Aggregate variant calling using short reads enables population and disease studies for paralogous genes.

Authors

Timofey Prodanov, Sang Yoon Byun, Vikas Bansal

Published in

HGG advances. Pages 100653. Jul 20, 2026. Epub Jul 20, 2026.

Abstract

Variant calling in paralogous genes using short-read sequencing is problematic due to mapping ambiguity between highly similar sequences. Aggregate variant calling, which treats paralogous loci as a single locus by realigning reads to a masked reference genome, can enable variant detection in paralogous genes. We used our informatics tool Parascopy to assess the accuracy of aggregate variant calling in paralogous genes using short-read data. Parascopy achieved significantly higher recall compared to standard variant calling without sacrificing precision. We identified 158 paralogous genes with over 25 percentage points improvement in recall using simulated data, and 118 genes with at least 10 percentage points improvement in recall across GIAB reference samples. Across 1000 Genomes samples, aggregate genotypes in paralogous genes were highly concordant between whole-genome and whole-exome data (r²=0.9998). Using sequence data from population and disease cohorts, we utilized aggregate variant calls to perform population-genetic analysis, case-control association analysis, and haplotype phasing in specific disease-relevant paralogous genes that are inaccessible to standard diploid variant calling. Specifically, in the SMN1 gene, we identified tag-SNPs for two-copy SMN1 haplotypes and showed that specific low-frequency missense variants in African populations occurred exclusively on such haplotypes. For the CFC1 gene-previously implicated in heterotaxy syndrome-association analysis using exome data indicated that loss-of-function variants are unlikely to be associated with heterotaxy in individuals with congenital heart disease. Finally, we utilized aggregate genotypes to perform haplotype phasing for the X-linked gene IKBKG, achieving 99.2% concordance between trio-based and statistical phasing approaches.

PMID:
42478050
Bibliographic data and abstract were imported from PubMed on 21 Jul 2026.

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