counts (69.5 million) and comparisons; LD blocks and varLD analyses across populations; LASI-DAD sub-populations by ANI percentage and geographic cline; PRS transferability and cross-population performance; Imputation performance and meta-imputation across reference panels
QC Summary:
- factual score: 10/10
- metadata score: 10/10
- supported core claims: 7
- claims flagged for review: 0
- metadata checks passed: 4
- metadata issues found: 0
Metadata Audited:
- article_doi
- article_title
- article_journal
- license
Factual Items Audited:
- LASI-DAD WGS sample size = 2,680 individuals
- Sequencing depth = 30×
- LD reference panel variant count = 69.5 million
- Imputation gains: mean aggregated r2 improvements = 38% versus TOPMed and 27% versus Genome Asia Pilot (GAsP)
- PRS predictive performance improvement = 2.1%–35.1% across traits and studies
- Rare variant imputation: 61,843,011 variants imputed
QC result: Pass.
