The exploration of copy-number variation (CNV), notably of somatic cells, is an understudied aspect of genome biology. Any differences in the genetic makeup between twins derived from the same zygote represent an irrefutable example of somatic mosaicism. We studied 19 pairs of monozygotic twins with either concordant or discordant phenotype by using two platforms for genome-wide CNV analyses and showed that CNVs exist within pairs in both groups. These findings have an impact on our views of genotypic and phenotypic diversity in monozygotic twins and suggest that CNV analysis in phenotypically discordant monozygotic twins may provide a powerful tool for identifying disease-predisposition loci. Our results also imply that caution should be exercised when interpreting disease causality of de novo CNVs found in patients based on analysis of a single tissue in routine disease-related DNA diagnostics.
Two major types of genetic variation are known: single nucleotide polymorphisms (SNPs), and a more recently discovered structural variation, involving changes in copy number (CNVs) of kilobase- to megabase-sized chromosomal segments. It is unknown whether CNVs arise in somatic cells, but it is, however, generally assumed that normal cells are genetically identical. We tested 34 tissue samples from three subjects and, having analyzed for each tissue < or =10(-6) of all cells expected in an adult human, we observed at least six CNVs, affecting a single organ or one or more tissues of the same subject. The CNVs ranged from 82 to 176 kb, often encompassing known genes, potentially affecting gene function. Our results indicate that humans are commonly affected by somatic mosaicism for stochastic CNVs, which occur in a substantial fraction of cells. The majority of described CNVs were previously shown to be polymorphic between unrelated subjects, suggesting that some CNVs previously reported as germline might represent somatic events, since in most studies of this kind, only one tissue is typically examined and analysis of parents for the studied subjects is not routinely performed. A considerable number of human phenotypes are a consequence of a somatic process. Thus, our conclusions will be important for the delineation of genetic factors behind these phenotypes. Consequently, biobanks should consider sampling multiple tissues to better address mosaicism in the studies of somatic disorders.
To further explore the extent of structural large-scale variation in the human genome, we assessed copy number variations (CNVs) in a series of 71 healthy subjects from three ethnic groups. CNVs were analyzed using comparative genomic hybridization (CGH) to a BAC array covering the human genome, using DNA extracted from peripheral blood, thus avoiding any culture-induced rearrangements. By applying a newly developed computational algorithm based on Hidden Markov modeling, we identified 1,078 autosomal CNVs, including at least two neighboring/overlapping BACs, which represent 315 distinct regions. The average size of the sequence polymorphisms was approximately 350 kb and involved in total approximately 117 Mb or approximately 3.5% of the genome. Gains were about four times more common than deletions, and segmental duplications (SDs) were overrepresented, especially in larger deletion variants. This strengthens the notion that SDs often define hotspots of chromosomal rearrangements. Over 60% of the identified autosomal rearrangements match previously reported CNVs, recognized with various platforms. However, results from chromosome X do not agree well with the previously annotated CNVs. Furthermore, data from single BACs deviating in copy number suggest that our above estimate of total variation is conservative. This report contributes to the establishment of the common baseline for CNV, which is an important resource in human genetics.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.