Frequency spectrum of rare and clinically relevant markers in multiethnic Indian populations (ClinIndb): A resource for genomic medicine in India

Abstract

There have been concerted efforts toward cataloging rare and deleterious variants in different world populations using high-throughput genotyping and sequencingbased methods. The Indian population is underrepresented or its information with respect to clinically relevant variants is sparse in public data sets. The aim of this study was to estimate the burden of monogenic disease-causing variants in Indian populations. Toward this, we have assessed the frequency profile of monogenic phenotype-associated ClinVar variants. The study utilized a genotype data set (global screening array, Illumina) from 2795 individuals (multiple in-house genomics cohorts) representing diverse ethnic and geographically distinct Indian populations. Of the analyzed variants from Global Screening Array,~9% were found to be informative and were either not known earlier or underrepresented in public databases in terms of their frequencies. These variants were linked to disorders, namely inborn errors of metabolism, monogenic diabetes, hereditary cancers, and various other hereditary conditions. We have also shown that our study cohort is genetically a better representative of the Indian population than its representation in the 1000 Genome Project (South Asians). We have created a database, ClinIndb, linked to the Leiden Open Variation Database, to help clinicians and researchers in diagnosis, counseling, and development of appropriate genetic screening tools relevant to the Indian populations and Indians living abroad.

Establishing analytical validity of BeadChip array genotype data by comparison to whole-genome sequence and standard benchmark datasets

“…To the best of our knowledge, this is the first comprehensive analytical validation of the GSA for clinical genotyping. Our findings support and extend recently reported research studies assessing the utility of the GSA for genetic screening in primary immunodeficiency [ 43 ], for population-based genomic screening for rare and medically relevant variation [ 44 ], and for detecting rare and clinically relevant markers in multiethnic Indian populations [ 45 ].…”

Establishing Analytical Validity of BeadChip Array Genotype Data by Comparison to Whole-Genome Sequence and Standard Benchmark Datasets

“…To the best of our knowledge, this is the first comprehensive analytical validation of the GSA for clinical genotyping. Our findings support and extend recently reported research studies assessing the utility of the GSA for genetic screening in primary immunodeficiency (45), for population-based genomic screening for rare and medically relevant variation (46), and for detecting rare and clinically relevant markers in multiethnic Indian populations (47).…”

“…al. (47). Similarly, Mendelian inheritance and pedigree information quality control are critical for linkage and segregation analyses and did not apply to our individual-focused assay.…”

Genetic Risk Prediction of COVID-19 Susceptibility and Severity in the Indian Population

“…So we first overlapped the GWAS results with the IGVC SNP data, from which the top 100 significant SNPs (sorted by p-value) were selected and verified for LD conservation. The genetic closeness of individuals of the IGVC to the SAS populations of the 1,000 Genomes has been shown in a recent study ( Narang et al, 2020 ). Another study ( Sengupta et al, 2016 ) has also displayed similar inferences with four distinct Indian ancestral categories - Ancestral North Indian, Ancestral South Indian, Austro-Asiatic and Tibeto Burman as represented by IE, DR, AA, and TB in the IGVC.…”