PERMORY: an LD-exploiting permutation test algorithm for powerful genome-wide association testing

AbstractGenome-wide association studies (GWAS) have identified over 100 loci associated with schizophrenia. Most of these studies test genetic variants for association one at a time. In this study, we performed GWAS of the molecular genetics of schizophrenia (MGS) dataset with 5334 subjects using multivariate Bayesian variable selection (BVS) method Posterior Inference via Model Averaging and Subset Selection (piMASS) and compared our results with the previous univariate analysis of the MGS dataset. We showed that piMASS can improve the power of detecting schizophrenia-associated SNPs, potentially leading to new discoveries from existing data without increasing the sample size. We tested SNPs in groups to allow for local additive effects and used permutation test to determine statistical significance in order to compare our results with univariate method. The previous univariate analysis of the MGS dataset revealed no genome-wide significant loci. Using the same dataset, we identified a single region that exceeded the genome-wide significance. The result was replicated using an independent Swedish Schizophrenia Case–Control Study (SSCCS) dataset. Based on the SZGR 2.0 database we found 63 SNPs from the best performing regions that are mapped to 27 genes known to be associated with schizophrenia. Overall, we demonstrated that piMASS could discover association signals that otherwise would need a much larger sample size. Our study has important implication that reanalyzing published datasets with BVS methods like piMASS might have more power to discover new risk variants for many diseases without new sample collection, ascertainment, and genotyping.

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Enabling genome-wide association testing with multiple diseases and no healthy controls

Gene ◽

10.1016/j.gene.2018.10.047 ◽

2019 ◽

Vol 684 ◽

pp. 118-123

Author(s):

Jennifer Tom ◽

Diana Chang ◽

Art Wuster ◽

Kiran Mukhyala ◽

Karen Cuenco ◽

...

Keyword(s):

Genome Wide Association ◽

Healthy Controls ◽

Association Testing ◽

Genome Wide

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A sex-stratified genome-wide association study of tuberculosis using a multi-ethnic genotyping array

10.1101/405571 ◽

2018 ◽

Cited By ~ 2

Author(s):

Haiko Schurz ◽

Craig J Kinnear ◽

Chris Gignoux ◽

Genevieve Wojcik ◽

Paul D van Helden ◽

...

Keyword(s):

Association Study ◽

X Chromosome ◽

Complex Disease ◽

Genome Wide Association Study ◽

Interaction Analysis ◽

Genome Wide Association ◽

Genotyping Array ◽

Association Testing ◽

Genome Wide ◽

A Genome

AbstractTuberculosis (TB), caused by Mycobacterium tuberculosis, is a complex disease with a known human genetic component. Males seem to be more affected than females and in most countries the TB notification rate is twice as high in males as in females. While socio-economic status, behaviour and sex hormones influence the male bias they do not fully account for it. Males have only one copy of the X chromosome, while diploid females are subject to X chromosome inactivation. In addition, the X chromosome codes for many immune-related genes, supporting the hypothesis that X-linked genes could contribute to TB susceptibility in a sex-biased manner. We report the first TB susceptibility genome-wide association study (GWAS) with a specific focus on sex-stratified autosomal analysis and the X chromosome. Individuals from an admixed South African population were genotyped using the Illumina Multi Ethnic Genotyping Array, specifically designed as a suitable platform for diverse and admixed populations. Association testing was done on the autosome and X chromosome in a sex stratified and combined manner. SNP association testing was not statistically significant using a stringent cut-off for significance but revealed likely candidate genes that warrant further investigation. A genome wide interaction analysis detected 16 significant interactions. Finally, the results highlight the importance of sex-stratified analysis as strong sex-specific effects were identified on both the autosome and X chromosome.

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Exploiting Homoplasy in Genome-Wide Association Studies to Enhance Identification of Antibiotic-Resistance Mutations in Bacterial Genomes

Evolutionary Bioinformatics ◽

10.1177/1176934320944932 ◽

2020 ◽

Vol 16 ◽

pp. 117693432094493

Author(s):

Yi-Pin Lai ◽

Thomas R Ioerger

Keyword(s):

Linear Mixed Model ◽

Association Studies ◽

Genome Wide Association ◽

Clinical Population ◽

Genome Wide Association Studies ◽

Resistance Mutations ◽

Association Testing ◽

Hypervariable Regions ◽

Genome Wide ◽

Or Gene

Many antibacterial drugs have multiple mechanisms of resistance, which are often represented simultaneously by a mixture of resistance mutations (some more frequent than others) in a clinical population. This presents a challenge for Genome-Wide Association Studies (GWAS) methods, making it difficult to detect less prevalent resistance mechanisms purely through (weak) statistical associations. Homoplasy, or the occurrence of multiple independent mutations at the same site, is often observed with drug resistance mutations and can be a strong indicator of positive selection. However, traditional GWAS methods, such as those based on allele counting or linear regression, are not designed to take homoplasy into account. In this article, we present a new method, called ECAT (for Evolutionary Cluster-based Association Test), that extends traditional regression-based GWAS methods with the ability to take advantage of homoplasy. This is achieved through a preprocessing step which identifies hypervariable regions in the genome exhibiting statistically significant clusters of distinct evolutionary changes, to which association testing by a linear mixed model (LMM) is applied using GEMMA (a well-established LMM-based GWAS tool). Thus, the approach can be viewed as extending GEMMA from the usual site- or gene-level analysis to focusing on clustered regions of mutations. This approach was evaluated on a large collection of more than 600 clinical isolates of multidrug-resistant (MDR) Mycobacterium tuberculosis from Lima, Peru. We show that ECAT does a better job of detecting known resistance mutations for several antitubercular drugs (including less prevalent mutations with weaker associations), compared with (site- or gene-based) GEMMA, as representative of existing GWAS methods. The power of the multiphase approach in ECAT comes from focusing association testing on the hypervariable regions of the genome, which reduces complexity in the model and increases statistical power.

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Ancestry-specific association mapping in admixed populations

10.1101/014001 ◽

2015 ◽

Cited By ~ 4

Author(s):

Line Skotte ◽

Emil Jørsboe ◽

Thorfinn Sand Korneliussen ◽

Ida Moltke ◽

Anders Albrechtsen

Keyword(s):

Effect Size ◽

Genetic Variants ◽

Statistical Power ◽

Association Studies ◽

Causal Variant ◽

Genome Wide Association ◽

Reasonable Assumption ◽

Genome Wide Association Studies ◽

Association Testing ◽

Genome Wide

AbstractDuring the last decade genome–wide association studies have proven to be a powerful approach to identifying disease-causing variants. However, for admixed populations, most current methods for association testing are based on the assumption that the effect of a genetic variant is the same regardless of its ancestry. This is a reasonable assumption for a causal variant, but may not hold for the genetic variants that are tested in genome–wide association studies, which are usually not causal. The effects of non-causal genetic variants depend on how strongly their presence correlate with the presence of the causal variant, which may vary between ancestral populations because of different linkage disequilibrium patterns and allele frequencies.Motivated by this, we here introduce a new statistical method for association testing in recently admixed populations, where the effect size is allowed to depend on the ancestry of a given allele. Our method does not rely on accurate inference of local ancestry, yet using simulations we show that in some scenarios it gives a dramatic increase in statistical power to detect associations. In addition, the method allows for testing for difference in effect size between ancestral populations, which can be used to help determine if a SNP is causal. We demonstrate the usefulness of the method on data from the Greenlandic population.

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Nonlinear post-selection inference for genome-wide association studies

10.1101/2020.09.30.320515 ◽

2020 ◽

Author(s):

Lotfi Slim ◽

Clément Chatelain ◽

Chloé-Agathe Azencott

Keyword(s):

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Association Testing ◽

Testing Tools ◽

Disease Mechanisms ◽

Novel Approach ◽

Genome Wide ◽

Gene Level ◽

The Uk

AbstractAssociation testing in genome-wide association studies (GWAS) is often performed at either the SNP level or the gene level. The two levels can bring different insights into disease mechanisms. In the present work, we provide a novel approach based on nonlinear post-selection inference to bridge the gap between them. Our approach selects, within a gene, the SNPs or LD blocks most associated with the phenotype, before testing their combined effect. Both the selection and the association testing are conducted nonlinearly. We apply our tool to the study of BMI and its variation in the UK BioBank. In this study, our approach outperformed other gene-level association testing tools, with the unique benefit of pinpointing the causal SNPs.

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Accounting for population structure and relatedness in gene expression genome-wide association testing using a mixed-model approach

Protocol Exchange ◽

10.1038/nprot.2009.216 ◽

2009 ◽

Author(s):

Samantha Bateman ◽

Greg Gibson ◽

Youssef Idaghdour ◽

Wendy Czika ◽

Kelci Miclaus ◽

...

Keyword(s):

Gene Expression ◽

Population Structure ◽

Mixed Model ◽

Genome Wide Association ◽

Mixed Model Approach ◽

Association Testing ◽

Genome Wide ◽

Model Approach

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THU0001 GENOME-WIDE ASSOCIATION STUDY ON JOINT EROSIONS IN RHEUMATOID ARTHRITIS SUPPORTS DIFFERENTIAL PATHOLOGICAL MECHANISMS ACCORDING TO ANTI-CCP STATUS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2020-eular.3133 ◽

2020 ◽

Vol 79 (Suppl 1) ◽

pp. 213.2-213

Author(s):

A. Julià ◽

F. Blanco ◽

B. Fernandez ◽

A. Gonzalez ◽

J. D ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Genetic Variation ◽

Association Study ◽

Genome Wide Association Study ◽

Joint Damage ◽

Genome Wide Association ◽

Biological Processes ◽

Positive Group ◽

Association Testing ◽

Genome Wide

Background:Joint damage is the pathological hallmark of rheumatoid arthritis (RA). To identify the genetic variation associated with a higher level of erosions has proven elusive.Objectives:The objective of the present study was to perform a genome-wide association study on joint damage in a cohort of RA patients of the Spanish population. Our aims were to provide independent validation of previously reported variants and to identify new candidate risk loci. A stratified analysis was performed based on positivity to ACPA status.Methods:A total of 1,135 patients diagnosed with RA using the ACR-EULAR criteria recruited by the IMID Consortium were genotyped using a 550,000 single-nucleotide polymorphism array. Additional SNPs were imputed using the 1KG genome data. Joint damage was performed using the S-score, a simplified radiographic erosion score that has a high correlation with the Sharp-van der Hejde score (1). Association testing of SNPs with joint damage was performed via linear regression with the addition of the years of evolution as covariate. The two main components of genetic variation were also added to adjust for potential population stratification. A total of 50 SNPs representing previously reported loci associated with joint damage were selected. Genetic association was also performed at the pathway level using Pascal.Results:45 out of 50 SNPs representing 31 previously reported loci for joint damage could be satisfactorily imputed. Association testing of the whole patient cohort replicated the association withIL2RAandTRAF1. Of relevance, after stratifying for anti-CCP five new loci were replicated:KIF5AandSOSTin ACPA-positive RA andCD40, DKK1andTNFin ACPA-negative RA.IL2RAwas only significant in the ACPA-positive group andTRAF1was not significant in either strata. GWAS on the ACPA-positive cohort and on the ACPA-negative group identified n=7 and n=18 loci with P-values < 1x10-5, respectively. From these, however, only 1 SNP showed nominal significant association in the other patient group. Based on this evidence, we performed a pathway-based analysis to understand the biological mechanisms underlying this difference. Pathway analysis showed 52 biological processes associated with joint damage in ACPA-negative RA and 32 pathways in the ACPA-positive group, with only two shared biological processes between the two groups. Fc Gamma receptor mediated phagocytosis was the topmost biological process associated with erosions specifically in ACPA-negative RA and Signalling by Fibroblast Growth Factor mutants was the top process specific for ACPA-positive patients.Conclusion:The results from our study provide suggestive evidence that the genetic basis for joint damage is different according to the presence of ACPA. Replication of the new candidate loci in an independent patient cohort is underway.References:[1]Lopez-Lasanta, M., Julià, A., Maymó, J., Fernández-Gutierrez, B., Ureña-Garnica, I., Blanco, F. J., ... & Tornero, J. (2015). Variation at interleukin-6 receptor gene is associated to joint damage in rheumatoid arthritis.Arthritis research & therapy,17(1), 242.Disclosure of Interests:Antonio Julià: None declared, Francisco Blanco: None declared, Benjamin Fernandez: None declared, Antonio Gonzalez: None declared, Juan D: None declared, Joan Maymó: None declared, Mercedes Alperi-López: None declared, Alejandro Olive: None declared, Héctor Corominas Speakers bureau: Abbvie, Lilly, Pfizer, Roche, Victor Martinez Taboada: None declared, Isidoro González-Álvaro Grant/research support from: Roche Laboratories, Consultant of: Lilly, Sanofi, Paid instructor for: Lilly, Speakers bureau: Abbvie, MSD, Roche, Lilly, Antonio Fernandez-Nebro: None declared, Alba Erra: None declared, Simon Sánchez Fernandez: None declared, Núria Palau: None declared, Maria Lopez Lasanta: None declared, Adrià Aterido: None declared, Jesús Tornero: None declared, Sara Marsal: None declared

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Abstract 136: Genetics of White Matter Hyperintensity Burden in Patients With Ischemic Stroke: The MRI-GENIE Study

Stroke ◽

10.1161/str.48.suppl_1.136 ◽

2017 ◽

Vol 48 (suppl_1) ◽

Author(s):

Anne-Katrin Giese ◽

Huichun Xu ◽

Kathleen Ryan ◽

Markus D Schirmer ◽

Adrian V Dalca ◽

...

Keyword(s):

Ischemic Stroke ◽

White Matter ◽

Brain Mri ◽

Genome Wide Association ◽

European Ancestry ◽

White Matter Hyperintensity ◽

Chromosome 2 ◽

Association Testing ◽

Genome Wide ◽

Mri Scans

Introduction: The MRI-Genetics Interface Exploration (MRI-GENIE) study is the first international collaboration that aims to facilitate genetic discoveries in clinical cohorts of patients with acute ischemic stroke (AIS). We have amassed the largest-to-date collection of AIS cases with brain MRI scans and genome-wide genotyping to test the role of genetic susceptibility in MRI-based cerebrovascular traits. Objective/Hypothesis: To elucidate the genetic architecture of white matter hyperintensity (WMH) burden in AIS patients. Methods: Using a novel automated algorithm, we extracted WMH volume (WMHv) from clinical MRI scans of 2704 AIS patients (age 63.1 ± 14.7 years, 60.6% male) of European ancestry. Quality control (QC) measures were undertaken per subject and per SNP, excluding subjects with non-European ancestry and poor genotyping, as well as SNPs deviating from Hardy-Weinberg equilibrium and high levels of missingness. Imputation to the Haplotype Reference Consortium (HRC version r1.1) was conducted for 1712 remaining subjects with 2.8 million SNPs on the Michigan Imputation Server. After exclusion of poorly imputed SNPs (R 2 <0.5) and SNPs with minor allele frequency < 1%, 7.7 million SNPs remained for further analysis. Genome-wide association testing of natural log-transformed WMHv on the allelic dosage per SNP was adjusted for age, sex and principal components 1-10. Results: Genome-wide association testing has identified a novel locus on chromosome 2 (T allele at rs72856504) near the LDL Receptor related Protein 1B gene (LRP1B) that was significantly associated with WMHv burden in AIS (β=0.54, SE=0.098, p=3.65*10 -8 ). Conclusion: We have identified a novel locus (T allele rs72856504) on chromosome 2 near the LRP1B gene, which is specific for WMH in AIS and has not been previosuly described in stroke-free WMH cohorts. A replication effort involving additional independent cohorts of AIS patients with brain MRI and genome-wide genotyping is ongoing.

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