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Manqiu Cao

Publications and source records attributed to Manqiu Cao.

8 recordsLinked to original sources
PubMed2006

Oligonucleotide microarray analysis of genomic imbalance in children with mental retardation.

The cause of mental retardation in one-third to one-half of all affected individuals is unknown. Microscopically detectable chromosomal abnormalities are the most frequently recognized cause, but gain or loss of chromosomal segments that are too small to be seen by conventional cytogenetic analysis has been found to be another important cause. Array-based methods offer a practical means of performing a high-resolution survey of the entire genome for submicroscopic copy-number variants. We studied 100 children with idiopathic mental retardation and normal results of standard chromosomal analysis, by use of whole-genome sampling analysis with Affymetrix GeneChip Human Mapping 100K arrays. We found de novo deletions as small as 178 kb in eight cases, de novo duplications as small as 1.1 Mb in two cases, and unsuspected mosaic trisomy 9 in another case. This technology can detect at least twice as many potentially pathogenic de novo copy-number variants as conventional cytogenetic analysis can in people with mental retardation.

Child↗
PubMed2006

A whole genome long-range haplotype (WGLRH) test for detecting imprints of positive selection in human populations.

MOTIVATION: The identification of signatures of positive selection can provide important insights into recent evolutionary history in human populations. Current methods mostly rely on allele frequency determination or focus on one or a small number of candidate chromosomal regions per study. With the availability of large-scale genotype data, efficient approaches for an unbiased whole genome scan are becoming necessary. METHODS: We have developed a new method, the whole genome long-range haplotype test (WGLRH), which uses genome-wide distributions to test for recent positive selection. Adapted from the long-range haplotype (LRH) test, the WGLRH test uses patterns of linkage disequilibrium (LD) to identify regions with extremely low historic recombination. Common haplotypes with significantly longer than expected ranges of LD given their frequencies are identified as putative signatures of recent positive selection. In addition, we have also determined the ancestral alleles of SNPs by genotyping chimpanzee and gorilla DNA, and have identified SNPs where the non-ancestral alleles have risen to extremely high frequencies in human populations, termed 'flipped SNPs'. Combining the haplotype test and the flipped SNPs determination, the WGLRH test serves as an unbiased genome-wide screen for regions under putative selection, and is potentially applicable to the study of other human populations. RESULTS: Using WGLRH and high-density oligonucleotide arrays interrogating 116 204 SNPs, we rapidly identified putative regions of positive selection in three populations (Asian, Caucasian, African-American), and extended these observations to a fourth population, Yoruba, with data obtained from the International HapMap consortium. We mapped significant regions to annotated genes. While some regions overlap with genes previously suggested to be under positive selection, many of the genes have not been previously implicated in natural selection and offer intriguing possibilities for further study. AVAILABILITY: the programs for the WGLRH algorithm are freely available and can be downloaded at http://www.affymetrix.com/support/supplement/WGLRH_program.zip.

Algorithms↗
PubMed2005

A genome-wide linkage analysis of alcoholism on microsatellite and single-nucleotide polymorphism data, using alcohol dependence phenotypes and electroencephalogram measures.

The Collaborative Study on the Genetics of Alcoholism (COGA) is a large-scale family study designed to identify genes that affect the risk for alcoholism and alcohol-related phenotypes. We performed genome-wide linkage analyses on the COGA data made available to participants in the Genetic Analysis Workshop 14 (GAW 14). The dataset comprised 1,350 participants from 143 families. The samples were analyzed on three technologies: microsatellites spaced at 10 cM, Affymetrix GeneChip Human Mapping 10 K Array (HMA10K) and Illumina SNP-based Linkage III Panel. We used ALDX1 and ALDX2, the COGA definitions of alcohol dependence, as well as electrophysiological measures TTTH1 and ECB21 to detect alcoholism susceptibility loci. Many chromosomal regions were found to be significant for each of the phenotypes at a p-value of 0.05. The most significant region for ALDX1 is on chromosome 7, with a maximum LOD score of 2.25 for Affymetrix SNPs, 1.97 for Illumina SNPs, and 1.72 for microsatellites. The same regions on chromosome 7 (96-106 cM) and 10 (149-176 cM) were found to be significant for both ALDX1 and ALDX2. A region on chromosome 7 (112-153 cM) and a region on chromosome 6 (169-185 cM) were identified as the most significant regions for TTTH1 and ECB21, respectively. We also performed linkage analysis on denser maps of markers by combining the SNPs datasets from Affymetrix and Illumina. Adding the microsatellite data to the combined SNP dataset improved the results only marginally. The results indicated that SNPs outperform microsatellites with the densest marker sets performing the best.

Alcoholism↗
PubMed2005

Description of the data from the Collaborative Study on the Genetics of Alcoholism (COGA) and single-nucleotide polymorphism genotyping for Genetic Analysis Workshop 14.

The data provided to the Genetic Analysis Workshop 14 (GAW 14) was the result of a collaboration among several different groups, catalyzed by Elizabeth Pugh from The Center for Inherited Disease Research (CIDR) and the organizers of GAW 14, Jean MacCluer and Laura Almasy. The DNA, phenotypic characterization, and microsatellite genomic survey were provided by the Collaborative Study on the Genetics of Alcoholism (COGA), a nine-site national collaboration funded by the National Institute of Alcohol and Alcoholism (NIAAA) and the National Institute of Drug Abuse (NIDA) with the overarching goal of identifying and characterizing genes that affect the susceptibility to develop alcohol dependence and related phenotypes. CIDR, Affymetrix, and Illumina provided single-nucleotide polymorphism genotyping of a large subset of the COGA subjects. This article briefly describes the dataset that was provided.

Alcoholism↗
PubMed2005

High-resolution identification of chromosomal abnormalities using oligonucleotide arrays containing 116,204 SNPs.

Mutation of the human genome ranges from single base-pair changes to whole-chromosome aneuploidy. Karyotyping, fluorescence in situ hybridization, and comparative genome hybridization are currently used to detect chromosome abnormalities of clinical significance. These methods, although powerful, suffer from limitations in speed, ease of use, and resolution, and they do not detect copy-neutral chromosomal aberrations--for example, uniparental disomy (UPD). We have developed a high-throughput approach for assessment of DNA copy-number changes, through use of high-density synthetic oligonucleotide arrays containing 116,204 single-nucleotide polymorphisms, spaced at an average distance of 23.6 kb across the genome. Using this approach, we analyzed samples that failed conventional karyotypic analysis, and we detected amplifications and deletions across a wide range of sizes (1.3-145.9 Mb), identified chromosomes containing anonymous chromatin, and used genotype data to determine the molecular origin of two cases of UPD. Furthermore, our data provided independent confirmation for a case that had been misinterpreted by karyotype analysis. The high resolution of our approach provides more-precise breakpoint mapping, which allows subtle phenotypic heterogeneity to be distinguished at a molecular level. The accurate genotype information provided on these arrays enables the identification of copy-neutral loss-of-heterozygosity events, and the minimal requirement of DNA (250 ng per array) allows rapid analysis of samples without the need for cell culture. This technology overcomes many limitations currently encountered in routine clinical diagnostic laboratories tasked with accurate and rapid diagnosis of chromosomal abnormalities.

Chromosome Aberrations↗
PubMed2004

Whole-genome scan, in a complex disease, using 11,245 single-nucleotide polymorphisms: comparison with microsatellites.

Despite the theoretical evidence of the utility of single-nucleotide polymorphisms (SNPs) for linkage analysis, no whole-genome scans of a complex disease have yet been published to directly compare SNPs with microsatellites. Here, we describe a whole-genome screen of 157 families with multiple cases of rheumatoid arthritis (RA), performed using 11,245 genomewide SNPs. The results were compared with those from a 10-cM microsatellite scan in the same cohort. The SNP analysis detected HLA*DRB1, the major RA susceptibility locus (P=.00004), with a linkage interval of 31 cM, compared with a 50-cM linkage interval detected by the microsatellite scan. In addition, four loci were detected at a nominal significance level (P<.05) in the SNP linkage analysis; these were not observed in the microsatellite scan. We demonstrate that variation in information content was the main factor contributing to observed differences in the two scans, with the SNPs providing significantly higher information content than the microsatellites. Reducing the number of SNPs in the marker set to 3,300 (1-cM spacing) caused several loci to drop below nominal significance levels, suggesting that decreases in information content can have significant effects on linkage results. In contrast, differences in maps employed in the analysis, the low detectable rate of genotyping error, and the presence of moderate linkage disequilibrium between markers did not significantly affect the results. We have demonstrated the utility of a dense SNP map for performing linkage analysis in a late-age-at-onset disease, where DNA from parents is not always available. The high SNP density allows loci to be defined more precisely and provides a partial scaffold for association studies, substantially reducing the resource requirement for gene-mapping studies.

Arthritis, Rheumatoid↗
PubMed2003

Large-scale genotyping of complex DNA.

Genetic studies aimed at understanding the molecular basis of complex human phenotypes require the genotyping of many thousands of single-nucleotide polymorphisms (SNPs) across large numbers of individuals. Public efforts have so far identified over two million common human SNPs; however, the scoring of these SNPs is labor-intensive and requires a substantial amount of automation. Here we describe a simple but effective approach, termed whole-genome sampling analysis (WGSA), for genotyping thousands of SNPs simultaneously in a complex DNA sample without locus-specific primers or automation. Our method amplifies highly reproducible fractions of the genome across multiple DNA samples and calls genotypes at >99% accuracy. We rapidly genotyped 14,548 SNPs in three different human populations and identified a subset of them with significant allele frequency differences between groups. We also determined the ancestral allele for 8,386 SNPs by genotyping chimpanzee and gorilla DNA. WGSA is highly scaleable and enables the creation of ultrahigh density SNP maps for use in genetic studies.

Algorithms↗