scholarly journals A global atlas of genetic associations of 220 deep phenotypes

2020 ◽  
Author(s):  
Saori Sakaue ◽  
Masahiro Kanai ◽  
Yosuke Tanigawa ◽  
Juha Karjalainen ◽  
Mitja Kurki ◽  
...  
Keyword(s):  
Association Studies ◽  
East Asian ◽  
Allergic Diseases ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Endemic Diseases ◽  
Genetic Components ◽  
Mhc Locus ◽  
The Uk ◽  
Meta Analyses

AbstractThe current genome-wide association studies (GWASs) do not yet capture sufficient diversity in terms of populations and scope of phenotypes. To address an essential need to expand an atlas of genetic associations in non-European populations, we conducted 220 deep-phenotype GWASs (disease endpoints, biomarkers, and medication usage) in BioBank Japan (n = 179,000), by incorporating past medical history and text-mining results of electronic medical records. Meta-analyses with the harmonized phenotypes in the UK Biobank and FinnGen (ntotal = 628,000) identified over 4,000 novel loci, which substantially deepened the resolution of the genomic map of human traits, benefited from East Asian endemic diseases and East Asian specific variants. This atlas elucidated the globally shared landscape of pleiotropy as represented by the MHC locus, where we conducted fine-mapping by HLA imputation. Finally, to intensify the value of deep-phenotype GWASs, we performed statistical decomposition of matrices of phenome-wide summary statistics, and identified the latent genetic components, which pinpointed the responsible variants and shared biological mechanisms underlying current disease classifications across populations. The decomposed components enabled genetically informed subtyping of similar diseases (e.g., allergic diseases). Our study suggests a potential avenue for hypothesis-free re-investigation of human disease classifications through genetics.

scholarly journals Carotid Artery Atherosclerosis: A Review on Heritability and Genetics

2018 ◽  
Vol 21 (5) ◽  
pp. 333-346 ◽  
Author(s):  
Bianka Forgo ◽  
Emanuela Medda ◽  
Anita Hernyes ◽  
Laszlo Szalontai ◽  
David Laszlo Tarnoki ◽  
...  
Keyword(s):  
Association Studies ◽  
Genetic Association Studies ◽  
Intima Media Thickness ◽  
Carotid Intima Media Thickness ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Carotid Artery Atherosclerosis ◽  
Meta Analyses

Carotid atherosclerosis (CAS) is associated with increased cardiovascular risk, and therefore, assessing the genetic versus environmental background of CAS traits is of key importance. Carotid intima-media-thickness and plaque characteristics seem to be moderately heritable, with remarkable differences in both heritability and presence or severity of these traits among ethnicities. Although the considerable role of additive genetic effects is obvious, based on the results so far, there is an important emphasis on non-shared environmental factors as well. We aimed to collect and summarize the papers that investigate twin and family studies assessing the phenotypic variance attributable to genetic associations with CAS. Genes in relation to CAS markers were overviewed with a focus on genetic association studies and genome-wide association studies. Although the role of certain genes is confirmed by studies conducted on large populations and meta-analyses, many of them show conflicting results. A great focus should be on future studies elucidating the exact pathomechanism of these genes in CAS in order to imply them as novel therapeutic targets.

Genetics of systemic sclerosis

2020 ◽  
Vol 5 (3) ◽  
pp. 192-201 ◽  
Author(s):  
Yuki Ishikawa ◽  
Chikashi Terao
Keyword(s):  
Systemic Sclerosis ◽  
Disease Risk ◽  
Association Studies ◽  
Human Leukocyte ◽  
Premature Death ◽  
Microvascular Dysfunction ◽  
Candidate Gene Approach ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Genetic Components

Systemic sclerosis is an autoimmune disease characterized by generalized fibrosis in connective tissues and internal organs as consequences of microvascular dysfunction and immune dysfunctions, which leads to premature death in affected individuals. The etiology of systemic sclerosis is complex and poorly understood, but as with most autoimmune diseases, it is widely accepted that both environmental and genetic factors contribute to disease risk. During the last decade, the number of genetic markers convincingly associated with systemic sclerosis has exponentially increased. In this article, we briefly mention the genetic components of systemic sclerosis. Then, we review the classical and novel genetic associations with systemic sclerosis, analyzing the firmest and replicated signals within non–human leukocyte antigen genes, identified by both candidate gene approach and genome-wide association studies. We also provide an insight into the future perspectives that will shed more light into the complex genetic background of the disease. Despite the remarkable advance of systemic sclerosis genetics during the last decade, the use of the new genetic technologies such as next-generation sequencing, as well as the deep phenotyping of the study cohorts, to fully characterize the genetic component of this disease is imperative to identify causal variants, which leads to more targeted and effective treatment of systemic sclerosis.

scholarly journals Systematic single-variant and gene-based association testing of 3,700 phenotypes in 281,850 UK Biobank exomes

2021 ◽  
Author(s):  
Konrad Karczewski ◽  
Matthew Solomonson ◽  
Katherine R Chao ◽  
Julia K Goodrich ◽  
Grace Tiao ◽  
...  
Keyword(s):  
Sequence Data ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Genetic Associations ◽  
Allelic Series ◽  
Association Analyses ◽  
Wide Range ◽  
The Uk ◽  
The Impact

Genome-wide association studies have successfully discovered thousands of common variants associated with human diseases and traits, but the landscape of rare variation in human disease has not been explored at scale. Exome sequencing studies of population biobanks provide an opportunity to systematically evaluate the impact of rare coding variation across a wide range of phenotypes to discover genes and allelic series relevant to human health and disease. Here, we present results from systematic association analyses of 3,700 phenotypes using single-variant and gene tests of 281,850 individuals in the UK Biobank with exome sequence data. We find that the discovery of genetic associations is tightly linked to frequency as well as correlated with metrics of deleteriousness and natural selection. We highlight biological findings elucidated by these data and release the dataset as a public resource alongside a browser framework for rapidly exploring rare variant association results.

scholarly journals Systematic meta-analyses, field synopsis and global assessment of the evidence of genetic association studies in colorectal cancer

Gut ◽  
2019 ◽  
Vol 69 (8) ◽  
pp. 1460-1471 ◽  
Author(s):  
Zahra Montazeri ◽  
Xue Li ◽  
Christine Nyiraneza ◽  
Xiangyu Ma ◽  
Maria Timofeeva ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genetic Associations ◽  
Genome Wide ◽  
Discovery Probability ◽  
Assessment Of Evidence ◽  
Meta Analyses

ObjectiveTo provide an understanding of the role of common genetic variations in colorectal cancer (CRC) risk, we report an updated field synopsis and comprehensive assessment of evidence to catalogue all genetic markers for CRC (CRCgene2).DesignWe included 869 publications after parallel literature review and extracted data for 1063 polymorphisms in 303 different genes. Meta-analyses were performed for 308 single nucleotide polymorphisms (SNPs) in 158 different genes with at least three independent studies available for analysis. Scottish, Canadian and Spanish data from genome-wide association studies (GWASs) were incorporated for the meta-analyses of 132 SNPs. To assess and classify the credibility of the associations, we applied the Venice criteria and Bayesian False-Discovery Probability (BFDP). Genetic associations classified as ‘positive’ and ‘less-credible positive’ were further validated in three large GWAS consortia conducted in populations of European origin.ResultsWe initially identified 18 independent variants at 16 loci that were classified as ‘positive’ polymorphisms for their highly credible associations with CRC risk and 59 variants at 49 loci that were classified as ‘less-credible positive’ SNPs; 72.2% of the ‘positive’ SNPs were successfully replicated in three large GWASs and the ones that were not replicated were downgraded to ‘less-credible’ positive (reducing the ‘positive’ variants to 14 at 11 loci). For the remaining 231 variants, which were previously reported, our meta-analyses found no evidence to support their associations with CRC risk.ConclusionThe CRCgene2 database provides an updated list of genetic variants related to CRC risk by using harmonised methods to assess their credibility.

scholarly journals Large-scale trans-ethnic replication and discovery of genetic associations for rare diseases with self-reported medical data

2021 ◽  
Author(s):  
Suyash S Shringarpure ◽  
Wei Wang ◽  
Yunxuan Jiang ◽  
Alison Acevedo ◽  
Devika Dhamija ◽  
...  
Keyword(s):  
Rare Disease ◽  
Rare Diseases ◽  
Large Scale ◽  
Mixed Model ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Genome Wide ◽  
Reported Data ◽  
The Uk

A key challenge in the study of rare disease genetics is assembling large case cohorts for well- powered studies. We demonstrate the use of self-reported diagnosis data to study rare diseases at scale. We performed genome-wide association studies (GWAS) for 33 rare diseases using self-reported diagnosis phenotypes and re-discovered 29 known associations to validate our approach. In addition, we performed the first GWAS for Duane retraction syndrome, vestibular schwannoma and spontaneous pneumothorax, and report novel genome-wide significant associations for these diseases. We replicated these novel associations in non-European populations within the 23andMe, Inc. cohort as well as in the UK Biobank cohort. We also show that mixed model analyses including all ethnicities and related samples increase the power for finding associations in rare diseases. Our results, based on analysis of 19,084 rare disease cases for 33 diseases from 7 populations, show that large-scale online collection of self-reported data is a viable method for discovery and replication of genetic associations for rare diseases. This approach, which is complementary to sequencing-based approaches, will enable the discovery of more novel genetic associations for increasingly rare diseases across multiple ancestries and shed more light on the genetic architecture of rare diseases.

scholarly journals How robust are cross-population signatures of polygenic adaptation in humans?

2020 ◽  
Author(s):  
Alba Refoyo-Martínez ◽  
Siyang Liu ◽  
Anja Moltke Jørgensen ◽  
Xin Jin ◽  
Anders Albrechtsen ◽  
...  
Keyword(s):  
Effect Size ◽  
Association Studies ◽  
Gwas Data ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Uk Biobank ◽  
Polygenic Adaptation ◽  
The Uk ◽  
Meta Analyses ◽  
Size Estimates

AbstractOver the past decade, summary statistics from genome-wide association studies (GWAS) have been used to detect and quantify polygenic adaptation in humans. Several studies have reported signatures of natural selection at sets of SNPs associated with complex traits, like height and body mass index. However, more recent studies suggest that some of these signals may be caused by biases from uncorrected population stratification in the GWAS data with which these tests are performed. Moreover, past studies have predominantly relied on SNP effect size estimates obtained from GWAS panels of European ancestries, which are known to be poor predictors of phenotypes in non-European populations. Here, we collated GWAS data from multiple anthropometric and metabolic traits that have been measured in more than one cohort around the world, including the UK Biobank, FINRISK, Chinese NIPT, Biobank Japan, APCDR and PAGE. We then evaluated how robust signals of polygenic adaptation are to the choice of GWAS cohort used to identify associated variants and their effect size estimates, while using the same panel to obtain population allele frequencies (The 1000 Genomes Project). We observe many discrepancies across tests performed on the same phenotype and find that association studies performed using multiple different cohorts, like meta-analyses, tend to produce scores with strong overdispersion across populations. This results in apparent signatures of polygenic adaptation which are not observed when using effect size estimates from biobank-based GWAS of homogeneous ancestries. Indeed, we were able to artificially create score overdispersion when taking the UK Biobank cohort and simulating a meta-analysis on multiple subsets of the cohort. This suggests that extreme caution should be taken in the execution and interpretation of future tests of polygenic adaptation based on population differentiation, especially when using summary statistics from GWAS meta-analyses.

scholarly journals Cancer GAMAdb: database of cancer genetic associations from meta-analyses and genome-wide association studies

2011 ◽  
Vol 19 (8) ◽  
pp. 928-930 ◽  
Author(s):  
Sheri D Schully ◽  
Wei Yu ◽  
Victoria McCallum ◽  
Camilla B Benedicto ◽  
Linda M Dong ◽  
...  
Keyword(s):  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Cancer Genetic ◽  
Genome Wide ◽  
Meta Analyses

scholarly journals Identification of Genetic Loci Simultaneously Associated with Multiple Cardiometabolic Traits

2021 ◽  
Author(s):  
Alexis C. Wood ◽  
Amit Arora ◽  
Michelle Newell ◽  
Victoria L. Bland ◽  
Jin Zhou ◽  
...  
Keyword(s):  
Association Studies ◽  
Density Lipoprotein ◽  
Independent Set ◽  
Genome Wide Association Studies ◽  
Genetic Loci ◽  
Full Spectrum ◽  
Genome Wide ◽  
Cardiometabolic Disorders ◽  
The Uk ◽  
Meta Analyses

Background and Aims: Cardiometabolic disorders (CMD) arise from a constellation of features such as increased adiposity, hyperlipidemia, hypertension and compromised glucose control. Many genetic loci have shown associations with individual CMD-related traits, but no investigations have focused on simultaneously identifying loci showing associations across all domains. We therefore sought to identify loci associated with risk across seven continuous CMD-related traits. Methods and Results: We conducted separate genome-wide association studies (GWAS) for systolic and diastolic blood pressure (SBP/DBP), hemoglobin A1c (HbA1c), low- and high- density lipoprotein cholesterol (LDL-C/HDL-C), waist-to-hip-ratio (WHR), and triglycerides (TGs) in the UK Biobank (N= 356,574-456,823). Multiple loci reached genome-wide levels of significance (N=145-333) for each trait, but only four loci (in/near VEGFA, GRB14-COBLL1, KLF14, and RGS19-OPRL1) were associated with risk across all seven traits (P<5×10-8). We sought replication of these four loci in an independent set of seven trait-specific GWAS meta-analyses. GRB14-COBLL1 showed the most consistent replication, revealing nominally significant associations (p<0.05) with all traits except DBP. Conclusions: Our analyses suggest that very few loci are associated in the same direction of risk with traits representing the full spectrum of CMD features. We identified four such loci, and an understanding of the pathways between these loci and CMD risk may eventually identify factors that can be used to identify pathologic disturbances that represent broadly beneficial therapeutic targets.

scholarly journals Assessing digital phenotyping to enhance genetic studies of human diseases

2019 ◽  
Author(s):  
Christopher DeBoever ◽  
Yosuke Tanigawa ◽  
Matthew Aguirre ◽  
Greg McInnes ◽  
Adam Lavertu ◽  
...  
Keyword(s):  
Family History ◽  
Association Studies ◽  
Heterogeneous Data ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Hospital Record ◽  
Hospital Records ◽  
Digital Phenotyping ◽  
History Of ◽  
The Uk

AbstractPopulation-scale biobanks that combine genetic data and high-dimensional phenotyping for a large number of participants provide an exciting opportunity to perform genome-wide association studies (GWAS) to identify genetic variants associated with diverse quantitative traits and diseases. A major challenge for GWAS in population biobanks is ascertaining disease cases from heterogeneous data sources such as hospital records, digital questionnaire responses, or interviews. In this study, we use genetic parameters including genetic correlation to evaluate whether GWAS performed using cases in the UK Biobank ascertained from hospital records, questionnaire responses, and family history of diseases implicate similar disease genetics across a range of effect sizes. We find that hospital record and questionnaire GWAS largely identify similar genetic effects for many complex phenotypes and that combining together both phenotyping methods improves power to detect genetic associations. We also show that family GWAS using cases ascertained on family history of disease agrees with combined hospital record/questionnaire GWAS and that family history GWAS has better power to detect genetic associations for some phenotypes. Overall, this work demonstrates that digital phenotyping and unstructured phenotype data can be combined with structured data such as hospital records to identify cases for GWAS in biobanks and improve the ability of such studies to identify genetic associations.

scholarly journals Genetic feature engineering enables characterisation of shared risk factors in immune-mediated diseases

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Oliver S. Burren ◽  
Guillermo Reales ◽  
Limy Wong ◽  
John Bowes ◽  
James C. Lee ◽  
...  
Keyword(s):  
Sample Size ◽  
Genetic Risk ◽  
Association Studies ◽  
Evidence Base ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Genetic Associations ◽  
Immune Mediated ◽  
The Uk ◽  
Shared Risk

Abstract Background Genome-wide association studies (GWAS) have identified pervasive sharing of genetic architectures across multiple immune-mediated diseases (IMD). By learning the genetic basis of IMD risk from common diseases, this sharing can be exploited to enable analysis of less frequent IMD where, due to limited sample size, traditional GWAS techniques are challenging. Methods Exploiting ideas from Bayesian genetic fine-mapping, we developed a disease-focused shrinkage approach to allow us to distill genetic risk components from GWAS summary statistics for a set of related diseases. We applied this technique to 13 larger GWAS of common IMD, deriving a reduced dimension “basis” that summarised the multidimensional components of genetic risk. We used independent datasets including the UK Biobank to assess the performance of the basis and characterise individual axes. Finally, we projected summary GWAS data for smaller IMD studies, with less than 1000 cases, to assess whether the approach was able to provide additional insights into genetic architecture of less common IMD or IMD subtypes, where cohort collection is challenging. Results We identified 13 IMD genetic risk components. The projection of independent UK Biobank data demonstrated the IMD specificity and accuracy of the basis even for traits with very limited case-size (e.g. vitiligo, 150 cases). Projection of additional IMD-relevant studies allowed us to add biological interpretation to specific components, e.g. related to raised eosinophil counts in blood and serum concentration of the chemokine CXCL10 (IP-10). On application to 22 rare IMD and IMD subtypes, we were able to not only highlight subtype-discriminating axes (e.g. for juvenile idiopathic arthritis) but also suggest eight novel genetic associations. Conclusions Requiring only summary-level data, our unsupervised approach allows the genetic architectures across any range of clinically related traits to be characterised in fewer dimensions. This facilitates the analysis of studies with modest sample size by matching shared axes of both genetic and biological risk across a wider disease domain, and provides an evidence base for possible therapeutic repurposing opportunities.

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