scholarly journals Whole-exome SNP array identifies 15 new susceptibility loci for psoriasis

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Xianbo Zuo ◽  
Liangdan Sun ◽  
Xianyong Yin ◽  
Jinping Gao ◽  
Yujun Sheng ◽  
...  
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Snp Array ◽  
Genome Wide Association Studies ◽  
Susceptibility Loci ◽  
Array Analysis ◽  
New Genes ◽  
Genome Wide ◽  
Whole Exome ◽  
Coding Variants

Abstract Genome-wide association studies (GWASs) have reproducibly associated ∼40 susceptibility loci with psoriasis. However, the missing heritability is evident and the contributions of coding variants have not yet been systematically evaluated. Here, we present a large-scale whole-exome array analysis for psoriasis consisting of 42,760 individuals. We discover 16 SNPs within 15 new genes/loci associated with psoriasis, including C1orf141, ZNF683, TMC6, AIM2, IL1RL1, CASR, SON, ZFYVE16, MTHFR, CCDC129, ZNF143, AP5B1, SYNE2, IFNGR2 and 3q26.2-q27 (P<5.00 × 10−08). In addition, we also replicate four known susceptibility loci TNIP1, NFKBIA, IL12B and LCE3D–LCE3E. These susceptibility variants identified in the current study collectively account for 1.9% of the psoriasis heritability. The variant within AIM2 is predicted to impact protein structure. Our findings increase the number of genetic risk factors for psoriasis and highlight new and plausible biological pathways in psoriasis.

Identification of six novel susceptibility loci for dyslipidemia by longitudinal exome-wide association studies in Japanese

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
M Oguri ◽  
K Kato ◽  
H Horibe ◽  
T Fujimaki ◽  
J Sakuma ◽  
...  
Keyword(s):  
Serum Lipid ◽  
Ldl Cholesterol ◽  
Association Studies ◽  
Density Lipoprotein ◽  
Hdl Cholesterol ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Susceptibility Loci ◽  
Cross Sectional ◽  
Genome Wide

Abstract Background The circulating concentrations of triglycerides, high density lipoprotein (HDL)-cholesterol, and low density lipoprotein (LDL)-cholesterol have a substantial genetic component. Although previous genome-wide association studies identified various genes and loci related to plasma lipid levels, those studies were conducted in a cross-sectional manner. Purpose The purpose of the study was to identify genetic variants that confer susceptibility to hypertriglyceridemia, hypo-HDL-cholesterolemia, and hyper-LDL-cholesterolemia in Japanese. We have now performed longitudinal exome-wide association studies (EWASs) to identify novel loci for dyslipidemia by examining temporal changes in serum lipid profiles. Methods Longitudinal EWASs (mean follow-up period, 5 years) for hypertriglyceridemia (2056 case, 3966 controls), hypo-HDL-cholesterolemia (698 cases, 5324 controls), and hyper-LDL-cholesterolemia (2769 cases, 3251 controls) were performed with Illumina Human Exome arrays. The relation of genotypes of 24,691 single nucleotide polymorphisms (SNPs) that passed quality control to dyslipidemia-related traits was examined with the generalized estimating equation (GEE). To compensate for multiple comparisons of genotypes with each of the three conditions, we applied Bonferroni's correction for statistical significance of association. Replication studies with cross-sectional data were performed for hypertriglyceridemia (2685 cases, 4703 controls), hypo-HDL-cholesterolemia (1947 cases, 6146 controls), and hyper-LDL-cholesterolemia (1719 cases, 5833 controls). Results Longitudinal EWASs revealed that 30 SNPs were significantly (P&lt;2.03 × 10–6 by GEE) associated with hypertriglyceridemia, 46 SNPs with hypo-HDL-cholesterolemia, and 25 SNPs with hyper-LDL-cholesterolemia. After examination of the relation of identified SNPs to serum lipid profiles, linkage disequilibrium, and results of the previous genome-wide association studies, we newly identified rs74416240 of TCHP, rs925368 of GIT2, rs7969300 of ATXN2, and rs12231744 of NAA25 as a susceptibility loci for hypo-HDL-cholesterolemia; and rs34902660 of SLC17A3 and rs1042127 of CDSN for hyper-LDL-cholesterolemia. These SNPs were not in linkage disequilibrium with those previously reported to be associated with dyslipidemia, indicating independent effects of the SNPs identified in the present study on serum concentrations of HDL-cholesterol or LDL-cholesterol in Japanese. According to allele frequency data from the 1000 Genomes project database, five of the six identified SNPs were monomorphic or rare variants in European populations. In the replication study, all six SNPs were associated with dyslipidemia-related phenotypes. Conclusion We have thus identified six novel loci that confer susceptibility to hypo-HDL-cholesterolemia or hyper-LDL-cholesterolemia. Determination of genotypes for these SNPs at these loci may prove informative for assessment of the genetic risk for dyslipidemia in Japanese. Funding Acknowledgement Type of funding source: None

scholarly journals GWASpro: a high-performance genome-wide association analysis server

2018 ◽  
Vol 35 (14) ◽  
pp. 2512-2514 ◽  
Author(s):  
Bongsong Kim ◽  
Xinbin Dai ◽  
Wenchao Zhang ◽  
Zhaohong Zhuang ◽  
Darlene L Sanchez ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Linear Mixed Model ◽  
Association Studies ◽  
Learning Curves ◽  
Experimental Designs ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Genome Wide

Abstract Summary We present GWASpro, a high-performance web server for the analyses of large-scale genome-wide association studies (GWAS). GWASpro was developed to provide data analyses for large-scale molecular genetic data, coupled with complex replicated experimental designs such as found in plant science investigations and to overcome the steep learning curves of existing GWAS software tools. GWASpro supports building complex design matrices, by which complex experimental designs that may include replications, treatments, locations and times, can be accounted for in the linear mixed model. GWASpro is optimized to handle GWAS data that may consist of up to 10 million markers and 10 000 samples from replicable lines or hybrids. GWASpro provides an interface that significantly reduces the learning curve for new GWAS investigators. Availability and implementation GWASpro is freely available at https://bioinfo.noble.org/GWASPRO. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A description of large-scale metabolomics studies: increasing value by combining metabolomics with genome-wide SNP genotyping and transcriptional profiling

2012 ◽  
Vol 215 (1) ◽  
pp. 17-28 ◽  
Author(s):  
Georg Homuth ◽  
Alexander Teumer ◽  
Uwe Völker ◽  
Matthias Nauck
Keyword(s):  
Blood Cells ◽  
Large Scale ◽  
Genetic Factors ◽  
Association Studies ◽  
Transcriptional Profiling ◽  
Genome Wide Association Studies ◽  
Protein Levels ◽  
Future Developments ◽  
Genome Wide ◽  
Metabolome Data

The metabolome, defined as the reflection of metabolic dynamics derived from parameters measured primarily in easily accessible body fluids such as serum, plasma, and urine, can be considered as the omics data pool that is closest to the phenotype because it integrates genetic influences as well as nongenetic factors. Metabolic traits can be related to genetic polymorphisms in genome-wide association studies, enabling the identification of underlying genetic factors, as well as to specific phenotypes, resulting in the identification of metabolome signatures primarily caused by nongenetic factors. Similarly, correlation of metabolome data with transcriptional or/and proteome profiles of blood cells also produces valuable data, by revealing associations between metabolic changes and mRNA and protein levels. In the last years, the progress in correlating genetic variation and metabolome profiles was most impressive. This review will therefore try to summarize the most important of these studies and give an outlook on future developments.

scholarly journals Better estimation of SNP heritability from summary statistics provides a new understanding of the genetic architecture of complex traits

2018 ◽  
Author(s):  
Doug Speed ◽  
David J Balding
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Large Scale ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Confounding Bias ◽  
Conserved Regions ◽  
Genome Wide ◽  
Variation Explained

LD Score Regression (LDSC) has been widely applied to the results of genome-wide association studies. However, its estimates of SNP heritability are derived from an unrealistic model in which each SNP is expected to contribute equal heritability. As a consequence, LDSC tends to over-estimate confounding bias, under-estimate the total phenotypic variation explained by SNPs, and provide misleading estimates of the heritability enrichment of SNP categories. Therefore, we present SumHer, software for estimating SNP heritability from summary statistics using more realistic heritability models. After demonstrating its superiority over LDSC, we apply SumHer to the results of 24 large-scale association studies (average sample size 121 000). First we show that these studies have tended to substantially over-correct for confounding, and as a result the number of genome-wide significant loci has under-reported by about 20%. Next we estimate enrichment for 24 categories of SNPs defined by functional annotations. A previous study using LDSC reported that conserved regions were 13-fold enriched, and found a further twelve categories with above 2-fold enrichment. By contrast, our analysis using SumHer finds that conserved regions are only 1.6-fold (SD 0.06) enriched, and that no category has enrichment above 1.7-fold. SumHer provides an improved understanding of the genetic architecture of complex traits, which enables more efficient analysis of future genetic data.

scholarly journals RAFFI: Accurate and fast familial relationship inference in large scale biobank studies using RaPID

PLoS Genetics ◽  
2021 ◽  
Vol 17 (1) ◽  
pp. e1009315
Author(s):  
Ardalan Naseri ◽  
Junjie Shi ◽  
Xihong Lin ◽  
Shaojie Zhang ◽  
Degui Zhi
Keyword(s):  
Large Scale ◽  
Association Studies ◽  
Scale Up ◽  
Data Driven ◽  
Genome Wide Association Studies ◽  
Inference Method ◽  
Genome Wide ◽  
Familial Relationship ◽  
Kinship Coefficients ◽  
Data Driven Approach

Inference of relationships from whole-genome genetic data of a cohort is a crucial prerequisite for genome-wide association studies. Typically, relationships are inferred by computing the kinship coefficients (ϕ) and the genome-wide probability of zero IBD sharing (π0) among all pairs of individuals. Current leading methods are based on pairwise comparisons, which may not scale up to very large cohorts (e.g., sample size >1 million). Here, we propose an efficient relationship inference method, RAFFI. RAFFI leverages the efficient RaPID method to call IBD segments first, then estimate the ϕ and π0 from detected IBD segments. This inference is achieved by a data-driven approach that adjusts the estimation based on phasing quality and genotyping quality. Using simulations, we showed that RAFFI is robust against phasing/genotyping errors, admix events, and varying marker densities, and achieves higher accuracy compared to KING, the current leading method, especially for more distant relatives. When applied to the phased UK Biobank data with ~500K individuals, RAFFI is approximately 18 times faster than KING. We expect RAFFI will offer fast and accurate relatedness inference for even larger cohorts.

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