104 research outputs found
Fast identification of biological pathways associated with a quantitative trait using group lasso with overlaps.
No full textWhere causal SNPs (single nucleotide polymorphisms) tend to accumulate within biological pathways, the incorporation of prior pathways information into a statistical model is expected to increase the power to detect true associations in a genetic association study. Most existing pathways-based methods rely on marginal SNP statistics and do not fully exploit the dependence patterns among SNPs within pathways.We use a sparse regression model, with SNPs grouped into pathways, to identify causal pathways associated with a quantitative trait. Notable features of our "pathways group lasso with adaptive weights" (P-GLAW) algorithm include the incorporation of all pathways in a single regression model, an adaptive pathway weighting procedure that accounts for factors biasing pathway selection, and the use of a bootstrap sampling procedure for the ranking of important pathways. P-GLAW takes account of the presence of overlapping pathways and uses a novel combination of techniques to optimise model estimation, making it fast to run, even on whole genome datasets.In a comparison study with an alternative pathways method based on univariate SNP statistics, our method demonstrates high sensitivity and specificity for the detection of important pathways, showing the greatest relative gains in performance where marginal SNP effect sizes are small
Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis
No full textMultiple sclerosis is a common disease of the central nervous system in which the interplay between inflammatory and neurodegenerative processes typically results in intermittent neurological disturbance followed by progressive accumulation of disability. Epidemiological studies have shown that genetic factors are primarily responsible for the substantially increased frequency of the disease seen in the relatives of affected individuals, and systematic attempts to identify linkage in multiplex families have confirmed that variation within the major histocompatibility complex (MHC) exerts the greatest individual effect on risk. Modestly powered genome-wide association studies (GWAS) have enabled more than 20 additional risk loci to be identified and have shown that multiple variants exerting modest individual effects have a key role in disease susceptibility. Most of the genetic architecture underlying susceptibility to the disease remains to be defined and is anticipated to require the analysis of sample sizes that are beyond the numbers currently available to individual research groups. In a collaborative GWAS involving 9,772 cases of European descent collected by 23 research groups working in 15 different countries, we have replicated almost all of the previously suggested associations and identified at least a further 29 novel susceptibility loci. Within the MHC we have refined the identity of the HLA-DRB1 risk alleles and confirmed that variation in the HLA-A gene underlies the independent protective effect attributable to the class I region. Immunologically relevant genes are significantly overrepresented among those mapping close to the identified loci and particularly implicate T-helper-cell differentiation in the pathogenesis of multiple sclerosis
A genome-wide scan for common alleles affecting risk for autism
No full textAlthough autism spectrum disorders (ASDs) have a substantial genetic basis, most of the known genetic risk has been traced to rare variants, principally copy number variants (CNVs). To identify common risk variation, the Autism Genome Project (AGP) Consortium genotyped 1558 rigorously defined ASD families for 1 million single-nucleotide polymorphisms (SNPs) and analyzed these SNP genotypes for association with ASD. In one of four primary association analyses, the association signal for marker rs4141463, located within MACROD2, crossed the genome-wide association significance threshold of P < 5 × 10−8. When a smaller replication sample was analyzed, the risk allele at rs4141463 was again over-transmitted; yet, consistent with the winner's curse, its effect size in the replication sample was much smaller; and, for the combined samples, the association signal barely fell below the P < 5 × 10−8 threshold. Exploratory analyses of phenotypic subtypes yielded no significant associations after correction for multiple testing. They did, however, yield strong signals within several genes, KIAA0564, PLD5, POU6F2, ST8SIA2 and TAF1C
Comparative genetic analysis of inflammatory bowel disease and type 1 diabetes implicates multiple loci with opposite effects
No full textInflammatory bowel disease, including Crohn's disease (CD) and ulcerative colitis (UC), and type 1 diabetes (T1D) are autoimmune diseases that may share common susceptibility pathways. We examined known susceptibility loci for these diseases in a cohort of 1689 CD cases, 777 UC cases, 989 T1D cases and 6197 shared control subjects of European ancestry, who were genotyped by the Illumina HumanHap550 SNP arrays. We identified multiple previously unreported or unconfirmed disease associations, including known CD loci (ICOSLG and TNFSF15) and T1D loci (TNFAIP3) that confer UC risk, known UC loci (HERC2 and IL26) that confer T1D risk and known UC loci (IL10 and CCNY) that confer CD risk. Additionally, we show that T1D risk alleles residing at the PTPN22, IL27, IL18RAP and IL10 loci protect against CD. Furthermore, the strongest risk alleles for T1D within the major histocompatibility complex (MHC) confer strong protection against CD and UC; however, given the multi-allelic nature of the MHC haplotypes, sequencing of the MHC locus will be required to interpret this observation. These results extend our current knowledge on genetic variants that predispose to autoimmunity, and suggest that many loci involved in autoimmunity may be under a balancing selection due to antagonistic pleiotropic effect. Our analysis implies that variants with opposite effects on different diseases may facilitate the maintenance of common susceptibility alleles in human populations, making autoimmune diseases especially amenable to genetic dissection by genome-wide association studies. © The Author 2010. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected]
Large Copy-Number Variations Are Enriched in Cases With Moderate to Extreme Obesity
No full text
OBJECTIVE
Obesity is an increasingly common disorder that predisposes to several medical conditions, including type 2 diabetes. We investigated whether large and rare copy-number variations (CNVs) differentiate moderate to extreme obesity from never-overweight control subjects.
RESEARCH DESIGN AND METHODS
Using single nucleotide polymorphism (SNP) arrays, we performed a genome-wide CNV survey on 430 obese case subjects (BMI &gt;35 kg/m2) and 379 never-overweight control subjects (BMI &lt;25 kg/m2). All subjects were of European ancestry and were genotyped on the Illumina HumanHap550 arrays with ∼550,000 SNP markers. The CNV calls were generated by PennCNV software.
RESULTS
CNVs &gt;1 Mb were found to be overrepresented in case versus control subjects (odds ratio [OR] = 1.5 [95% CI 0.5–5]), and CNVs &gt;2 Mb were present in 1.3% of the case subjects but were absent in control subjects (OR = infinity [95% CI 1.2–infinity]). When focusing on rare deletions that disrupt genes, even more pronounced effect sizes are observed (OR = 2.7 [95% CI 0.5–27.1] for CNVs &gt;1 Mb). Interestingly, obese case subjects who carry these large CNVs have moderately high BMI and do not appear to be extreme cases. Several CNVs disrupt known candidate genes for obesity, such as a 3.3-Mb deletion disrupting NAP1L5 and a 2.1-Mb deletion disrupting UCP1 and IL15.
CONCLUSIONS
Our results suggest that large CNVs, especially rare deletions, confer risk of obesity in patients with moderate obesity and that genes impacted by large CNVs represent intriguing candidates for obesity that warrant further study.
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Author Correction: Trans-ancestral genome-wide association study of longitudinal pubertal height growth and shared heritability with adult health outcomes.
No full textFollowing publication of the original article, the authors identified an error in the author name of Zhanna Balkhiyarova. The incorrect author name is: Zhanna Balkiyarova The correct author name is: Zhanna Balkhiyarova The author group has been updated above and the original article has been corrected
Additional file 1 of Trans-ancestral genome-wide association study of longitudinal pubertal height growth and shared heritability with adult health outcomes
No full text<b>External Organisations</b><br/>The Children's Hospital of Philadelphia; University of Pennsylvania; Imperial College London; McMaster University; Harvard University; University of Bristol; University of North Carolina at Chapel Hill; University of Southern California; National University of Singapore; Barcelona Institute for Global Health; University of Oulu; Vrije Universiteit Amsterdam; University of Valencia; National Institute on Aging; University of Helsinki; Tampere University Hospital; University of Oxford; Turku University Hospital; University of Copenhagen; University of London; Ludwig Maximilian University of Munich; Erasmus MC; University of Newcastle; Hunter Medical Research Institute; University College London; Instituto Mexicano del Seguro Social; Children's Hospital Los Angeles; Haverford College; University of Surrey; Cincinnati Children's Hospital Medical Center; University of Georgia; Singapore National Eye Center; Creighton University; University of San Carlos - Philippines; Tampere University; University of Turku; Pompeu Fabra University; Université de Lorraine; Columbia University; John Hunter Hospital; University of Cambridge; University of Hawai'i at Mānoa; Helmholtz Zentrum München - German Research Center for Environmental Health; Fundació Institut d'Investigació Sanitària Illes Balears; Center for Biomedical Research in Epidemiology and Public Health; South Australian Health And Medical Research Institute; University of Exeter<b>Associated Persons</b><br/>Carol A. Wang (Creator); Craig E. Pennell (Creator)Jonathan P. Bradfield (Creator); Rachel L. Kember (Creator); Anna Ulrich (Creator); Zhanna Balkiyarova (Creator); Akram Alyass (Creator); Izzuddin M. Aris (Creator); Joshua A. Bell (Creator); K. Alaine Broadaway (Creator); Zhanghua Chen (Creator); Jin-Fang Chai (Creator); Neil M. Davies (Creator); Dietmar Fernandez-Orth (Creator); Mariona Bustamante (Creator); Ruby Fore (Creator); Amitavo Ganguli (Creator); Anni Heiskala (Creator); Jouke-Jan Hottenga (Creator); Carmen Íñiguez (Creator); Sayuko Kobes (Creator); Jaakko Leinonen (Creator); Estelle Lowry (Creator); Leo-Pekka Lyytikainen (Creator); Anubha Mahajan (Creator); Niina Pitkänen (Creator); Theresia M. Schnurr (Creator); Christian Theil Have (Creator); David P. Strachan (Creator); Elisabeth Thiering (Creator); Suzanne Vogelezang (Creator); Kaitlin H. Wade (Creator); Andrew Wong (Creator); Louise Aas Holm (Creator); Alessandra Chesi (Creator); Miguel Cruz (Creator); Paul Elliott (Creator); Steve Franks (Creator); Christine Frithioff-Bøjsøe (Creator); W. James Gauderman (Creator); Joseph T. Glessner (Creator); Vicente Gilsanz (Creator); Kendra Griesman (Creator); Robert L. Hanson (Creator); Marika Kaakinen (Creator); Heidi Kalkwarf (Creator); Andrea Kelly (Creator); Joseph Kindler (Creator); Mika Kähönen (Creator); Carla Lanca (Creator); Joan Lappe (Creator); Nanette R. Lee (Creator); Shana McCormack (Creator); Frank D. Mentch (Creator); Jonathan A. Mitchell (Creator); Nina Mononen (Creator); Harri Niinikoski (Creator); Emily Oken (Creator); Katja Pahkala (Creator); Xueling Sim (Creator); Yik-Ying Teo (Creator); Leslie J. Baier (Creator); Toos van Beijsterveldt (Creator); Linda S. Adair (Creator); Dorret I. Boomsma (Creator); Eco de Geus (Creator); Mònica Guxens (Creator); Johan G. Eriksson (Creator); Janine F. Felix (Creator); Frank D. Gilliland (Creator); Penn Medicine Biobank (Creator); Torben Hansen (Creator); Rebecca Hardy (Creator); Marie-France Hivert (Creator); Jens-Christian Holm (Creator); Vincent W. V. Jaddoe (Creator); Marjo-Riitta Järvelin (Creator); Terho Lehtimäki (Creator); David Meyre (Creator); Karen L. Mohlke (Creator); Juha Mykkänen (Creator); Sharon Oberfield (Creator); John R. B. Perry (Creator); Olli Raitakari (Creator); Fernando Rivadeneira (Creator); Seang-Mei Saw (Creator); Sylvain Sebert (Creator); John A. Shepherd (Creator); Marie Standl (Creator); Thorkild I. A. Sørensen (Creator); Nicholas J. Timpson (Creator); Maties Torrent (Creator); Gonneke Willemsen (Creator); Elina Hypponen (Creator); Chris Power (Creator); Mark I. McCarthy (Creator); Rachel M. Freathy (Creator); Elisabeth Widén (Creator); Hakon Hakonarson (Creator); Inga Prokopenko (Creator); Benjamin F. Voight (Creator); Babette S. Zemel (Creator); Struan F. A. Grant (Creator); Diana L. Cousminer (Creator)Additional file 1: Table 1 and tables S1-S11
A Novel Susceptibility Locus for Type 1 Diabetes on Chr12q13 Identified by a Genome-Wide Association Study
No full textA genome-wide association study on obesity and obesity-related traits.
No full textLarge-scale genome-wide association studies (GWAS) have identified many loci associated with body mass index (BMI), but few studies focused on obesity as a binary trait. Here we report the results of a GWAS and candidate SNP genotyping study of obesity, including extremely obese cases and never overweight controls as well as families segregating extreme obesity and thinness. We first performed a GWAS on 520 cases (BMI>35 kg/m(2)) and 540 control subjects (BMI<25 kg/m(2)), on measures of obesity and obesity-related traits. We subsequently followed up obesity-associated signals by genotyping the top ∼500 SNPs from GWAS in the combined sample of cases, controls and family members totaling 2,256 individuals. For the binary trait of obesity, we found 16 genome-wide significant signals within the FTO gene (strongest signal at rs17817449, P = 2.5 × 10(-12)). We next examined obesity-related quantitative traits (such as total body weight, waist circumference and waist to hip ratio), and detected genome-wide significant signals between waist to hip ratio and NRXN3 (rs11624704, P = 2.67 × 10(-9)), previously associated with body weight and fat distribution. Our study demonstrated how a relatively small sample ascertained through extreme phenotypes can detect genuine associations in a GWAS
Copy number variation at 1q21.1 associated with neuroblastoma
No full textCommon copy number variations (CNVs) represent a significant source of genetic diversity, yet their influence on phenotypic variability, including disease susceptibility, remains poorly understood. To address this problem in human cancer, we performed a genome-wide association study of CNVs in the childhood cancer neuroblastoma, a disease in which single nucleotide polymorphism variations are known to influence susceptibility. We first genotyped 846 Caucasian neuroblastoma patients and 803 healthy Caucasian controls at 550,000 single nucleotide polymorphisms, and performed a CNV-based test for association. We then replicated significant observations in two independent sample sets comprised of a total of 595 cases and 3,357 controls. Here we describe the identification of a common CNV at chromosome 1q21.1 associated with neuroblastoma in the discovery set, which was confirmed in both replication sets. This CNV was validated by quantitative polymerase chain reaction, fluorescent in situ hybridization and analysis of matched tumour specimens, and was shown to be heritable in an independent set of 713 cancer-free parent-offspring trios. We identified a previously unknown transcript within the CNV that showed high sequence similarity to several neuroblastoma breakpoint family (NBPF) genes and represents a new member of this gene family (NBPF23). This transcript was preferentially expressed in fetal brain and fetal sympathetic nervous tissues, and the expression level was strictly correlated with CNV state in neuroblastoma cells. These data demonstrate that inherited copy number variation at 1q21.1 is associated with neuroblastoma and implicate a previously unknown neuroblastoma breakpoint family gene in early tumorigenesis of this childhood cancer. © 2009 Macmillan Publishers Limited
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