Spectrum of large copy number variations in 26 diverse Indian populations: potential involvement in phenotypic diversity

Abstract

Copy number variations (CNVs) have provided a dynamic aspect to the apparently static human genome. We have analyzed CNVs larger than 100 kb in 477 healthy individuals from 26 diverse Indian populations of different linguistic, ethnic and geographic backgrounds. These CNVRs were identified using the Affymetrix 50K Xba 240 Array. We observed 1,425 and 1,337 CNVRs in the deletion and amplification sets, respectively, after pooling data from all the populations. More than 50% of the genes encompassed entirely in CNVs had both deletions and amplifications. There was wide variability across populations not only with respect to CNV extent (ranging from 0.04-1.14% of genome under deletion and 0.11-0.86% under amplification) but also in terms of functional enrichments of processes like keratinization, serine proteases and their inhibitors, cadherins, homeobox, olfactory receptors etc. These did not correlate with linguistic, ethnic, geographic backgrounds and size of populations. Certain processes were near exclusive to deletion (serine proteases, keratinization, olfactory receptors, GPCRs) or duplication (homeobox, serine protease inhibitors, embryonic limb morphogenesis) datasets. Populations having same enriched processes were observed to contain genes from different genomic loci. Comparison of polymorphic CNVRs (5% or more) with those cataloged in Database of Genomic Variants revealed that 78% (2473) of the genes in CNVRs in Indian populations are novel. Validation of CNVs using Sequenom MassARRAY revealed extensive heterogeneity in CNV boundaries. Exploration of CNV profiles in such diverse populations would provide a widely valuable resource for understanding diversity in phenotypes and disease.

Complex Patterns of Genomic Admixture within Southern Africa

“…While we have previously alluded to the unlikely ancestral contribution of Han Chinese to the Coloured [17], we provide evidence for lack of the East Asian specific ‘dry earwax’ ( ABCC11 rs17822931-AA) [41] and the ‘alcohol-induced flush’ genotypes ( ALDH 2 rs671 A-allele) [42] in our study subjects. ADMIXTURE analysis [43] of our data merged with that published for 20 Indonesians [17] and 179 Indians [44]–[46] was used to establish significance based on ancestral representation in each individual within a population identifier assuming six population clusters with the separation of Han Chinese, Indonesian and Indian (Figure 5A and Table S13). The inclusion of these datasets however dramatically reduced the total number of available markers for interrogation to 3,725.…”

“…These include (i) published Affymetrix SNP6.0 data for 20 Indonesians representing 4 population identifiers, specifically Makassar, Bugi, Javanese and Batak Toba [17], and (ii) Affymetrix 50K genotype data supplied by the Indian Genome Variation Consortium for 179 Indians from 10 populations, selected based on geographical distribution, and as previously published [44]–[46]. Indian subjects were classified linguistically as Tibeto-Burman (TB), Austro-Asiatic (AA), Indo-European (IE) or Dravidian (DR), geographically as north (N), northeast (NE), central (C), east (E), west (W) or south (S), or by caste/religious group/tribe size as a large population (LP) or an isolated population (IP) [46]. Merging with the Indian dataset generated 9,317 overlapping autosomal markers, while including the Indonesian data generated 3,725 overlapping autosomal markers.…”

Comprehensive DNA copy number profile and BAC library construction of an Indian individual

“…In each gel, the vertical axis refers to the PCR amplicons loaded along with 100 bp DNA molecular weight marker denoted as M and control PCR product from human genomic DNA denoted as C, respectively. et al, 2010; Gautam et al, 2011;Kim et al, 2009;McKernan et al, 2009;Pang et al, 2010;Zhang et al, 2006). Findings from the overlap analysis with previously published datasets and personal genome resources showed a significant difference in the variation content with 221 CNV segments unique to IMBL4 genome.…”

Global patterns of large copy number variations in the human genome reveal complexity in chromosome organization

“…Both low and high resolution CNV studies have been performed across control population cohorts' samples since 2003, majorly covering Africa, America, Europe, China, Tibet, Taiwan, India, Germany and Finland (Lin et al, 2008;McElroy et al, 2009;Chen et al, 2011;Lou et al, 2011;Gautam et al, 2012;Zhang et al, 2012;Kanduri et al, 2013;Liu et al, 2013). In view of this, the rationale for conducting this study are as follows: (i) to identify the number, size and frequency of the CNVs in the genome; (ii) to construct a high resolution, regional as well as a global CNV map; (iii) to characterize CNVs in the imperative chromosomal structures, pseudoautosomal regions (PARs) and telomeres; (iv) to detect CNV hotspot regions in the chromosomes; (v) to identify the presence or absence of sex bias in distribution of CNVs; and (vi) to calculate the CNV inheritance and de novo mutation rate in family trios.…”