Genotype imputation from low-coverage data for medical and population genetic analyses

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14740%2F25%3A00142430" target="_blank" >RIV/00216224:14740/25:00142430 - isvavai.cz</a>

Result on the web

<a href="https://genome.cshlp.org/content/35/9/1929" target="_blank" >https://genome.cshlp.org/content/35/9/1929</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1101/gr.280175.124" target="_blank" >10.1101/gr.280175.124</a>

Result language

angličtina

Original language name

Genotype imputation from low-coverage data for medical and population genetic analyses

Original language description

Genotype imputation from low-pass sequencing data presents unique opportunities for genomic analyses but comes with specific challenges. In this study, we explore the impact of quality filters on genetic ancestry and Polygenic Score (PGS) estimation after imputing 32,769 low-pass genome-wide sequences (LPS) from noninvasive prenatal screening (NIPS) with an average autosomal sequence depth of similar to 0.15x. In studies involving ultra-low coverage sequences, conventional approaches to secure genotype accuracy may fail, especially when multiple samples are pooled. To enhance the proportion of high-quality genotypes in large data sets, we introduce a filtering approach called GDI that combines genotype probability (GP), alternate allele dosage (DS), and INFO score filters. We demonstrate that the imputation tools QUILT and GLIMPSE2 achieve similar accuracy, which is high enough for broad-scale ancestry mapping but insufficient for high resolution principal component analysis (PCA), when applied without filters. With the GDI approach, we can achieve quality that is adequate for such purposes. Furthermore, we explored the impact of imputation errors, choice of variants, and filtering methods on PGS prediction for height in 1911 subjects with height data. We show that polygenic scores predict 23.7% of variance in height in our imputed data and that, contrary to the effect on PCA, the GDI filter does not improve the performance of PGS in height prediction. These results highlight that imputed LPS data can be leveraged for further biomedical and population genetic use, but there is a need to consider each downstream analysis tool individually for its imputation quality thresholds and filtering requirements.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10608 - Biochemistry and molecular biology

Project

<a href="/en/project/EH22_008%2F0004593" target="_blank" >EH22_008/0004593: Ready for the future: understanding long-term resilience of the human culture (RES-HUM)</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Genome research

ISSN

1088-9051

e-ISSN

—

Volume of the periodical

35

Issue of the periodical within the volume

9

Country of publishing house

US - UNITED STATES

Number of pages

13

Pages from-to

1929-1941

UT code for WoS article

001564932200001

EID of the result in the Scopus database

2-s2.0-105015087848