scholarly journals Genome-wide association studies of cardiac electrical phenotypes

2020 ◽  
Vol 116 (9) ◽  
pp. 1620-1634
Author(s):  
Charlotte Glinge ◽  
Najim Lahrouchi ◽  
Reza Jabbari ◽  
Jacob Tfelt-Hansen ◽  
Connie R Bezzina
Keyword(s):  
Genetic Basis ◽  
Association Studies ◽  
Individual Variability ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
Challenges And Opportunities ◽  
Electrocardiographic Parameters

Abstract The genetic basis of cardiac electrical phenotypes has in the last 25 years been the subject of intense investigation. While in the first years, such efforts were dominated by the study of familial arrhythmia syndromes, in recent years, large consortia of investigators have successfully pursued genome-wide association studies (GWAS) for the identification of single-nucleotide polymorphisms that govern inter-individual variability in electrocardiographic parameters in the general population. We here provide a review of GWAS conducted on cardiac electrical phenotypes in the last 14 years and discuss the implications of these discoveries for our understanding of the genetic basis of disease susceptibility and variability in disease severity. Furthermore, we review functional follow-up studies that have been conducted on GWAS loci associated with cardiac electrical phenotypes and highlight the challenges and opportunities offered by such studies.

Data Mining in Genome Wide Association Studies

2011 ◽  
pp. 465-471
Author(s):  
Tom Burr
Keyword(s):  
Data Mining ◽  
Genetic Basis ◽  
Association Studies ◽  
Causal Variant ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Common Diseases ◽  
Genome Wide ◽  
Genomic Regions

The genetic basis for some human diseases, in which one or a few genome regions increase the probability of acquiring the disease, is fairly well understood. For example, the risk for cystic fibrosis is linked to particular genomic regions. Identifying the genetic basis of more common diseases such as diabetes has proven to be more difficult, because many genome regions apparently are involved, and genetic effects are thought to depend in unknown ways on other factors, called covariates, such as diet and other environmental factors (Goldstein and Cavalleri, 2005). Genome-wide association studies (GWAS) aim to discover the genetic basis for a given disease. The main goal in a GWAS is to identify genetic variants, single nucleotide polymorphisms (SNPs) in particular, that show association with the phenotype, such as “disease present” or “disease absent” either because they are causal, or more likely, because they are statistically correlated with an unobserved causal variant (Goldstein and Cavalleri, 2005). A GWAS can analyze “by DNA site” or “by multiple DNA sites. ” In either case, data mining tools (Tachmazidou, Verzilli, and De Lorio, 2007) are proving to be quite useful for understanding the genetic causes for common diseases.

scholarly journals GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background

2020 ◽  
Author(s):  
Nasa Sinnott-Armstrong ◽  
Sahin Naqvi ◽  
Manuel Rivas ◽  
Jonathan K Pritchard
Keyword(s):  
Complex Traits ◽  
Genetic Basis ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Biological Processes ◽  
Uk Biobank ◽  
The Core ◽  
Genome Wide ◽  
Core Genes

SummaryGenome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. However, for most traits it remains difficult to interpret what genes and biological processes are impacted by the top hits. Here, as a contrast, we describe UK Biobank GWAS results for three molecular traits—urate, IGF-1, and testosterone—that are biologically simpler than most diseases, and for which we know a great deal in advance about the core genes and pathways. Unlike most GWAS of complex traits, for all three traits we find that most top hits are readily interpretable. We observe huge enrichment of significant signals near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of variation in each trait, including insights into differences in testosterone regulation between females and males. Meanwhile, in other respects the results are reminiscent of GWAS for more-complex traits. In particular, even these molecular traits are highly polygenic, with most of the variance coming not from core genes, but from thousands to tens of thousands of variants spread across most of the genome. Given that diseases are often impacted by many distinct biological processes, including these three, our results help to illustrate why so many variants can affect risk for any given disease.

scholarly journals The Mediation Effects of Aluminum in Plasma and Dipeptidyl Peptidase Like Protein 6 (DPP6) Polymorphism on Renal Function via Genome-Wide Typing Association

2021 ◽  
Vol 18 (19) ◽  
pp. 10484
Author(s):  
Ting-Hao Chen ◽  
Chen-Cheng Yang ◽  
Kuei-Hau Luo ◽  
Chia-Yen Dai ◽  
Yao-Chung Chuang ◽  
...  
Keyword(s):  
Renal Function ◽  
Mediation Analysis ◽  
Association Studies ◽  
Dipeptidyl Peptidase ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Mediation Effects ◽  
Genome Wide ◽  
Estimated Glomerular Filtration Rates

Aluminum (Al) toxicity is related to renal failure and the failure of other systems. Although there were some genome-wide association studies (GWAS) in Australia and England, there were no GWAS about Han Chinese to our knowledge. Thus, this research focused on using whole genomic genotypes from the Taiwan Biobank for exploring the association between Al concentrations in plasma and renal function. Participants, who underwent questionnaire interviews, biomarkers, and genotyping, were from the Taiwan Biobank database. Then, we measured their plasma Al concentrations with ICP-MS in the laboratory at Kaohsiung Medical University. We used this data to link genome-wide association (GWA) tests while looking for candidate genes and associated plasma Al concentration to renal function. Furthermore, we examined the path relationship between Single Nucleotide Polymorphisms (SNPs), Al concentrations, and estimated glomerular filtration rates (eGFR) through the mediation analysis with 3000 replication bootstraps. Following the principles of GWAS, we focused on three SNPs within the dipeptidyl peptidase-like protein 6 (DPP6) gene in chromosome 7, rs10224371, rs2316242, and rs10268004, respectively. The results of the mediation analysis showed that all of the selected SNPs have indirectly affected eGFR through a mediation of Al concentrations. Our analysis revealed the association between DPP6 SNPs, plasma Al concentrations, and eGFR. However, further longitudinal studies and research on mechanism are in need. Our analysis was still be the first study that explored the association between the DPP6, SNPs, and Al in plasma affecting eGFR.

scholarly journals Genome-wide association studies of yield-related traits in high-latitude japonica rice

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Guomin Zhang ◽  
Rongsheng Wang ◽  
Juntao Ma ◽  
Hongru Gao ◽  
Lingwei Deng ◽  
...  
Keyword(s):  
Linear Model ◽  
High Latitude ◽  
Association Studies ◽  
Genome Wide Association ◽  
Japonica Rice ◽  
Genome Wide Association Studies ◽  
Heilongjiang Province ◽  
Nucleotide Polymorphisms ◽  
Coding Region ◽  
Genome Wide

Abstract Background Heilongjiang Province is a high-quality japonica rice cultivation area in China. One in ten bowls of Chinese rice is produced here. Increasing yield is one of the main aims of rice production in this area. However, yield is a complex quantitative trait composed of many factors. The purpose of this study was to determine how many genetic loci are associated with yield-related traits. Genome-wide association studies (GWAS) were performed on 450 accessions collected from northeast Asia, including Russia, Korea, Japan and Heilongjiang Province of China. These accessions consist of elite varieties and landraces introduced into Heilongjiang Province decade ago. Results After resequencing of the 450 accessions, 189,019 single nucleotide polymorphisms (SNPs) were used for association studies by two different models, a general linear model (GLM) and a mixed linear model (MLM), examining four traits: days to heading (DH), plant height (PH), panicle weight (PW) and tiller number (TI). Over 25 SNPs were found to be associated with each trait. Among them, 22 SNPs were selected to identify candidate genes, and 2, 8, 1 and 11 SNPs were found to be located in 3′ UTR region, intron region, coding region and intergenic region, respectively. Conclusions All SNPs detected in this research may become candidates for further fine mapping and may be used in the molecular breeding of high-latitude rice.

scholarly journals Secure large-scale genome-wide association studies using homomorphic encryption

2020 ◽  
Vol 117 (21) ◽  
pp. 11608-11613 ◽  
Author(s):  
Marcelo Blatt ◽  
Alexander Gusev ◽  
Yuriy Polyakov ◽  
Shafi Goldwasser
Keyword(s):  
Large Scale ◽  
Homomorphic Encryption ◽  
Association Studies ◽  
Genome Wide Association ◽  
Single Server ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
User Interactions ◽  
Individual Level ◽  
Genome Wide

Genome-wide association studies (GWASs) seek to identify genetic variants associated with a trait, and have been a powerful approach for understanding complex diseases. A critical challenge for GWASs has been the dependence on individual-level data that typically have strict privacy requirements, creating an urgent need for methods that preserve the individual-level privacy of participants. Here, we present a privacy-preserving framework based on several advances in homomorphic encryption and demonstrate that it can perform an accurate GWAS analysis for a real dataset of more than 25,000 individuals, keeping all individual data encrypted and requiring no user interactions. Our extrapolations show that it can evaluate GWASs of 100,000 individuals and 500,000 single-nucleotide polymorphisms (SNPs) in 5.6 h on a single server node (or in 11 min on 31 server nodes running in parallel). Our performance results are more than one order of magnitude faster than prior state-of-the-art results using secure multiparty computation, which requires continuous user interactions, with the accuracy of both solutions being similar. Our homomorphic encryption advances can also be applied to other domains where large-scale statistical analyses over encrypted data are needed.

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 .

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