scholarly journals A Compendium of Age-Related PheWAS and GWAS Traits for Human Genetic Association Studies, Their Networks and Genetic Correlations

2021 ◽  
Vol 12 ◽  
Author(s):  
Seung-Soo Kim ◽  
Adam D. Hudgins ◽  
Brenda Gonzalez ◽  
Sofiya Milman ◽  
Nir Barzilai ◽  
...  
Keyword(s):  
Genetic Association ◽  
Heart Diseases ◽  
Association Studies ◽  
Genetic Correlations ◽  
Genetic Association Studies ◽  
Brain Diseases ◽  
Genome Wide Association Studies ◽  
Medical Subject Headings ◽  
Age Related ◽  
Rich Data

The rich data from the genome-wide association studies (GWAS) and phenome-wide association studies (PheWAS) offer an unprecedented opportunity to identify the biological underpinnings of age-related disease (ARD) risk and multimorbidity. Surprisingly, however, a comprehensive list of ARDs remains unavailable due to the lack of a clear definition and selection criteria. We developed a method to identify ARDs and to provide a compendium of ARDs for genetic association studies. Querying 1,358 electronic medical record-derived traits, we first defined ARDs and age-related traits (ARTs) based on their prevalence profiles, requiring a unimodal distribution that shows an increasing prevalence after the age of 40 years, and which reaches a maximum peak at 60 years of age or later. As a result, we identified a list of 463 ARDs and ARTs in the GWAS and PheWAS catalogs. We next translated the ARDs and ARTs to their respective 276 Medical Subject Headings diseases and 45 anatomy terms. The most abundant disease categories are neoplasms (48 terms), cardiovascular diseases (44 terms), and nervous system diseases (27 terms). Employing data from a human symptoms-disease network, we found 6 symptom-shared disease groups, representing cancers, heart diseases, brain diseases, joint diseases, eye diseases, and mixed diseases. Lastly, by overlaying our ARD and ART list with genetic correlation data from the UK Biobank, we found 54 phenotypes in 2 clusters with high genetic correlations. Our compendium of ARD and ART is a highly useful resource, with broad applicability for studies of the genetics of aging, ARD, and multimorbidity.

scholarly journals Fine scale human genetic structure in three regions of Cameroon reveals episodic diversifying selection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kevin K. Esoh ◽  
Tobias O. Apinjoh ◽  
Steven G. Nyanjom ◽  
Ambroise Wonkam ◽  
Emile R. Chimusa ◽  
...  
Keyword(s):  
Genetic Structure ◽  
Ethnic Groups ◽  
Genetic Association ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Genomic Region ◽  
Sub Saharan Africa ◽  
Genome Wide Association Studies ◽  
Fine Scale ◽  
Sub Saharan

AbstractInferences from genetic association studies rely largely on the definition and description of the underlying populations that highlight their genetic similarities and differences. The clustering of human populations into subgroups (population structure) can significantly confound disease associations. This study investigated the fine-scale genetic structure within Cameroon that may underlie disparities observed with Cameroonian ethnicities in malaria genome-wide association studies in sub-Saharan Africa. Genotype data of 1073 individuals from three regions and three ethnic groups in Cameroon were analyzed using measures of genetic proximity to ascertain fine-scale genetic structure. Model-based clustering revealed distinct ancestral proportions among the Bantu, Semi-Bantu and Foulbe ethnic groups, while haplotype-based coancestry estimation revealed possible longstanding and ongoing sympatric differentiation among individuals of the Foulbe ethnic group, and their Bantu and Semi-Bantu counterparts. A genome scan found strong selection signatures in the HLA gene region, confirming longstanding knowledge of natural selection on this genomic region in African populations following immense disease pressure. Signatures of selection were also observed in the HBB gene cluster, a genomic region known to be under strong balancing selection in sub-Saharan Africa due to its co-evolution with malaria. This study further supports the role of evolution in shaping genomes of Cameroonian populations and reveals fine-scale hierarchical structure among and within Cameroonian ethnicities that may impact genetic association studies in the country.

Efficient estimation of disease odds ratios for follow-up genetic association studies

2017 ◽  
Vol 28 (7) ◽  
pp. 1927-1941
Author(s):  
Jiyuan Hu ◽  
Wei Zhang ◽  
Xinmin Li ◽  
Dongdong Pan ◽  
Qizhai Li
Keyword(s):  
Genetic Association ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Efficient Estimation ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Odds Ratios ◽  
Genome Wide ◽  
Follow Up Studies

In the past decade, genome-wide association studies have identified thousands of susceptible variants associated with complex human diseases and traits. Conducting follow-up genetic association studies has become a standard approach to validate the findings of genome-wide association studies. One problem of high interest in genetic association studies is to accurately estimate the strength of the association, which is often quantified by odds ratios in case-control studies. However, estimating the association directly by follow-up studies is inefficient since this approach ignores information from the genome-wide association studies. In this article, an estimator called GFcom, which integrates information from genome-wide association studies and follow-up studies, is proposed. The estimator includes both the point estimate and corresponding confidence interval. GFcom is more efficient than competing estimators regarding MSE and the length of confidence intervals. The superiority of GFcom is particularly evident when the genome-wide association study suffers from severe selection bias. Comprehensive simulation studies and applications to three real follow-up studies demonstrate the performance of the proposed estimator. An R package, “GFcom”, implementing our method is publicly available at https://github.com/JiyuanHu/GFcom .

scholarly journals Identification of 12 genetic loci associated with human healthspan

2018 ◽  
Author(s):  
Aleksandr Zenin ◽  
Yakov Tsepilov ◽  
Sodbo Sharapov ◽  
Evgeny Getmantsev ◽  
L. I. Menshikov ◽  
...  
Keyword(s):  
Association Studies ◽  
Statistical Significance ◽  
Genetic Correlations ◽  
Human Longevity ◽  
Age At First Birth ◽  
Genome Wide Association Studies ◽  
Psychological Traits ◽  
Regulatory Pathways ◽  
Age Related ◽  
A Genome

The mounting challenge of preserving the quality of life in an aging population directs the focus of longevity science to the regulatory pathways controlling healthspan. To understand the nature of the relationship between the healthspan and lifespan and uncover the genetic architecture of the two phenotypes, we studied the incidence of major age-related diseases in the UK Biobank (UKB) cohort. We observed that the incidence rates of major chronic diseases increase exponentially. The risk of disease acquisition doubled approximately every eight years, i.e., at a rate compatible with the doubling time of the Gompertz mortality law. Assuming that aging is the single underlying factor behind the morbidity rates dynamics, we built a proportional hazards model to predict the risks of the diseases and therefore the age corresponding to the end of healthspan of an individual depending on their age, gender, and the genetic background. We suggested a computationally efficient procedure for the determination of the effect size and statistical significance of individual gene variants associations with healthspan in a form suitable for a Genome-Wide Association Studies (GWAS). Using the UKB sub-population of 300,447 genetically Caucasian, British individuals as a discovery cohort, we identified 12 loci associated with healthspan and reaching the whole-genome level of significance. We observed strong (|ρg| > 0.3) genetic correlations between healthspan and the incidence of specific age-related disease present in our healthspan definition (with the notable exception of dementia). Other examples included all-cause mortality (as derived from parental survival, with ρg = −0.76), life-history traits (metrics of obesity, age at first birth), levels of different metabolites (lipids, amino acids, glycemic traits), and psychological traits (smoking behaviour, cognitive performance, depressive symptoms, insomnia). We conclude by noting that the healthspan phenotype, suggested and characterized here, offers a promising new way to investigate human longevity by exploiting the data from genetic and clinical data on living individuals.

scholarly journals A Validated Phenotyping Algorithm for Genetic Association Studies in Age-related Macular Degeneration

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Joseph M. Simonett ◽  
Mahsa A. Sohrab ◽  
Jennifer Pacheco ◽  
Loren L. Armstrong ◽  
Margarita Rzhetskaya ◽  
...  
Keyword(s):  
Macular Degeneration ◽  
Genetic Association ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Age Related Macular Degeneration ◽  
Age Related

scholarly journals Systemic Sclerosis is a Complex Disease Associated Mainly with Immune Regulatory and Inflammatory Genes

2014 ◽  
Vol 8 (1) ◽  
pp. 29-42 ◽  
Author(s):  
Jingxiao Jin ◽  
Chou Chou ◽  
Maria Lima ◽  
Danielle Zhou ◽  
Xiaodong Zhou
Keyword(s):  
Systemic Sclerosis ◽  
Genetic Association ◽  
Dna Cleavage ◽  
Complex Disease ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Genome Wide Association Studies ◽  
Functional Changes ◽  
Circulating Autoantibodies ◽  
Genetic Contributions

Systemic sclerosis (SSc) is a fibrotic and autoimmune disease characterized clinically by skin and internal organ fibrosis and vascular damage, and serologically by the presence of circulating autoantibodies. Although etiopathogenesis is not yet well understood, the results of numerous genetic association studies support genetic contributions as an important factor to SSc. In this paper, the major genes of SSc are reviewed. The most recent genome-wide association studies (GWAS) are taken into account along with robust candidate gene studies. The literature search was performed on genetic association studies of SSc in PubMed between January 2000 and March 2014 while eligible studies generally had over 600 total participants with replication. A few genetic association studies with related functional changes in SSc patients were also included. A total of forty seven genes or specific genetic regions were reported to be associated with SSc, although some are controversial. These genes include HLA genes, STAT4, CD247, TBX21, PTPN22, TNFSF4, IL23R, IL2RA, IL-21, SCHIP1/IL12A, CD226, BANK1, C8orf13-BLK, PLD4, TLR-2, NLRP1, ATG5, IRF5, IRF8, TNFAIP3, IRAK1, NFKB1, TNIP1, FAS, MIF, HGF, OPN, IL-6, CXCL8, CCR6, CTGF, ITGAM, CAV1, MECP2, SOX5, JAZF1, DNASEIL3, XRCC1, XRCC4, PXK, CSK, GRB10, NOTCH4, RHOB, KIAA0319, PSD3 and PSOR1C1. These genes encode proteins mainly involved in immune regulation and inflammation, and some of them function in transcription, kinase activity, DNA cleavage and repair. The discovery of various SSc-associated genes is important in understanding the genetics of SSc and potential pathogenesis that contribute to the development of this disease.

scholarly journals The Role of SNP Interactions when Determining Independence of Novel Signals in Genetic Association Studies—An Application to ARG1 and Bronchodilator Response

2021 ◽  
Vol 11 (2) ◽  
pp. 145
Author(s):  
Ryan Walsh ◽  
Kirsten Voorhies ◽  
Merry-Lynn McDonald ◽  
Michael McGeachie ◽  
Joanne E. Sordillo ◽  
...  
Keyword(s):  
Genetic Association ◽  
Association Studies ◽  
Critical Role ◽  
Genetic Association Studies ◽  
Genome Wide Association Studies ◽  
The Novel ◽  
Simulation Studies ◽  
Nucleotide Polymorphisms ◽  
Bronchodilator Response

Genome-wide association studies (GWAS) play a critical role in identifying many loci for common diseases and traits. There has been a rapid increase in the number of GWAS over the past decade. As additional GWAS are being conducted, it is unclear whether a novel signal associated with the trait of interest is independent of single nucleotide polymorphisms (SNPs) in the same region that has been previously associated with the trait of interest. The general approach to determining whether the novel association is independent of previous signals is to examine the association of the novel SNP with the trait of interest conditional on the previously identified SNP and/or calculate linkage disequilibrium (LD) between the two SNPs. However, the role of epistasis and SNP by SNP interactions are rarely considered. Through simulation studies, we examined the role of SNP by SNP interactions when determining the independence of two genetic association signals. We have created an R package on Github called gxgRC to generate these simulation studies based on user input. In genetic association studies of asthma, we considered the role of SNP by SNP interactions when determining independence of signals for SNPs in the ARG1 gene and bronchodilator response.

scholarly journals Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision

2021 ◽  
Author(s):  
James A Watson ◽  
Carolyne M Ndila ◽  
Sophie Uyoga ◽  
Alex W Macharia ◽  
Gideon Nyutu ◽  
...  
Keyword(s):  
Severe Malaria ◽  
Genetic Association ◽  
Statistical Power ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Diagnostic Model ◽  
Genome Wide Association Studies ◽  
Case Control Studies ◽  
False Discovery Rates ◽  
Population Controls

Severe falciparum malaria has substantially affected human evolution. Genetic association studies of patients with clinically defined severe malaria and matched population controls have helped characterise human genetic susceptibility to severe malaria, but phenotypic imprecision compromises discovered associations. In areas of high malaria transmission the diagnosis of severe malaria in young children and, in particular, the distinction from bacterial sepsis, is imprecise. We developed a probabilistic diagnostic model of severe malaria using platelet and white count data. Under this model we re-analysed clinical and genetic data from 2,220 Kenyan children with clinically defined severe malaria and 3,940 population controls, adjusting for phenotype mis-labelling. Our model, validated by the distribution of sickle trait, estimated that approximately one third of cases did not have severe malaria. We propose a data-tilting approach for case-control studies with phenotype mis-labelling and show that this reduces false discovery rates and improves statistical power in genome-wide association studies.

scholarly journals Genetic Associations with Aging Muscle: A Systematic Review

Cells ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 12 ◽  
Author(s):  
Jedd Pratt ◽  
Colin Boreham ◽  
Sean Ennis ◽  
Anthony W. Ryan ◽  
Giuseppe De Vito
Keyword(s):  
Skeletal Muscle ◽  
Genetic Association ◽  
Functional Disability ◽  
Association Studies ◽  
Final Analysis ◽  
Dna Polymorphisms ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Muscle Properties ◽  
Age Related

The age-related decline in skeletal muscle mass, strength and function known as ‘sarcopenia’ is associated with multiple adverse health outcomes, including cardiovascular disease, stroke, functional disability and mortality. While skeletal muscle properties are known to be highly heritable, evidence regarding the specific genes underpinning this heritability is currently inconclusive. This review aimed to identify genetic variants known to be associated with muscle phenotypes relevant to sarcopenia. PubMed, Embase and Web of Science were systematically searched (from January 2004 to March 2019) using pre-defined search terms such as “aging”, “sarcopenia”, “skeletal muscle”, “muscle strength” and “genetic association”. Candidate gene association studies and genome wide association studies that examined the genetic association with muscle phenotypes in non-institutionalised adults aged ≥50 years were included. Fifty-four studies were included in the final analysis. Twenty-six genes and 88 DNA polymorphisms were analysed across the 54 studies. The ACTN3, ACE and VDR genes were the most frequently studied, although the IGF1/IGFBP3, TNFα, APOE, CNTF/R and UCP2/3 genes were also shown to be significantly associated with muscle phenotypes in two or more studies. Ten DNA polymorphisms (rs154410, rs2228570, rs1800169, rs3093059, rs1800629, rs1815739, rs1799752, rs7412, rs429358 and 192 bp allele) were significantly associated with muscle phenotypes in two or more studies. Through the identification of key gene variants, this review furthers the elucidation of genetic associations with muscle phenotypes associated with sarcopenia.

scholarly journals Improving statistical power in severe malaria genetic association studies by augmenting phenotypic precision

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
James A Watson ◽  
Carolyne M Ndila ◽  
Sophie Uyoga ◽  
Alexander Macharia ◽  
Gideon Nyutu ◽  
...  
Keyword(s):  
Severe Malaria ◽  
Genetic Association ◽  
Statistical Power ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Diagnostic Model ◽  
Genome Wide Association Studies ◽  
Case Control Studies ◽  
False Discovery Rates ◽  
Population Controls

Severe falciparum malaria has substantially affected human evolution. Genetic association studies of patients with clinically defined severe malaria and matched population controls have helped characterise human genetic susceptibility to severe malaria, but phenotypic imprecision compromises discovered associations. In areas of high malaria transmission the diagnosis of severe malaria in young children and, in particular, the distinction from bacterial sepsis, is imprecise. We developed a probabilistic diagnostic model of severe malaria using platelet and white count data. Under this model we re-analysed clinical and genetic data from 2,220 Kenyan children with clinically defined severe malaria and 3,940 population controls, adjusting for phenotype mis-labelling. Our model, validated by the distribution of sickle trait, estimated that approximately one third of cases did not have severe malaria. We propose a data-tilting approach for case-control studies with phenotype mis-labelling and show that this reduces false discovery rates and improves statistical power in genome-wide association studies.

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