Identifying the risk of producing aneuploids using meiotic recombination genes as biomarkers: A copy number variation approach.

BACKGROUND & OBJECTIVES: Aneuploids are the most common chromosomal abnormality in liveborns and are usually the result of non-disjunction (NDJ) in meiosis. Copy number variations (CNVs) are large structural variations affecting the human genome. CNVs influence critical genes involved in causing NDJ by altering their copy number which affects the clinical outcome. In this study influence of CNVs on critical meiotic recombination was examined using new computational technologies to assess their role in causing aneuploidy. METHODS: This investigation was based on the analysis of 12 random normal populations consisting of 1714 individuals for aneuploid causing genes under CNV effect. To examine the effect of CNVs on genes causing aneuploidy, meiotic recombination genes were analyzed using EnrichR, WebGestalt and Ingenuity Pathway Analysis (IPA). RESULTS: Forty three NDJ genes were found under CNV burden; IPA (Ingenuity Pathway Analysis) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis of CNV in meiotic recombination genes revealed a significant role of breast cancer gene 1, amyloid protein precursor, mitogen-activated protein kinase and nerve growth factor as key molecular players involved in causing aneuploidy. Interaction between these genes with other CNV-overlapping genes involved in cell cycle, recombination and meiosis might lead to increased incidences of aneuploidy. INTERPRETATION & CONCLUSIONS: The findings of this study implied that the effect of CNVs on normal genome contributed in amplifying the occurrences of chromosomal aneuploidies. The normal individuals consisting of variations in the susceptible genes causing aneuploids in the population remain undetected until the disorder genes express in the succeeding generations.

Type 2 diabetes mellitus disease risk genes identified by genome wide copy number variation scan in normal populations.

AIMS: To identify the role of copy number variations (CNVs) on disease risk genes and its effect on disease phenotypes in type 2 diabetes mellitus (T2DM) in 12 random populations using high throughput arrays. METHODS: CNV analysis was carried out on a total of 1715 individuals from 12 populations, from ArrayExpress Archive of the European Bioinformatics Institute along with our subjects using Affymetrix Genome Wide SNP 6.0 array. CNV effect on T2DM genes were analyzed using several bioinformatics tools and a molecular protein interaction network was constructed to identify the disease mechanism altered by the CNVs. RESULTS: Analysis showed 34.4% of the total population to be under CNV burden for T2DM, with 83 disease causal and associated genes being under CNV influence. Hotspots were identified on chromosomes 22, 12, 6, 19 and 11.Overlap studies with case cohorts revealed significant disease risk genes such as EGFR, E2F1, PPP1R3A, HLA and TSPAN8. CONCLUSIONS: CNVs play a significant role in predisposing T2DM in normal cohorts and contribute to the phenotypic effects. Thus, CNVs should be considered as one of the major contributors in predisposition of the disease.

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

Global patterns of copy number variations (CNVs) in chromosomes are required to understand the dynamics of genome organization and complexity. For this study, analysis was performed using the Affymetrix Genome-Wide Human SNP Array 6.0 chip and CytoScan High-Density arrays. We identified a total of 44 109 CNVs from 1715 genomes with a mean of 25 CNVs in an individual, which established the first drafts of population-specific CNV maps providing a rationale for prioritizing chromosomal regions. About 19 905 ancient CNVs were identified across all chromosomes and populations at varying frequencies. CNV count, and sometimes CNV size, contributed to the bulk CNV size of the chromosome. Population specific lengthening and shortening of chromosomal length was observed. Sex bias for CNV presence was largely dependent on ethnicity. Lower CNV inheritance rate was observed for India, compared to YRI and CEU. A total of 33 candidate CNV hotspots from 5382 copy number (CN) variable region (CNVR) clusters were identified. Population specific CNV distribution patterns in p and q arms disturbed the assumption that CNV counts in the p arm are less common compared to long arms, and the CNV occurrence and distribution in chromosomes is length independent. This study unraveled the force of independent evolutionary dynamics on genome organization and complexity across chromosomes and populations.

Global spectrum of copy number variations reveals genome organizational plasticity and proposes new migration routes.

Global spectrum of CNVs is required to catalog variations to provide a high-resolution on the dynamics of genome-organization and human migration. In this study, we performed genome-wide genotyping using high-resolution arrays and identified 44,109 CNVs from 1,715 genomes across 12 populations. The study unraveled the force of independent evolutionary dynamics on genome-organizational plasticity across populations. We demonstrated the use of CNV tool to study human migration and identified a second major settlement establishing new migration routes in addition to existing ones.

Insertion-deletions burden in copy number polymorphisms of the Tibetan population.

BACKGROUND: Many studies have been conducted to identify either insertions-deletions (inDels) or copy number variations (CNVs) in humans, but few studies have been conducted to identify both of these forms coexisting in the same region. AIMS AND OBJECTIVES: To map the functionally significant sites within human genes that are likely to influence human traits and diseases. MATERIALS AND METHODS: In this report, we describe an inDel map in the 1051 Tibetan CNV regions obtained through CNV genotyping using Affymetrix Genome-wide single nucleotide polymorphism 6.0 chip. InDel polymorphisms in these copy number polymorphism regions were identified with a computational approach using the 2500 deoxyribonucleic acid sequences obtained from the 1000 Genome Project. RESULTS: The study identified a total of 95935 inDels that range from 1 bp to several bps in length which were found scattered across regulatory regions, exons and in introns of genes underlying the CNVs. A study on the distribution of inDels revealed that the majority of inDels were found in coding regions of the genome than the noncoding, while within the genes, inDels in intron regions were more followed by exonic regions and finally the regulatory regions. CONCLUSION: Study of inDels in CNV regions contribute to the enhanced understanding of the role played by the two variations and their collective influence on the genome. Further, a collection of these inDel genetic markers will aid in genetic mapping, further understanding of the phenotypic variability, identification of disease genes and in detecting novel CNVs.

Copy number variations burden on miRNA genes reveals layers of complexities involved in the regulation of pathways and phenotypic expression.

MicroRNAs are involved in post-transcriptional down-regulation of gene expression. Variations in miRNA genes can severely affect downstream-regulated genes and their pathways. However, population-specific burden of CNVs on miRNA genes and the complexities created towards the phenotype is not known. From a total of 44109 CNVs investigated from 1715 individuals across 12 populations using high-throughput arrays, 4007 miRNA-CNVs (∼ 9%) consisting 6542 (∼ 5%) miRNA genes with a total of 333 (∼ 5%) singleton miRNA genes were identified. We found miRNA-CNVs across the genomes of individuals showing multiple hits in many targets, co-regulated under the same pathway. This study proposes four mechanisms unraveling the many complexities in miRNA genes, targets and co-regulated miRNA genes towards establishment of phenotypic diversity.

Impact of copy number variations burden on coding genome in humans using integrated high resolution arrays.

Copy number variations (CNVs) alter the transcriptional and translational levels of genes by disrupting the coding structure and this burden of CNVs seems to be a significant contributor to phenotypic variations. Therefore it was necessary to assess the complexities of CNV burden on the coding genome. A total of 1715 individuals from 12 populations were used for CNV analysis in the present investigation. Analysis was performed using Affymetrix Genome-Wide Human SNP Array 6·0 chip and CytoScan High-Density arrays. CNVs were more frequently observed in the coding region than in the non-coding region. CNVs were observed vastly more frequently in the coding region than the non-coding region. CNVs were found to be enriched in the regions containing functional genes (83-96%) compared with the regions containing pseudogenes (4-17%). CNVs across the genome of an individual showed multiple hits across many genes, whose proteins interact physically and function under the same pathway. We identified varying numbers of proteins and degrees of interactions within protein complexes of single individual genomes. This study represents the first draft of a population-specific CNV genes map as well as a cross-populational map. The complex relationship of CNVs on genes and their physically interacting partners unravels many complexities involved in phenotype expression. This study identifies four mechanisms contributing to the complexities caused by the presence of multiple CNVs across many genes in the coding part of the genome.