scholarly journals TRIBES: A user-friendly pipeline for relatedness detection and disease gene discovery

2019 ◽  
Author(s):  
Natalie A. Twine ◽  
Piotr Szul ◽  
Lyndal Henden ◽  
Emily P. McCann ◽  
Ian P. Blair ◽  
...  
Keyword(s):  
Disease Gene ◽  
Association Studies ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Susceptibility Loci ◽  
Disease Gene Discovery ◽  
Genome Wide ◽  
Single User ◽  
Relationship Structures ◽  
User Friendly

AbstractSummaryTRIBES is a user-friendly pipeline for relatedness detection in genomic data. TRIBES is the first tool which is both accurate up to 7th degree relatives (e.g. third cousins) and combines essential data processing steps into a single user-friendly pipeline. Furthermore, using a proof-of-principle cohort comprising amyotrophic lateral sclerosis cases with known relationship structures and a known causal mutation in SOD1, we demonstrated that TRIBES can successfully uncover disease susceptibility loci. TRIBES has multiple applications in addition to disease gene mapping, including sample quality control in genome wide association studies and avoiding consanguineous unions in family planning.AvailabilityTRIBES is freely available on GitHub: https://github.com/aehrc/TRIBES/[email protected] informationXXXX

deMeta: Removing sub-studies from meta-analysis of genome wide association studies (GWAS)

2020 ◽  
Author(s):  
Jiangming Sun ◽  
Yunpeng Wang
Keyword(s):  
Association Studies ◽  
Meta Analysis ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Computationally Efficient ◽  
Genome Wide ◽  
Quantile Plots ◽  
User Friendly ◽  
The Impact

ABSTRACTSummaryPost-GWAS studies using the results from large consortium meta-analysis often need to correctly take care of the overlapping sample issue. The gold standard approach for resolving this issue is to reperform the GWAS or meta-analysis excluding the overlapped participants. However, such approach is time-consuming and, sometimes, restricted by the available data. deMeta provides a user friendly and computationally efficient command-line implementation for removing the effect of a contributing sub-study to a consortium from the meta-analysis results. Only the summary statistics of the meta-analysis the sub-study to be removed are required. In addition, deMeta can generate contrasting Manhattan and quantile-quantile plots for users to visualize the impact of the sub-study on the meta-analysis results.Availability and ImplementationThe python source code, examples and documentations of deMeta are publicly available at https://github.com/Computational-NeuroGenetics/[email protected] (J. Sun); [email protected] (Y. Wang)Supplementary informationNone.

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<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 bGWAS: an R package to perform Bayesian genome wide association studies

2020 ◽  
Vol 36 (15) ◽  
pp. 4374-4376
Author(s):  
Ninon Mounier ◽  
Zoltán Kutalik
Keyword(s):  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
R Package ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Biological Mechanisms ◽  
Genome Wide ◽  
Related Risk

Abstract Summary Increasing sample size is not the only strategy to improve discovery in Genome Wide Association Studies (GWASs) and we propose here an approach that leverages published studies of related traits to improve inference. Our Bayesian GWAS method derives informative prior effects by leveraging GWASs of related risk factors and their causal effect estimates on the focal trait using multivariable Mendelian randomization. These prior effects are combined with the observed effects to yield Bayes Factors, posterior and direct effects. The approach not only increases power, but also has the potential to dissect direct and indirect biological mechanisms. Availability and implementation bGWAS package is freely available under a GPL-2 License, and can be accessed, alongside with user guides and tutorials, from https://github.com/n-mounier/bGWAS. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals 12 new susceptibility loci for prostate cancer identified by genome-wide association study in Japanese population

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Ryo Takata ◽  
Atsushi Takahashi ◽  
Masashi Fujita ◽  
Yukihide Momozawa ◽  
Edward J. Saunders ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Japanese Population ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
European Population ◽  
Genome Wide Association ◽  
High Risk Population ◽  
Genome Wide Association Studies ◽  
Susceptibility Loci ◽  
Genome Wide

Abstract Genome-wide association studies (GWAS) have identified ~170 genetic loci associated with prostate cancer (PCa) risk, but most of them were identified in European populations. We here performed a GWAS and replication study using a large Japanese cohort (9,906 cases and 83,943 male controls) to identify novel susceptibility loci associated with PCa risk. We found 12 novel loci for PCa including rs1125927 (TMEM17, P = 3.95 × 10−16), rs73862213 (GATA2, P = 5.87 × 10−23), rs77911174 (ZMIZ1, P = 5.28 × 10−20), and rs138708 (SUN2, P = 1.13 × 10−15), seven of which had crucially low minor allele frequency in European population. Furthermore, we stratified the polygenic risk for Japanese PCa patients by using 82 SNPs, which were significantly associated with Japanese PCa risk in our study, and found that early onset cases and cases with family history of PCa were enriched in the genetically high-risk population. Our study provides important insight into genetic mechanisms of PCa and facilitates PCa risk stratification in Japanese population.

Identification of two genes as novel susceptibility loci for type 2 diabetes mellitus in Japanese

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
M Oguri ◽  
K Kato ◽  
H Horibe ◽  
T Fujimaki ◽  
J Sakuma ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Association Studies ◽  
East Asian ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Susceptibility Loci ◽  
Asian Populations ◽  
Genome Wide ◽  
Minor Alleles

Abstract Background The heritability of Type 2 diabetes mellitus (T2DM) has been estimated to be 50% to 60%. Although genome-wide association studies identified >120 loci that confer susceptibility to T2DM, these studies were commonly conducted in a cross-sectional manner. Purpose The purpose of the study was to identify genetic variants that confer susceptibility to T2DM in Japanese. We have now performed longitudinal exome-wide association studies (EWASs) to identify novel loci for T2DM by examining temporal changes in fasting plasma glucose (FPG) level, blood hemoglobin A1c (HbA1c) content, and the prevalence of T2DM. Methods Longitudinal EWASs (mean follow-up period, 5 years) were performed with Illumina Human Exome-12 v1.2 DNA Analysis BeadChip or Infinium Exome-24 v1.0 BeadChip arrays and with 6,022 Japanese (755 subjects with T2DM, 5267 controls). The relation of genotypes of 24,579 SNPs that passed quality control to FPG level, blood HbA1c content, or the prevalence of T2DM was examined with the generalized estimating equation (GEE). To compensate for multiple comparisons of genotypes with each of the three parameters, we applied Bonferroni's correction for statistical significance of association. Results Longitudinal EWASs (GEE with adjustment for age, sex, body mass index, and smoking) revealed that rs6414624 of EVC (P<2.0×10–16 for T2DM, P=9.1×10–11 for FPG), rs78338345 of GGA3 (P<2.0×10–16 for T2DM, P=4.3×10–9 for FPG), rs10490775 of PTPRG (P<2.0×10–16 for T2DM, P=3.3×10–7 for FPG), and rs61739510 of GLT6D1 (P<2.0×10–16 for T2DM, P=5.8×10–7 for FPG) were significantly associated with the prevalence of T2DM and FPG levels; and rs11558471 in SLC30A8 with FPG level (P=1.8×10–8) and blood HbA1c content (P=1.2×10–7). After examination of the relation of identified SNPs to FPG level and blood HbA1c content, linkage disequilibrium of the SNPs, and results of the previous genome-wide association studies, we identified rs6414624 of EVC and rs78338345 of GGA3 as novel susceptibility loci for T2DM. In the identified SNPs (rs6414624 and rs7833834), FPG level, blood HbA1c content, and the prevalence of T2DM were significantly lower in homozygotes with the minor alleles than in homozygotes with the major alleles or heterozygotes. These results suggest that the minor alleles of rs6414624 and rs78338345 are protective against T2DM in Japanese. According to allele frequency data from the 1000 Genomes Project database, the minor G allele of rs78338345 of GGA3 is specifically distributed in East Asia. This suggests that the minor allele frequency may have increased in East Asian populations after the split of East Asian and non-East Asian populations. Conclusion We have newly identified EVC and GGA3 as susceptibility loci for T2DM in Japanese. Determination of genotypes for these SNPs at these loci may prove informative for assessment of the genetic risk for T2DM in Japanese. Funding Acknowledgement Type of funding source: None

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