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Giulia C Kennedy

Publications and source records attributed to Giulia C Kennedy.

17 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↗
PubMed2006

An oligonucleotide microarray for high-throughput sequencing of the mitochondrial genome.

Previously we developed an oligonucleotide sequencing microarray (MitoChip) as an array-based sequencing platform for rapid and high-throughput analysis of mitochondrial DNA. The first generation MitoChip, however, was not tiled with probes for the noncoding D-loop region, a site frequently mutated in human cancers. Here we report the development of a second-generation MitoChip (v2.0) with oligonucleotide probes to sequence the entire mitochondrial genome. In addition, the MitoChip v2.0 contains redundant tiling of sequences for 500 of the most common haplotypes including single-nucleotide changes, insertions, and deletions. Sequencing results from 14 primary head and neck tumor tissues demonstrated that the v2.0 MitoChips detected a larger number of variants than the original version. Multiple coding region variants detected only in the second generation MitoChips, but not the earlier chip version, were further confirmed with conventional sequencing. Moreover, 31 variations in noncoding region were identified using MitoChips v2.0. Replicate experiments demonstrated >99.99% reproducibility in the second generation MitoChip. In seven head and neck cancer samples with matched lymphocyte DNA, the MitoChip v2.0 detected at least one cancer-associated mitochondrial mutation in four (57%) samples. These results indicate that the second generation MitoChip is a high-throughput platform for identification of mitochondrial DNA mutations in primary tumors.

Base Sequence↗
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↗
PubMed2005

A robust algorithm for copy number detection using high-density oligonucleotide single nucleotide polymorphism genotyping arrays.

We have developed a robust algorithm for copy number analysis of the human genome using high-density oligonucleotide microarrays containing 116,204 single-nucleotide polymorphisms. The advantages of this algorithm include the improvement of signal-to-noise (S/N) ratios and the use of an optimized reference. The raw S/N ratios were improved by accounting for the length and GC content of the PCR products using quadratic regressions. The use of constitutional DNA, when available, gives the lowest SD values (0.16 +/- 0.03) and also enables allele-based copy number detection in cancer genomes, which can unmask otherwise concealed allelic imbalances. In the absence of constitutional DNA, optimized selection of multiple normal references with the highest S/N ratios, in combination with the data regressions, dramatically improves SD values from 0.67 +/- 0.12 to 0.18 +/- 0.03. These improvements allow for highly reliable comparison of data across different experimental conditions, detection of allele-based copy number changes, and more accurate estimations of the range and magnitude of copy number aberrations. This algorithm has been implemented in a software package called Copy Number Analyzer for Affymetrix GeneChip Mapping 100K arrays (CNAG). Overall, these enhancements make CNAG a useful tool for high-resolution detection of copy number alterations which can help in the understanding of the pathogenesis of cancers and other diseases as well as in exploring the complexities of the human genome.

Algorithms↗
PubMed2005

Dynamic model based algorithms for screening and genotyping over 100 K SNPs on oligonucleotide microarrays.

MOTIVATION: A high density of single nucleotide polymorphism (SNP) coverage on the genome is desirable and often an essential requirement for population genetics studies. Region-specific or chromosome-specific linkage studies also benefit from the availability of as many high quality SNPs as possible. The availability of millions of SNPs from both Perlegen and the public domain and the development of an efficient microarray-based assay for genotyping SNPs has brought up some interesting analytical challenges. Effective methods for the selection of optimal subsets of SNPs spanning the genome and methods for accurately calling genotypes from probe hybridization patterns have enabled the development of a new microarray-based system for robustly genotyping over 100,000 SNPs per sample. RESULTS: We introduce a new dynamic model-based algorithm (DM) for screening over 3 million SNPs and genotyping over 100,000 SNPs. The model is based on four possible underlying states: Null, A, AB and B for each probe quartet. We calculate a probe-level log likelihood for each model and then select between the four competing models with an SNP-level statistical aggregation across multiple probe quartets to provide a high-quality genotype call along with a quality measure of the call. We assess performance with HapMap reference genotypes, informative Mendelian inheritance relationship in families, and consistency between DM and another genotype classification method. At a call rate of 95.91% the concordance with reference genotypes from the HapMap Project is 99.81% based on over 1.5 million genotypes, the Mendelian error rate is 0.018% based on 10 trios, and the consistency between DM and MPAM is 99.90% at a comparable rate of 97.18%. We also develop methods for SNP selection and optimal probe selection. AVAILABILITY: The DM algorithm is available in Affymetrix's Genotyping Tools software package and in Affymetrix's GDAS software package. See http://www.affymetrix.com for further information. 10 K and 100 K mapping array data are available on the Affymetrix website.

Algorithms↗
PubMed2005

Comparison of single-nucleotide polymorphisms and microsatellite markers for linkage analysis in the COGA and simulated data sets for Genetic Analysis Workshop 14: Presentation Groups 1, 2, and 3.

The papers in presentation groups 1-3 of Genetic Analysis Workshop 14 (GAW14) compared microsatellite (MS) markers and single-nucleotide polymorphism (SNP) markers for a variety of factors, using multiple methods in both data sets provided to GAW participants. Group 1 focused on data provided from the Collaborative Study on the Genetics of Alcoholism (COGA). Group 2 focused on data simulated for the workshop. Group 3 contained analyses of both data sets. Issues examined included: information content, signal strength, localization of the signal, use of haplotype blocks, population structure, power, type I error, control of type I error, the effect of linkage disequilibrium, and computational challenges. There were several broad resulting observations. 1) Information content was higher for dense SNP marker panels than for MS panels, and dense SNP markers sets appeared to provide slightly higher linkage scores and slightly higher power to detect linkage than MS markers. 2) Dense SNP panels also gave higher type I errors, suggesting that increased test thresholds may be needed to maintain the correct error rate. 3) Dense SNP panels provided better trait localization, but only in the COGA data, in which the MS markers were relatively loosely spaced. 4) The strength of linkage signals did not vary with the density of SNP panels, once the marker density was approximately 1 SNP/cM. 5) Analyses with SNPs were computationally challenging, and identified areas where improvements in analysis tools will be necessary to make analysis practical for widespread use.

Alcoholism↗
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↗
PubMed2004

Genotyping over 100,000 SNPs on a pair of oligonucleotide arrays.

We present a genotyping method for simultaneously scoring 116,204 SNPs using oligonucleotide arrays. At call rates >99%, reproducibility is >99.97% and accuracy, as measured by inheritance in trios and concordance with the HapMap Project, is >99.7%. Average intermarker distance is 23.6 kb, and 92% of the genome is within 100 kb of a SNP marker. Average heterozygosity is 0.30, with 105,511 SNPs having minor allele frequencies >5%.

Algorithms↗
PubMed2004

Parallel genotyping of over 10,000 SNPs using a one-primer assay on a high-density oligonucleotide array.

The analysis of single nucleotide polymorphisms (SNPs) is increasingly utilized to investigate the genetic causes of complex human diseases. Here we present a high-throughput genotyping platform that uses a one-primer assay to genotype over 10,000 SNPs per individual on a single oligonucleotide array. This approach uses restriction digestion to fractionate the genome, followed by amplification of a specific fractionated subset of the genome. The resulting reduction in genome complexity enables allele-specific hybridization to the array. The selection of SNPs was primarily determined by computer-predicted lengths of restriction fragments containing the SNPs, and was further driven by strict empirical measurements of accuracy, reproducibility, and average call rate, which we estimate to be >99.5%, >99.9%, and>95%, respectively [corrected]. With average heterozygosity of 0.38 and genome scan resolution of 0.31 cM, the SNP array is a viable alternative to panels of microsatellites (STRs). As a demonstration of the utility of the genotyping platform in whole-genome scans, we have replicated and refined a linkage region on chromosome 2p for chronic mucocutaneous candidiasis and thyroid disease, previously identified using a panel of microsatellite (STR) markers.

Alleles↗
PubMed2004

The genomic distribution of population substructure in four populations using 8,525 autosomal SNPs.

Understanding the nature of evolutionary relationships among persons and populations is important for the efficient application of genome science to biomedical research. We have analysed 8,525 autosomal single nucleotide polymorphisms (SNPs) in 84 individuals from four populations: African-American, European-American, Chinese and Japanese. Individual relationships were reconstructed using the allele sharing distance and the neighbour-joining tree making method. Trees show clear clustering according to population, with the root branching from the African-American clade. The African-American cluster is much less star-like than European-American and East Asian clusters, primarily because of admixture. Furthermore, on the East Asian branch, all ten Chinese individuals cluster together and all ten Japanese individuals cluster together. Using positional information, we demonstrate strong correlations between inter-marker distance and both locus-specific FST (the proportion of total variation due to differentiation) levels and branch lengths. Chromosomal maps of the distribution of locus-specific branch lengths were constructed by combining these data with other published SNP markers (total of 33,704 SNPs). These maps clearly illustrate a non-uniform distribution of human genetic substructure, an instructional and useful paradigm for education and research.

Genetics, Population↗
PubMed2003

Algorithms for large-scale genotyping microarrays.

MOTIVATION: Analysis of many thousands of single nucleotide polymorphisms (SNPs) across whole genome is crucial to efficiently map disease genes and understanding susceptibility to diseases, drug efficacy and side effects for different populations and individuals. High density oligonucleotide microarrays provide the possibility for such analysis with reasonable cost. Such analysis requires accurate, reliable methods for feature extraction, classification, statistical modeling and filtering. RESULTS: We propose the modified partitioning around medoids as a classification method for relative allele signals. We use the average silhouette width, separation and other quantities as quality measures for genotyping classification. We form robust statistical models based on the classification results and use these models to make genotype calls and calculate quality measures of calls. We apply our algorithms to several different genotyping microarrays. We use reference types, informative Mendelian relationship in families, and leave-one-out cross validation to verify our results. The concordance rates with the single base extension reference types are 99.36% for the SNPs on autosomes and 99.64% for the SNPs on sex chromosomes. The concordance of the leave-one-out test is over 99.5% and is 99.9% higher for AA, AB and BB cells. We also provide a method to determine the gender of a sample based on the heterozygous call rate of SNPs on the X chromosome. See http://www.affymetrix.com for further information. The microarray data will also be available from the Affymetrix web site. AVAILABILITY: The algorithms will be available commercially in the Affymetrix software package.

Algorithms↗
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↗
PubMed2002

Paternal origins of complete hydatidiform moles proven by whole genome single-nucleotide polymorphism haplotyping.

Complete hydatidiform moles (CHMs) are diploid tumors that result from fertilization of an empty ovum by a haploid 23,X sperm. In most cases, the resulting duplication of the genome gives rise to a 46,XX genotype and is thought to be androgenetic in origin. If this hypothesis is correct, then the genotypes of all polymorphic markers in CHMs should be homozygous. We used a dense set of single-nucleotide polymorphism (SNP) markers, evenly spaced throughout the genome, to definitively test this hypothesis. We genotyped genomic DNA samples from five CHMs and their corresponding maternal samples with 1494 SNP markers using high-density microarrays (HuSNP). As predicted, the maternal samples were heterozygous at >25% of the markers, which is consistent with the expected average heterozygosity of this panel of SNPs. In contrast, the five CHM samples were heterozygous at <0.75% of the SNP markers, which shows that these diploid tumors consist of a duplicated set of chromosomes. Because the CHM genotypes represent the haplotypes of their genomes, our results show that long-range haplotypes can be obtained easily with this resource and that a collection of such samples is a simple way to obtain reference haplotypes for association studies in various populations.

Female↗