GCAT|Panel, a comprehensive structural variant haplotype map of the Iberian population from high-coverage whole-genome sequencing

Abstract

The combined analysis of haplotype panels with phenotype clinical cohorts is a common approach to explore the genetic architecture of human diseases. However, genetic studies are mainly based on single nucleotide variants (SNVs) and small insertions and deletions (indels). Here, we contribute to fill this gap by generating a dense haplotype map focused on the identification, characterization, and phasing of structural variants (SVs). By integrating multiple variant identification methods and Logistic Regression Models (LRMs), we present a catalogue of 35 431 441 variants, including 89 178 SVs (≥50 bp), 30 325 064 SNVs and 5 017 199 indels, across 785 Illumina high coverage (30x) whole-genomes from the Iberian GCAT Cohort, containing a median of 3.52M SNVs, 606 336 indels and 6393 SVs per individual. The haplotype panel is able to impute up to 14 360 728 SNVs/indels and 23 179 SVs, showing a 2.7-fold increase for SVs compared with available genetic variation panels. The value of this panel for SVs analysis is shown through an imputed rare Alu element located in a new locus associated with Mononeuritis of lower limb, a rare neuromuscular disease. This study represents the first deep characterization of genetic variation within the Iberian population and the first operational haplotype panel to systematically include the SVs into genome-wide genetic studies.

PerSVade: personalized structural variant detection in any species of interest

“…We thus centered our testing on the accuracy of different parameters on SV calling. In fact, some recent approaches specifically developed for human genomes [22,30] may outcompete perSVade in human samples. However, such methods rely on previously defined sets of known SVs, which are not available in most taxa.…”

“…This means that the role of SVs remains largely unexplored across eukaryotes.Inferring SVs from short reads is challenging because it relies mostly on indirect evidence coming from de novo assembly alignment, changes in read depth, or the presence of discordantly paired / split reads in read mapping analysis [16][17][18][19][20][21]. Long-read-based SV calling may avoid some of these limitations, but short read-based SV calling remains a cost-effective strategy to find SVs in large cohorts [14,15,22]. Recent benchmarking studies compared the performance of different tools in human genomes and found that SV calling accuracy is highly dependent on the methods and filtering strategy used [15,…”

Skin Phototype and Disease. A Comprehensive Genetic Approach to Pigmentary Traits Pleiotropy using PRS in the GCAT Cohort

“…Genotyping was done using the Infinium Expanded Multi-Ethnic Genotyping Array (MEGAEX) (ILLUMINA, San Diego, California, USA). Then, we imputed both SNVs and SVs, with IMPUTE2, and using GCAT|Panel as reference [40], an ancestry-geographically matched panel generated by whole-genome sequencing. The final core set included a total of 10,216,971 variants with MAF ≥ 0.01 and INFO score ≥ 0.7 (21,620 SVs,and 10,195,351 SNVs and small indels).…”

“…Standard genome-wide threshold (p < 5e-8) was kept for significance. SNPs were functionally annotated and mapped to the closest gene with SnpEff [40]. Manhattan and quantile-quantile (QQ) plots were generated to explore the results, as well as a 3D Manhattan representation that provides a visual representation of the common GWAS hits between the different traits.…”

“…Then, for each GWAS, independent genomic loci were identified as described in Watanabe et al [45], using GCAT|panel [40] as the reference panel to compute the linkage disequilibrium (LD) measure. First, we defined independent significant SNPs (PLINK software, p1 = 0.05; p2 = 1, linkage disequilibrium threshold r2 = 0.6, and physical distance threshold for clumping 1000kb), and lead SNPs (PLINK software, p1 = 0.05, p2 = 1, r2 = 0.1, and distance 1000kb).…”