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Daisuke Komura

Publications and source records attributed to Daisuke Komura.

4 recordsLinked to original sources

Global variation in copy number in the human genome.

Copy number variation (CNV) of DNA sequences is functionally significant but has yet to be fully ascertained. We have constructed a first-generation CNV map of the human genome through the study of 270 individuals from four populations with ancestry in Europe, Africa or Asia (the HapMap collection). DNA from these individuals was screened for CNV using two complementary technologies: single-nucleotide polymorphism (SNP) genotyping arrays, and clone-based comparative genomic hybridization. A total of 1,447 copy number variable regions (CNVRs), which can encompass overlapping or adjacent gains or losses, covering 360 megabases (12% of the genome) were identified in these populations. These CNVRs contained hundreds of genes, disease loci, functional elements and segmental duplications. Notably, the CNVRs encompassed more nucleotide content per genome than SNPs, underscoring the importance of CNV in genetic diversity and evolution. The data obtained delineate linkage disequilibrium patterns for many CNVs, and reveal marked variation in copy number among populations. We also demonstrate the utility of this resource for genetic disease studies.

Chromosome Mapping↗

Genome-wide detection of human copy number variations using high-density DNA oligonucleotide arrays.

Recent reports indicate that copy number variations (CNVs) within the human genome contribute to nucleotide diversity to a larger extent than single nucleotide polymorphisms (SNPs). In addition, the contribution of CNVs to human disease susceptibility may be greater than previously expected, although a complete understanding of the phenotypic consequences of CNVs is incomplete. We have recently reported a comprehensive view of CNVs among 270 HapMap samples using high-density SNP genotyping arrays and BAC array CGH. In this report, we describe a novel algorithm using Affymetrix GeneChip Human Mapping 500K Early Access (500K EA) arrays that identified 1203 CNVs ranging in size from 960 bp to 3.4 Mb. The algorithm consists of three steps: (1) Intensity pre-processing to improve the resolution between pairwise comparisons by directly estimating the allele-specific affinity as well as to reduce signal noise by incorporating probe and target sequence characteristics via an improved version of the Genomic Imbalance Map (GIM) algorithm; (2) CNV extraction using an adapted SW-ARRAY procedure to automatically and robustly detect candidate CNV regions; and (3) copy number inference in which all pairwise comparisons are summarized to more precisely define CNV boundaries and accurately estimate CNV copy number. Independent testing of a subset of CNVs by quantitative PCR and mass spectrometry demonstrated a >90% verification rate. The use of high-resolution oligonucleotide arrays relative to other methods may allow more precise boundary information to be extracted, thereby enabling a more accurate analysis of the relationship between CNVs and other genomic features.

Algorithms↗

Noise reduction from genotyping microarrays using probe level information.

Genomic copy number change is one of the important phenomenon observed in cancer and other genetic disorders. Recently oligonucleotide microarrays have been used to analyze changes in the copy number. Although high density microarrays provide genome wide useful data on copy number, they are often associated with substantial amount of experimental noise that could affect the performance of the analyses. We used the high density oligonucleotide genotyping microarrays in our experiments that uses redundant probe tiling approach for individual SNPs. We found that the noise in the genotyping microarray data is associated with several experimental steps during target preparation and devised an algorithm that takes into account those experimental parameters. Additionally, defective probes that do not hybridize well to the target and therefore could not be modified inherently were detected and omitted automatically by using the algorithm. When we applied the algorithm to actual datasets, we could reduce the noise substantially without compressing the dynamic range. Additionally, combinatorial use of our noise reduction algorithm and conventional breakpoint detection algorithm successfully detected a microamplification of c-myc which was overlooked in the raw data. The algorithm described here is freely available with the software upon request to all non-profit researchers.

Adenocarcinoma↗

Allelic dosage analysis with genotyping microarrays.

Genomic alternations, including dosage and allelic imbalance, constitute a major basis of neoplastic and other genetic disorders. Using oligonucleotide genotyping microarrays, here we report the development and usage of an algorithm, called genome imbalance map (GIM) algorithm, for allelic as well as total gene dosage analysis. Using the GIM algorithm, global genome imbalance status at over 100,000 loci was simultaneously analyzed with unprecedented accuracy and allelic discrimination.

Algorithms↗