Copyright © 2007 The American Society of Human Genetics. All rights reserved.
The American Journal of Human Genetics, Volume 80, Issue 6, 1188-1193, 1 June 2007
doi:10.1086/518427
Report
Neil J. Rischa, b,
,
, Susan B. Bressmanc, d, Geetha Senthile and Laurie J. Ozeliuse
a Institute for Human Genetics and Department of Epidemiology and Biostatistics, University of California at San Francisco, San Francisco
b Division of Research, Kaiser Permanente, Oakland
c Department of Neurology, Beth Israel Medical Center, New York
d Departments of Neurology Bronx
e Departments of Molecular Genetics Bronx
f Albert Einstein College of Medicine, Bronx
Address for correspondence and reprints: Dr. Neil J. Risch, Institute for Human Genetics, University of California at San Francisco, 513 Parnassus Avenue, MS 0794, Room 901F HSW, San Francisco, CA 94143-0794.Abstract
A GAG deletion in the DYT1 gene is a major cause of early-onset dystonia, but clinical disease expression occurs in only 30% of mutation carriers. To gain insight into genetic factors that may influence penetrance, we evaluated three DYT1 single-nucleotide polymorphisms, including D216H, a coding-sequence variation that moderates the effects of the DYT1 GAG deletion in cellular models. We tested DYT1 GAG-deletion carriers with (n=119) and without (n=113) clinical signs of dystonia and control individuals (n=197) and found the frequency of the 216H allele to be increased in GAG-deletion carriers without dystonia and to be decreased in carriers with dystonia, compared with the control individuals. Analysis of haplotypes demonstrated a highly protective effect of the H allele in trans with the GAG deletion; there was also suggestive evidence that the D216 allele in cis is required for the disease to be penetrant. Our findings establish, for the first time, a clinically relevant gene modifier of DYT1.
| Family-Based Association Tests for Genomewide Association Scans The American Journal of Human Genetics, Volume 81, Issue 5, 1 November 2007, Pages 913-926 Wei-Min Chen and Gonçalo R. Abecasis Abstract With millions of single-nucleotide polymorphisms (SNPs) identified and characterized, genomewide association studies have begun to identify susceptibility genes for complex traits and diseases. These studies involve the characterization and analysis of very-high-resolution SNP genotype data for hundreds or thousands of individuals. We describe a computationally efficient approach to testing association between SNPs and quantitative phenotypes, which can be applied to whole-genome association scans. In addition to observed genotypes, our approach allows estimation of missing genotypes, resulting in substantial increases in power when genotyping resources are limited. We estimate missing genotypes probabilistically using the Lander-Green or Elston-Stewart algorithms and combine high-resolution SNP genotypes for a subset of individuals in each pedigree with sparser marker data for the remaining individuals. We show that power is increased whenever phenotype information for ungenotyped individuals is included in analyses and that high-density genotyping of just three carefully selected individuals in a nuclear family can recover >90% of the information available if every individual were genotyped, for a fraction of the cost and experimental effort. To aid in study design, we evaluate the power of strategies that genotype different subsets of individuals in each pedigree and make recommendations about which individuals should be genotyped at a high density. To illustrate our method, we performed genomewide association analysis for 27 gene-expression phenotypes in 3-generation families (Centre d'Etude du Polymorphisme Humain pedigrees), in which genotypes for ∼860,000 SNPs in 90 grandparents and parents are complemented by genotypes for ∼6,700 SNPs in a total of 168 individuals. In addition to increasing the evidence of association at 15 previously identified cis-acting associated alleles, our genotype-inference algorithm allowed us to identify associated alleles at 4 cis-acting loci that were missed when analysis was restricted to individuals with the high-density SNP data. Our genotype-inference algorithm and the proposed association tests are implemented in software that is available for free. Abstract | | |
| A Fast Method for Computing High-Significance Disease Association in Large Population-Based Studies The American Journal of Human Genetics, Volume 79, Issue 3, 1 September 2006, Pages 481-492 Gad Kimmel and Ron Shamir Abstract Because of rapid progress in genotyping techniques, many large-scale, genomewide disease-association studies are now under way. Typically, the disorders examined are multifactorial, and, therefore, researchers seeking association must consider interactions among loci and between loci and other factors. One of the challenges of large disease-association studies is obtaining accurate estimates of the significance of discovered associations. The linkage disequilibrium between SNPs makes the tests highly dependent, and dependency worsens when interactions are tested. The standard way of assigning significance (P value) is by a permutation test. Unfortunately, in large studies, it is prohibitively slow to compute low P values by this method. We present here a faster algorithm for accurately calculating low P values in case-control association studies. Unlike with several previous methods, we do not assume a specific distribution of the traits, given the genotypes. Our method is based on importance sampling and on accounting for the decay in linkage disequilibrium along the chromosome. The algorithm is dramatically faster than the standard permutation test. On data sets mimicking medium-to-large association studies, it speeds up computation by a factor of 5,000–100,000, sometimes reducing running times from years to minutes. Thus, our method significantly increases the problem-size range for which accurate, meaningful association results are attainable. Abstract | | |
| Classification of Human Chromosome 21 Gene-Expression Variations in Down Syndrome: Impact on Disease Phenotypes The American Journal of Human Genetics, Volume 81, Issue 3, 1 September 2007, Pages 475-491 E. Aït Yahya-Graison, J. Aubert, L. Dauphinot, I. Rivals, M. Prieur, G. Golfier, J. Rossier, L. Personnaz, N. Créau, H. Bléhaut, S. Robin, J.M. Delabar and M.-C. Potier Abstract Down syndrome caused by chromosome 21 trisomy is the most common genetic cause of mental retardation in humans. Disruption of the phenotype is thought to be the result of gene-dosage imbalance. Variations in chromosome 21 gene expression in Down syndrome were analyzed in lymphoblastoid cells derived from patients and control individuals. Of the 359 genes and predictions displayed on a specifically designed high-content chromosome 21 microarray, one-third were expressed in lymphoblastoid cells. We performed a mixed-model analysis of variance to find genes that are differentially expressed in Down syndrome independent of sex and interindividual variations. In addition, we identified genes with variations between Down syndrome and control samples that were significantly different from the gene-dosage effect (1.5). Microarray data were validated by quantitative polymerase chain reaction. We found that 29% of the expressed chromosome 21 transcripts are overexpressed in Down syndrome and correspond to either genes or open reading frames. Among these, 22% are increased proportional to the gene-dosage effect, and 7% are amplified. The other 71% of expressed sequences are either compensated (56%, with a large proportion of predicted genes and antisense transcripts) or highly variable among individuals (15%). Thus, most of the chromosome 21 transcripts are compensated for the gene-dosage effect. Overexpressed genes are likely to be involved in the Down syndrome phenotype, in contrast to the compensated genes. Highly variable genes could account for phenotypic variations observed in patients. Finally, we show that alternative transcripts belonging to the same gene are similarly regulated in Down syndrome but sense and antisense transcripts are not. Abstract | | |