IMSGC Genome Wide Association Study of Multiple Sclerosis
Ontology highlight
ABSTRACT: The goal of this study is to perform a comprehensive allelic and genotypic association analysis of the entire human genome
in multiple sclerosis. The recent definitive linkage genome screen demonstrated that there is no other MS risk gene with an
effect size anywhere near that of the MHC. However, linkage analysis is significantly hampered by reduced power in the face
of heterogeneity and requires multiplex families, which also hampers acquiring an appropriate sample size. In contrast,
genotyping 500K SNPs allows us to survey a significant amount of the genome (we estimate >70%) directly for allelic
or genotypic association. This uses the improved power of association analysis and can also take advantage
of the linkage disequilibrium relationships among SNPs to further increase power (e.g. haplotype analysis). Quality control
and data analysis are significant challenges. We will initially perform substantial QC checks and analyze the data using both
TDT and AFBAC approaches. Multigenic interactions will also be tested using MDR.
SECONDARY ACCESSION(S): PRJNA75427PRJNA75425
REPOSITORIES: dbGaP
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| README.txt | Txt | |||
| phs000139.IMSGC.analysis-NCBI.tar.gz | Other | |||
| phs000139.pha002854.txt.gz | Txt | |||
| 00readme.txt | Txt | |||
| GapExchange_phs000139.v1.p1.xml | Xml |
Items per page: 5 1 - 5 of 34 |
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Project description:<p>The goal of this study is to perform a comprehensive allelic and genotypic association analysis of the entire human genome
in multiple sclerosis. The recent definitive linkage genome screen demonstrated that there is no other MS risk gene with an
effect size anywhere near that of the MHC. However, linkage analysis is significantly hampered by reduced power in the face
of heterogeneity and requires multiplex families, which also hampers acquiring an appropriate sample size. In contrast,
genotyping 500K SNPs allows us to survey a significant amount of the genome (we estimate >70%) directly for allelic
or genotypic association. This uses the improved power of association analysis and can also take advantage
of the linkage disequilibrium relationships among SNPs to further increase power (e.g. haplotype analysis). Quality control
and data analysis are significant challenges. We will initially perform substantial QC checks and analyze the data using both
TDT and AFBAC approaches. Multigenic interactions will also be tested using MDR.</p>
| phs000139.v1.p1 | EGA
Project description:The remarkable success observed in using genome-wide association (GWA) mapping in human cohorts to identify multiple genes linked to a wide number of traits related to complex diseases has renewed interest in applying genome-wide association mapping techniques to model organisms such as inbred laboratory mice. However, unlike humans, the limited genetic diversity present in the ancestry of laboratory mice combined with intense selection pressure over the past decades have yielded an intricate population structure within the genomes of laboratory mouse that could potentially complicate the results obtained from such a study. We sought to empirically assess the viability of genome-wide association studies in inbred mice using hundreds of expression traits where the true location of the eQTL is known a priori. Using data from a previously published experimental mouse cross (C57BL/6J x C3H/HeJ), we selected over a thousand of the strongest cis-acting expression QTLs and measured transcript abundance levels of the associated expression traits in 16 classical and 3 wild-derived inbred strains. We next perform a genome-wide association scan demonstrating the low statistical power of such studies and show empirically the large extent to which high allelic association gives rise to spurious associations. Moreover, we provide evidence illustrating that in a large fraction of cases, the marker with the most significant p-values fails to map to the location of the true eQTL; hence, as a result, selecting the most significant marker may lead to spurious findings. Finally, we demonstrate that combining linkage analysis with association mapping provides significant increases in statistical power over a stand-alone GWA study as well as significantly higher mapping resolution than either study alone.
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Project description:The identification of multiple signals at individual loci could explain additional phenotypic variance ('missing heritability') of common traits, and help identify causal genes. We examined gene expression levels as a model trait because of the large number of strong genetic effects acting in cis. Using expression profiles from 613 individuals, we performed genome-wide single nucleotide polymorphism (SNP) analyses to identify cis-expression quantitative trait loci (eQTLs), and conditional analysis to identify second signals. We examined patterns of association when accounting for multiple SNPs at a locus and when including additional SNPs from the 1000 Genomes Project. We identified 1298 cis-eQTLs at an approximate false discovery rate 0.01, of which 118 (9%) showed evidence of a second independent signal. For this subset of 118 traits, accounting for two signals resulted in an average 31% increase in phenotypic variance explained (Wilcoxon P< 0.0001). The association of SNPs with cis gene expression could increase, stay similar or decrease in significance when accounting for linkage disequilibrium with second signals at the same locus. Pairs of SNPs increasing in significance tended to have gene expression increasing alleles on opposite haplotypes, whereas pairs of SNPs decreasing in significance tended to have gene expression increasing alleles on the same haplotypes. Adding data from the 1000 Genomes Project showed that apparently independent signals could be potentially explained by a single association signal. Our results show that accounting for multiple variants at a locus will increase the variance explained in a substantial fraction of loci, but that allelic heterogeneity will be difficult to define without resequencing loci and functional work. Gene expression data was determined of peripheral blood samples (n=705) from InChianti cohort.
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Project description:Body weight (BW) is a critical economic trait for meat production in sheep. The current study aimed to perform a genome-wide association study (GWAS) to detect significant single-nucleotide polymorphisms (SNPs) that are associated with BW in Hu sheep.
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Project description:The identification of multiple signals at individual loci could explain additional phenotypic variance ('missing heritability') of common traits, and help identify causal genes. We examined gene expression levels as a model trait because of the large number of strong genetic effects acting in cis. Using expression profiles from 613 individuals, we performed genome-wide single nucleotide polymorphism (SNP) analyses to identify cis-expression quantitative trait loci (eQTLs), and conditional analysis to identify second signals. We examined patterns of association when accounting for multiple SNPs at a locus and when including additional SNPs from the 1000 Genomes Project. We identified 1298 cis-eQTLs at an approximate false discovery rate 0.01, of which 118 (9%) showed evidence of a second independent signal. For this subset of 118 traits, accounting for two signals resulted in an average 31% increase in phenotypic variance explained (Wilcoxon P< 0.0001). The association of SNPs with cis gene expression could increase, stay similar or decrease in significance when accounting for linkage disequilibrium with second signals at the same locus. Pairs of SNPs increasing in significance tended to have gene expression increasing alleles on opposite haplotypes, whereas pairs of SNPs decreasing in significance tended to have gene expression increasing alleles on the same haplotypes. Adding data from the 1000 Genomes Project showed that apparently independent signals could be potentially explained by a single association signal. Our results show that accounting for multiple variants at a locus will increase the variance explained in a substantial fraction of loci, but that allelic heterogeneity will be difficult to define without resequencing loci and functional work.
2013-06-21 | GSE48152 | GEO
Project description:Genome-wide association study (GWAS) was performed in 120 patient-parents trio samples from Japanese schizophrenia pedigrees ABSTRACT: Schizophrenia is a devastating neuropsychiatric disorder with genetically complex traits. Genetic variants should explain a considerable portion of the risk for schizophrenia, and genome-wide association study (GWAS) is a potentially powerful tool for identifying the risk variants that underlie the disease. Here, we report the results of a three-stage analysis of three independent cohorts consisting of a total of 2,535 samples from Japanese and Chinese populations for searching schizophrenia susceptibility genes using a GWAS approach. Firstly, we examined 115,770 single nucleotide polymorphisms (SNPs) in 120 patient-parents trio samples from Japanese schizophrenia pedigrees. In stage II, we evaluated 1,632 SNPs (1,159 SNPs of p < 0.01 and 473 SNPs of p < 0.05 that located in previously reported linkage regions). The second sample consisted of 1,012 case-control samples of Japanese origin. The most significant p value was obtained for the SNP in the ELAVL2 [(embryonic lethal, abnormal vision, Drosophila)-like 2] gene located on 9p21.3 (p = 0.00087). In stage III, we scrutinized the ELAVL2 gene by genotyping gene-centric tagSNPs in the third sample set of 293 family samples (1,163 individuals) of Chinese descent and the SNP in the gene showed a nominal association with schizophrenia in Chinese population (p = 0.026). The current data in Asian population would be helpful for deciphering ethnic diversity of schizophrenia etiology. Affymetrix SNP arrays were performed according to the manufacturer's directions on DNA extracted from peripheral blood samples. SUPPLEMENTARY FILES: CEL files were processed by manufacture's protocol. Genotype data were analyzed with the GeneSpring GT (Varia) 2.0 software package developed by Agilent Technologies (Santa Clara, CA). Matrix tables for the Genetic programs were produced (Linkage format: http://bioinformatics.med.utah.edu/~alun/software/docs/linkage.html) File 1: Matrix file_SNP_Map.txt File 2: Matrix file_Family_Information.txt File 3: Matrix file_Pedigree_Format.txt (Genotyping data of Linkage format) Transmission disequilibrium test was performed using the R program (http://www.r-project.org). File 4: Matrix file_results.txt
2011-06-13 | E-GEOD-27923 | biostudies-arrayexpress
Project description:The remarkable success observed in using genome-wide association (GWA) mapping in human cohorts to identify multiple genes linked to a wide number of traits related to complex diseases has renewed interest in applying genome-wide association mapping techniques to model organisms such as inbred laboratory mice. However, unlike humans, the limited genetic diversity present in the ancestry of laboratory mice combined with intense selection pressure over the past decades have yielded an intricate population structure within the genomes of laboratory mouse that could potentially complicate the results obtained from such a study. We sought to empirically assess the viability of genome-wide association studies in inbred mice using hundreds of expression traits where the true location of the eQTL is known a priori. Using data from a previously published experimental mouse cross (C57BL/6J x C3H/HeJ), we selected over a thousand of the strongest cis-acting expression QTLs and measured transcript abundance levels of the associated expression traits in 16 classical and 3 wild-derived inbred strains. We next perform a genome-wide association scan demonstrating the low statistical power of such studies and show empirically the large extent to which high allelic association gives rise to spurious associations. Moreover, we provide evidence illustrating that in a large fraction of cases, the marker with the most significant p-values fails to map to the location of the true eQTL; hence, as a result, selecting the most significant marker may lead to spurious findings. Finally, we demonstrate that combining linkage analysis with association mapping provides significant increases in statistical power over a stand-alone GWA study as well as significantly higher mapping resolution than either study alone. RNA preparation and array hybridizations were performed at Rosetta Inpharmatics. The custom ink-jet microarrays were manufactured by Agilent Technologies (Palo Alto, CA). A custom array was designed for this study and consisted of 39,280 non-control oligonuceotides extracted from the mouse Unigene clusters and combined with RefSeq sequences and RIKEN full-length cDNA clones. Mouse liver tissues were homogenized and total RNA extracted using Trizol reagent (Invitrogen, CA) according to manufacturer’s protocol. Three µg of total RNA was reverse transcribed and labeled with either Cy3 or Cy5 fluorochrome. Labeled complementary RNA (cRNA) from each animal was hybridized against a cross-specific pool of labeled cRNAs constructed from equal aliquots of RNA from representative animals for each strain. The hybridizations were performed in fluor reversal for 24 hours in a hybridization chamber, washed, and scanned using a confocal laser scanner. Arrays were quantified on the basis of spot intensity relative to background, adjusted for experimental variation between arrays using average intensity over multiple channels, and fitted to a previously described error model to determine significance23 (type I error).
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2007-08-08 | GSE7952 | GEO
Project description:We have explored the potential role of genetics in the development of osteonecrosis of the jaw (ONJ) in multiple myeloma (MM) patients under bisphosphonate therapy. A genome wide association study was performed using 500.568 single nucleotide polymorphisms (SNPs) in two series of homogeneously treated MM patients: one with ONJ (22 MM cases) and another without ONJ (65 matched MM controls). Four SNPs (rs1934951, rs1934980, rs1341162 and rs17110453) mapped within the Cytochrome P450-2C gene (CYP2C8) showed a different distribution between cases and controls with statistically significant differences (p=1.07x10-6, p=4.231x10-6, p=6.22x10-6 and p=2.15x10-5, respectively). SNP rs1934951 was significantly associated with a higher risk of ONJ development even after Bonferroni correction (P corrected value=0.02). Genotyping results displayed an overrepresentation of the T allele in cases as compared with controls (48% vs. 12%). Thus, individuals homozygous for the T allele had an increased likelihood of developing ONJ (Odds ratio 12.75, 95% confidence interval 3.7 to 43.5). We studied 22 cases (MM with ONJ) and 65 controls (MM without ONJ), matched for age, gender and ethnicity (note: all of the cases and controls are Caucasian). All patients were enrolled in the GEM-00 protocol, which consists on polychemotherapy and autologous transplantation. All received BPs therapy, either Pamidronate (16 cases, 57 controls) or Zoledronic Acid (6 cases, 8 controls) planned for 2 years (median 22 months, range 9-24 months). Clinical characteristics were similar between controls and cases. Study protocols were approved by the ethics committee and written informed consent was obtained from all participants. Each patient was genotyped using the Affymetrix GeneChip Mapping 500K set of microarrays (Affymetrix, Santa Clara, CA) according to the manufacturer’s recommendations. Genotypes were determined using the BRLMM algorithm with cases and controls undergoing joint cluster analysis, after ensuring a robust association test through quality filtering tests. Based on stringent quality control criteria a total of 339.972 SNPs were selected for subsequent analyses. Criteria for exclusion were: 1) call rate<90%, 2) minor allele frequency <5% and 3) deviations from Hardy-Weinberg equilibrium with a p<0.00001. Sexual chromosomes were excluded for analysis. To test for allelic associations between SNPs and ONJ, we constructed 2x2 contingency tables and compared using the two-sided Fisher’s exact or Chisquare tests through SPSS software (SPSS 14.0, Inc. Chicago, IL, USA). P-values were corrected (Pc) using the Bonferroni correction. The strength of association was estimated by the odds ratio (OR), and their 95% confidence intervals (CI) were calculated by Cornfield methods. Linkage disequilibrium between SNPs was analyzed using the Arlequin Software (http://anthro.unige.ch/arlequin).
2008-09-25 | E-GEOD-11948 | biostudies-arrayexpress