scholarly journals The metabolome as a link in the genotype-phenotype map for peroxide resistance in the fruit fly, Drosophila melanogaster

2019 ◽  
Author(s):  
Benjamin R Harrison ◽  
Lu Wang ◽  
Erika Gajda ◽  
Elise V. Hoffman ◽  
Brian Y. Chung ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Feeding Behavior ◽  
Complex Traits ◽  
Metabolic Response ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Fruit Fly ◽  
Metabolomic Data ◽  
H2o2 Resistance ◽  
Drosophila Genetic Reference Panel

Abstract Background Genetic association studies that seek to explain the inheritance of complex traits typically fail to explain more than a small fraction of the heritability of the trait under study. Thus we are left with a gap in the map from genotype to phenotype. Several approaches have been used to fill this gap, including those that attempt to map endophenotype such as the transcriptome, proteome or metabolome, that underlie complex traits. Here we used metabolomics to explore the nature of genetic variation for hydrogen peroxide (H­2O2) resistance in the sequenced inbred Drosophila Genetic Reference Panel (DGRP). Results We first studied genetic variation for H2O2 resistance in 180 DGRP lines and identify the insulin signaling modulator u-shaped and several regulators of feeding behavior. We then profiled a portion of the aqueous metabolome in subsets of eight ‘high resistance’ lines and eight ‘low resistance’ lines. We used these lines to represent collections of genotypes that were either resistant or sensitive to the stressor, effectively modeling a discrete trait. Across the range of genotypes in both populations, flies exhibited surprising consistency in their metabolomic signature of resistance. Metabolomic profiles were also able to distinguish stress-resistant from stress-sensitive flies with greater accuracy than the genotype of these same lines. Furthermore, we found a metabolic response to H2O2 that was shared among sensitive, but not resistant genotypes. Metabolomic data further implicated at least two pathways, glycogen and folate metabolism, as determinants of sensitivity to H2O2. We also discovered a confounding effect of feeding behavior on assays involving supplemented food. Conclusions This work suggests that the metabolome can be a point of convergence for genetic variation influencing complex traits, and efficiently elucidate the mechanisms underlying this trait variation.

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 Recent advances in genetic predisposition to clinical acute lung injury

2009 ◽  
Vol 296 (5) ◽  
pp. L713-L725 ◽  
Author(s):  
Li Gao ◽  
Kathleen C. Barnes
Keyword(s):  
Acute Lung Injury ◽  
Lung Injury ◽  
Candidate Genes ◽  
Genetic Variants ◽  
Complex Traits ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Special Focus ◽  
Disease Manifestation ◽  
The Impact

It has been well established that acute lung injury (ALI), and the more severe presentation of acute respiratory distress syndrome (ARDS), constitute complex traits characterized by a multigenic and multifactorial etiology. Identification and validation of genetic variants contributing to disease susceptibility and severity has been hampered by the profound heterogeneity of the clinical phenotype and the role of environmental factors, which includes treatment, on outcome. The critical nature of ALI and ARDS, compounded by the impact of phenotypic heterogeneity, has rendered the amassing of sufficiently powered studies especially challenging. Nevertheless, progress has been made in the identification of genetic variants in select candidate genes, which has enhanced our understanding of the specific pathways involved in disease manifestation. Identification of novel candidate genes for which genetic association studies have confirmed a role in disease has been greatly aided by the powerful tool of high-throughput expression profiling. This article will review these studies to date, summarizing candidate genes associated with ALI and ARDS, acknowledging those that have been replicated in independent populations, with a special focus on the specific pathways for which candidate genes identified so far can be clustered.

scholarly journals Automated Detection of Informative Combined Effects in Genetic Association Studies of Complex Traits

2003 ◽  
Vol 13 (8) ◽  
pp. 1952-1960
Author(s):  
Nadia Tahri-Daizadeh ◽  
David-Alexandre Tregouet ◽  
Viviane Nicaud ◽  
Nicolas Manuel ◽  
François Cambien ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Complex Traits ◽  
Association Studies ◽  
Density Lipoprotein ◽  
Genetic Association Studies ◽  
Relevant Information ◽  
Cholesterol Concentration ◽  
Common Disease ◽  
Automated Method ◽  
Gene Variability

There is a growing body of evidence suggesting that the relationships between gene variability and common disease are more complex than initially thought and require the exploration of the whole polymorphism of candidate genes as well as several genes belonging to biological pathways. When the number of polymorphisms is relatively large and the structure of the relationships among them complex, the use of data mining tools to extract the relevant information is a necessity. Here, we propose an automated method for the detection of informative combined effects (DICE) among several polymorphisms (and nongenetic covariates) within the framework of association studies. The algorithm combines the advantages of the regressive approaches with those of data exploration tools. Importantly, DICE considers the problem of interaction between polymorphisms as an effect of interest and not as a nuisance effect. We illustrate the method with three applications on the relationship between (1) the P-selectin gene and myocardial infarction, (2) the cholesteryl ester transfer protein gene and plasma high-density-lipoprotein cholesterol concentration, and (3) genes of the renin-angiotensin-aldosterone system and myocardial infarction. The applications demonstrated that the method was able to recover results already found using other approaches, but in addition detected biologically sensible effects not previously described.

scholarly journals Statistical analysis of genetic interactions

2010 ◽  
Vol 92 (5-6) ◽  
pp. 443-459 ◽  
Author(s):  
NENGJUN YI
Keyword(s):  
Statistical Analysis ◽  
Complex Traits ◽  
Association Studies ◽  
Computer Software ◽  
Genetic Association Studies ◽  
Genetic Interactions ◽  
Future Research ◽  
Genome Wide Association Studies ◽  
Gene Environment ◽  
Genetic And Environmental Factors

SummaryMany common human diseases and complex traits are highly heritable and influenced by multiple genetic and environmental factors. Although genome-wide association studies (GWAS) have successfully identified many disease-associated variants, these genetic variants explain only a small proportion of the heritability of most complex diseases. Genetic interactions (gene–gene and gene–environment) substantially contribute to complex traits and diseases and could be one of the main sources of the missing heritability. This paper provides an overview of the available statistical methods and related computer software for identifying genetic interactions in animal and plant experimental crosses and human genetic association studies. The main discussion falls under the three broad issues in statistical analysis of genetic interactions: the definition, detection and interpretation of genetic interactions. Recently developed methods based on modern techniques for high-dimensional data are reviewed, including penalized likelihood approaches and hierarchical models; the relationships between these methods are also discussed. I conclude this review by highlighting some areas of future research.

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.

WORLDWIDE GENETIC VARIATION IN THE REELIN GENE: IMPLICATIONS FOR GENETIC ASSOCIATION STUDIES

2008 ◽  
Vol 102 (1-3) ◽  
pp. 189
Author(s):  
Araceli Rosa ◽  
Ferran Casals ◽  
Anna Ferrer-Admetlla ◽  
Michelle Gardner ◽  
Jaume Bertranpetit ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Genetic Association ◽  
Association Studies ◽  
Genetic Association Studies

scholarly journals Polygenic inheritance, GWAS, polygenic risk scores, and the search for functional variants

2020 ◽  
Vol 117 (32) ◽  
pp. 18924-18933
Author(s):  
Daniel J. M. Crouch ◽  
Walter F. Bodmer
Keyword(s):  
Genetic Association ◽  
Complex Traits ◽  
Rare Variants ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Mendelian Inheritance ◽  
Risk Scores ◽  
Polygenic Inheritance ◽  
Polygenic Risk ◽  
Functional Variants

The reconciliation between Mendelian inheritance of discrete traits and the genetically based correlation between relatives for quantitative traits was Fisher’s infinitesimal model of a large number of genetic variants, each with very small effects, whose causal effects could not be individually identified. The development of genome-wide genetic association studies (GWAS) raised the hope that it would be possible to identify single polymorphic variants with identifiable functional effects on complex traits. It soon became clear that, with larger and larger GWAS on more and more complex traits, most of the significant associations had such small effects, that identifying their individual functional effects was essentially hopeless. Polygenic risk scores that provide an overall estimate of the genetic propensity to a trait at the individual level have been developed using GWAS data. These provide useful identification of groups of individuals with substantially increased risks, which can lead to recommendations of medical treatments or behavioral modifications to reduce risks. However, each such claim will require extensive investigation to justify its practical application. The challenge now is to use limited genetic association studies to find individually identifiable variants of significant functional effect that can help to understand the molecular basis of complex diseases and traits, and so lead to improved disease prevention and treatment. This can best be achieved by 1) the study of rare variants, often chosen by careful candidate assessment, and 2) the careful choice of phenotypes, often extremes of a quantitative variable, or traits with relatively high heritability.

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