scholarly journals The Y Chromosome: A Complex Locus for Genetic Analyses of Complex Human Traits

Genes ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1273
Author(s):  
Katherine Parker ◽  
A. Mesut Erzurumluoglu ◽  
Santiago Rodriguez
Keyword(s):  
Population Genetics ◽  
Genetic Variation ◽  
Y Chromosome ◽  
Complex Traits ◽  
Association Studies ◽  
Epidemiological Studies ◽  
Complex Locus ◽  
Complex Human Traits ◽  
Human Complex ◽  
Human Y Chromosome

The Human Y chromosome (ChrY) has been demonstrated to be a powerful tool for phylogenetics, population genetics, genetic genealogy and forensics. However, the importance of ChrY genetic variation in relation to human complex traits is less clear. In this review, we summarise existing evidence about the inherent complexities of ChrY variation and their use in association studies of human complex traits. We present and discuss the specific particularities of ChrY genetic variation, including Y chromosomal haplogroups, that need to be considered in the design and interpretation of genetic epidemiological studies involving ChrY.

scholarly journals Population Admixture May Appear to Mask, Change or Reverse Genetic Effects of Genes Underlying Complex Traits

Genetics ◽  
2001 ◽  
Vol 159 (3) ◽  
pp. 1319-1323
Author(s):  
Hong-Wen Deng
Keyword(s):  
Population Genetics ◽  
False Positive ◽  
Complex Traits ◽  
Association Studies ◽  
Genetic Effects ◽  
Population Admixture ◽  
Reverse Genetic ◽  
Positive Identification ◽  
False Positive Identification

Abstract Association studies using random population samples are increasingly being applied in the identification and inference of genetic effects of genes underlying complex traits. It is well recognized that population admixture may yield false-positive identification of genetic effects for complex traits. However, it is less well appreciated that population admixture can appear to mask, change, or reverse true genetic effects for genes underlying complex traits. By employing a simple population genetics model, we explore the effects and the conditions of population admixture in masking, changing, or even reversing true genetic effects of genes underlying complex traits.

scholarly journals Dominant Genetic Variation and Missing Heritability for Human Complex Traits: Insights from Twin versus Genome-wide Common SNP Models

2015 ◽  
Vol 97 (5) ◽  
pp. 708-714 ◽  
Author(s):  
Xu Chen ◽  
Ralf Kuja-Halkola ◽  
Iffat Rahman ◽  
Johannes Arpegård ◽  
Alexander Viktorin ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Complex Traits ◽  
Missing Heritability ◽  
Genome Wide ◽  
Human Complex

scholarly journals Bayesian weighted Mendelian randomization for causal inference based on summary statistics

2019 ◽  
Author(s):  
Jia Zhao ◽  
Jingsi Ming ◽  
Xianghong Hu ◽  
Gang Chen ◽  
Jin Liu ◽  
...  
Keyword(s):  
Causal Inference ◽  
Complex Traits ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Simulation Studies ◽  
Genome Wide ◽  
Complex Human Traits

Abstract Motivation The results from Genome-Wide Association Studies (GWAS) on thousands of phenotypes provide an unprecedented opportunity to infer the causal effect of one phenotype (exposure) on another (outcome). Mendelian randomization (MR), an instrumental variable (IV) method, has been introduced for causal inference using GWAS data. Due to the polygenic architecture of complex traits/diseases and the ubiquity of pleiotropy, however, MR has many unique challenges compared to conventional IV methods. Results We propose a Bayesian weighted Mendelian randomization (BWMR) for causal inference to address these challenges. In our BWMR model, the uncertainty of weak effects owing to polygenicity has been taken into account and the violation of IV assumption due to pleiotropy has been addressed through outlier detection by Bayesian weighting. To make the causal inference based on BWMR computationally stable and efficient, we developed a variational expectation-maximization (VEM) algorithm. Moreover, we have also derived an exact closed-form formula to correct the posterior covariance which is often underestimated in variational inference. Through comprehensive simulation studies, we evaluated the performance of BWMR, demonstrating the advantage of BWMR over its competitors. Then we applied BWMR to make causal inference between 130 metabolites and 93 complex human traits, uncovering novel causal relationship between exposure and outcome traits. Availability and implementation The BWMR software is available at https://github.com/jiazhao97/BWMR. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Retrospective analysis of main and interaction effects in genetic association studies of human complex traits

BMC Genetics ◽  
2007 ◽  
Vol 8 (1) ◽  
Author(s):  
Qihua Tan ◽  
Lene Christiansen ◽  
Charlotte Brasch-Andersen ◽  
Jing Hua Zhao ◽  
Shuxia Li ◽  
...  
Keyword(s):  
Retrospective Analysis ◽  
Genetic Association ◽  
Complex Traits ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Interaction Effects ◽  
Human Complex

scholarly journals Prospects and pitfalls in whole genome association studies

2005 ◽  
Vol 360 (1460) ◽  
pp. 1589-1595 ◽  
Author(s):  
Robert W Lawrence ◽  
David M Evans ◽  
Lon R Cardon
Keyword(s):  
Genetic Variation ◽  
Genetic Markers ◽  
Complex Traits ◽  
Large Scale ◽  
Association Studies ◽  
Whole Genome ◽  
Common Genetic Variation ◽  
Close Attention ◽  
Genome Association ◽  
Whole Genome Association

Recent large-scale studies of common genetic variation throughout the human genome are making it feasible to conduct whole genome studies of genotype–phenotype associations. Such studies have the potential to uncover novel contributors to common complex traits and thus lead to insights into the aetiology of multifactorial phenotypes. Despite this promise, it is important to recognize that the availability of genetic markers and the ability to assay them at realistic cost does not guarantee success of this approach. There are a number of practical issues that require close attention, some forms of allelic architecture are not readily amenable to the association approach with even the most rigorous design, and doubtless new hurdles will emerge as the studies begin. Here we discuss the promise and current challenges of the whole genome approach, and raise some issues to consider in interpreting the results of the first whole genome studies.

scholarly journals Genetic correlates of socio-economic status influence the pattern of shared heritability across mental health traits

2020 ◽  
Author(s):  
Andries T. Marees ◽  
Dirk J.A. Smit ◽  
Abdel Abdellaoui ◽  
Michel G. Nivard ◽  
Wim van den Brink ◽  
...  
Keyword(s):  
Mental Health ◽  
Genetic Variation ◽  
Mental Health Problems ◽  
Association Studies ◽  
Economic Status ◽  
Genetic Correlations ◽  
Epidemiological Studies ◽  
Health Problems ◽  
Genome Wide Association Studies ◽  
Socio Economic Status

AbstractEpidemiological studies show high comorbidity between different mental health problems, indicating that individuals with a diagnosis of one disorder are more likely to develop other mental health problems. Genetic studies reveal substantial sharing of genetic risk factors across mental health traits. However, mental health is genetically correlated with socio-economic status (SES) and it is therefore important to investigate and disentangle the genetic relationship between mental health and SES. We used summary statistics from large genome-wide association studies (average N∼160,000) to estimate the genetic overlap across nine psychiatric disorders and seven substance use traits and explored the genetic influence of three different indicators of SES. Using Genomic SEM, we show significant changes in patterns of genetic correlations after partialling out SES-associated genetic variation. Our approach allows the separation of disease-specific genetic variation and genetic variation shared with SES, thereby improving our understanding of the genetic architecture of mental health.

scholarly journals Phenome-wide search for pleiotropic loci highlights key genes and molecular pathways for human complex traits

2019 ◽  
Author(s):  
Anton E. Shikov ◽  
Alexander V. Predeus ◽  
Yury A. Barbitoff
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Human Genetics ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Statistical Framework ◽  
Genome Wide ◽  
Mhc Locus ◽  
High Degree ◽  
Human Complex

AbstractOver recent decades, genome-wide association studies (GWAS) have dramatically changed the understanding of human genetics. A recent genetic data release by UK Biobank has allowed many researchers worldwide to have comprehensive look into the genetic architecture of thousands of human phenotypes. In this study, we developed a novel statistical framework to assess phenome-wide significance and genetic pleiotropy across the human phenome based on GWAS summary statistics. We demonstrate widespread sharing of genetic architecture components between distinct groups of traits. Apart from known multiple associations inside the MHC locus, we discover high degree of pleiotropy for genes involved in immune system function, apoptosis, hemostasis cascades, as well as lipid and xenobiotic metabolism. We find several notable examples of novel pleiotropic loci (e.g., the MIR2113 microRNA broadly associated with cognition), and provide several possible mechanisms for these association signals. Our results allow for a functional phenome-wide look into the shared components of genetic architecture of human complex traits, and highlight crucial genes and pathways for their development.

scholarly journals Dominance Genetic Variation Contributes Little to the Missing Heritability for Human Complex Traits

2015 ◽  
Vol 96 (3) ◽  
pp. 377-385 ◽  
Author(s):  
Zhihong Zhu ◽  
Andrew Bakshi ◽  
Anna A.E. Vinkhuyzen ◽  
Gibran Hemani ◽  
Sang Hong Lee ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Complex Traits ◽  
Missing Heritability ◽  
Human Complex

Genetics

2018 ◽  
Author(s):  
Carol Kan ◽  
Ma-Li Wong
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Genetic Correlations ◽  
Twin Studies ◽  
Epidemiological Studies ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Small Magnitude ◽  
Gene Environment ◽  
Genetic Overlap

An association between type 2 diabetes mellitus (T2DM) and depression has been reported in epidemiological studies. Finding a genetic overlap between T2DM and depression will provide evidence to support a common biological pathway to both disorders. Genetic correlations observed from twin studies indicate that a small magnitude of the variance in liability can be attributed to genetic factors. However, no genetic overlap has been observed between T2DM and depression in genome-wide association studies using both the polygenic score and the linkage disequilibrium score regression approaches. Clarifying the shared heritability between these two complex traits is an important next step towards better therapy and treatment. Another area that needs to be explored is gene–environment interaction, since genotypes can affect an individual’s responses to the environment and environment can differentially affect genotypes expression.

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