Structural variant selection for high-altitude adaptation using single-molecule long-read sequencing

Abstract

Structural variants (SVs) can be important drivers of human adaptation with strong effects, but previous studies have focused primarily on common variants with weak effects. Here, we used large-scale single-molecule long-read sequencing of 320 Tibetan and Han samples, to show that SVs are key drivers of selection under high-altitude adaptation. We expand the landscape of global SVs, apply robust models of selection and population differentiation combining SVs, SNPs and InDels, and use epigenomic analyses to predict driver enhancers, target genes, upstream regulators, and biological functions, which we validate using enhancer reporter and DNA pull-down assays. We reveal diverse Tibetan-specific SVs affecting the cis- and trans-regulatory circuitry of diverse biological functions, including hypoxia response, energy metabolism, lung function, etc. Our study greatly expands the global SV landscape, reveals the central role of gene-regulatory circuitry rewiring in human adaptation, and illustrates the diverse functional roles that SVs can play in human biology.

Jasmine: Population-scale structural variant comparison and analysis

“…These improvements include the mitigation of threshold effects ( Figure 2d ), improved variant calling parameters ( Figure 2e ), and using Jasmine for SV merging ( Figure 2f ). Furthermore, we compared Jasmine to five existing methods (Shi et al 2021;Jeffares et al 2017;Ebert et al 2021;Larson et al 2019;Beyter et al 2021) for SV comparison between samples, and Jasmine achieves the lowest rate of discordance and correctly avoids merging variants of different types or variants from the same sample. In addition, Jasmine avoids merging variants of the same type which correspond to unique breakpoint adjacencies, which is particularly important when resolving complex nested SVs ( Supplementary Figure 21 ).…”

“…As the cost of long-read sequencing has continued to decrease in recent years, long-read studies including large cohorts have become more prevalent (Shi et al 2021;Beyter et al 2021) . As this trend is expected to continue (Ranallo-Benavidez et al 2021) , it is particularly important for SV inference methods to be able to scale to many samples.…”