scholarly journals Higher-order genetic interactions and their contribution to complex traits

2015 ◽  
Vol 31 (1) ◽  
pp. 34-40 ◽  
Author(s):  
Matthew B. Taylor ◽  
Ian M. Ehrenreich
Keyword(s):  
Complex Traits ◽  
Higher Order ◽  
Genetic Interactions

scholarly journals Pairwise genetic interactions modulate lipid plasma levels and cellular uptake

2020 ◽  
Author(s):  
Magdalena Zimon ◽  
Yunfeng Huang ◽  
Anthi Trasta ◽  
Jimmy Z. Liu ◽  
Chia-Yen Chen ◽  
...  
Keyword(s):  
Complex Traits ◽  
Drug Target ◽  
Human Genetics ◽  
Large Population ◽  
Genetic Interactions ◽  
Model Systems ◽  
Lipid Lowering ◽  
Gene Pairs ◽  
Population Sizes ◽  
The Uk

SUMMARYGenetic interactions (GIs), the joint impact of different genes or variants on a phenotype, are foundational to the genetic architecture of complex traits. However, identifying GIs through human genetics is challenging since it necessitates very large population sizes, while findings from model systems not always translate to humans. Here, we combined exome-sequencing and genotyping in the UK Biobank with combinatorial RNA-interference (coRNAi) screening to systematically test for pairwise GIs between 30 lipid GWAS genes. Gene-based protein-truncating variant (PTV) burden analyses from 240,970 exomes revealed additive GIs for APOB with PCSK9 and LPL, respectively. Both, genetics and coRNAi identified additive GIs for 12 additional gene pairs. Overlapping non-additive GIs were detected only for TOMM40 at the APOE locus with SORT1 and NCAN. Our study identifies distinct gene pairs that modulate both, plasma and cellular lipid levels via additive and non-additive effects and nominates drug target pairs for improved lipid-lowering combination therapies.

scholarly journals Pathway Analysis within Multiple Human Ancestries Reveals Novel Signals for Epistasis in Complex Traits

2020 ◽  
Author(s):  
Michael C. Turchin ◽  
Gregory Darnell ◽  
Lorin Crawford ◽  
Sohini Ramachandran
Keyword(s):  
Complex Traits ◽  
African Ancestry ◽  
Genetic Interactions ◽  
European Ancestry ◽  
Model Organisms ◽  
Multiple Model ◽  
Genetic Associations ◽  
Epistatic Effects ◽  
Gwa Studies ◽  
Human Complex

AbstractGenome-wide association (GWA) studies have identified thousands of significant genetic associations in humans across a number of complex traits. However, the majority of these studies focus on linear additive relationships between genotypic and phenotypic variation. Epistasis, or non-additive genetic interactions, has been identified as a major driver of both complex trait architecture and evolution in multiple model organisms; yet, this same phenomenon is not considered to be a significant factor underlying human complex traits. There are two possible reasons for this assumption. First, most large GWA studies are conducted solely with European cohorts; therefore, our understanding of broad-sense heritability for many complex traits is limited to just one ancestry group. Second, current epistasis mapping methods commonly identify significant genetic interactions by exhaustively searching across all possible pairs of SNPs. In these frameworks, estimated epistatic effects size are often small and power can be low due to the multiple testing burden. Here, we present a case study that uses a novel region-based mapping approach to analyze sets of variants for the presence of epistatic effects across six diverse subgroups within the UK Biobank. We refer to this method as the “MArginal ePIstasis Test for Regions” or MAPIT-R. Even with limited sample sizes, we find a total of 245 pathways within the KEGG and REACTOME databases that are significantly enriched for epistatic effects in height and body mass index (BMI), with 67% of these pathways being detected within individuals of African ancestry. As a secondary analysis, we introduce a novel region-based “leave-one-out” approach to localize pathway-level epistatic signals to specific interacting genes in BMI. Overall, our results indicate that non-European ancestry populations may be better suited for the discovery of non-additive genetic variation in human complex traits — further underscoring the need for publicly available, biobank-sized datasets of diverse groups of individuals.

scholarly journals Inferring genetic interactions from comparative fitness data

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Kristina Crona ◽  
Alex Gavryushkin ◽  
Devin Greene ◽  
Niko Beerenwinkel
Keyword(s):  
Evolutionary Biology ◽  
Fitness Landscape ◽  
Higher Order ◽  
Complete Characterization ◽  
Genetic Interactions ◽  
Gene Interactions ◽  
Rank Orders ◽  
Darwinian Fitness ◽  
Parasite Plasmodium

Darwinian fitness is a central concept in evolutionary biology. In practice, however, it is hardly possible to measure fitness for all genotypes in a natural population. Here, we present quantitative tools to make inferences about epistatic gene interactions when the fitness landscape is only incompletely determined due to imprecise measurements or missing observations. We demonstrate that genetic interactions can often be inferred from fitness rank orders, where all genotypes are ordered according to fitness, and even from partial fitness orders. We provide a complete characterization of rank orders that imply higher order epistasis. Our theory applies to all common types of gene interactions and facilitates comprehensive investigations of diverse genetic interactions. We analyzed various genetic systems comprising HIV-1, the malaria-causing parasite Plasmodium vivax, the fungus Aspergillus niger, and the TEM-family of β-lactamase associated with antibiotic resistance. For all systems, our approach revealed higher order interactions among mutations.

scholarly journals Genetic Interactions with Sex Make a Relatively Small Contribution to the Heritability of Complex Traits in Mice

PLoS ONE ◽  
2014 ◽  
Vol 9 (5) ◽  
pp. e96450 ◽  
Author(s):  
Jon Krohn ◽  
Doug Speed ◽  
Rupert Palme ◽  
Chadi Touma ◽  
Richard Mott ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Interactions ◽  
Small Contribution

scholarly journals Establishment of the Avera Twin Register in the Midwest USA

2017 ◽  
Vol 20 (5) ◽  
pp. 414-418 ◽  
Author(s):  
Julie Kittelsrud ◽  
Erik A. Ehli ◽  
Vikki Petersen ◽  
Tammy Jung ◽  
Gonneke Willemsen ◽  
...  
Keyword(s):  
United States ◽  
South Dakota ◽  
Health Information ◽  
Complex Traits ◽  
Family Members ◽  
Higher Order ◽  
Genetic Influences ◽  
Target Area ◽  
Activity Levels ◽  
Health And Disease

The Avera Twin Register (ATR) aims to study environmental and genetic influences on health and disease using a longitudinal repository of biological specimens, survey data, and health information provided by multiples and their family members. The ATR is located in Sioux Falls, South Dakota, which is a rural and frontier area in the Midwestern United States with a density of four people per square kilometer. The target area of the ATR is South Dakota and the four surrounding states: Minnesota, Iowa, North Dakota, and Nebraska. Enrollment of twins and higher-order multiples of all ages and their family members started on May 18, 2016. A description of the first 13 months of enrollment in this longitudinal register will be provided. The ATR will collect longitudinal data on lifestyle, including diet and activity levels, aging, complex traits, and diseases. Upon registration, all participants are genotyped on the Illumina Global Screening Array (GSA) and twins and higher order multiples receive information on their zygosity. The ATR aims to contribute to large international GWAS consortia and collaborates closely with the Netherlands Twin Register, allowing for the comparison of collected data and analyses of results. In addition, the ATR will address twin-specific questions.

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 Pairwise and higher-order genetic interactions during the evolution of a tRNA

Nature ◽  
2018 ◽  
Vol 558 (7708) ◽  
pp. 117-121 ◽  
Author(s):  
Júlia Domingo ◽  
Guillaume Diss ◽  
Ben Lehner
Keyword(s):  
Higher Order ◽  
Genetic Interactions

The Causes and Consequences of Genetic Interactions (Epistasis)

2019 ◽  
Vol 20 (1) ◽  
pp. 433-460 ◽  
Author(s):  
Júlia Domingo ◽  
Pablo Baeza-Centurion ◽  
Ben Lehner
Keyword(s):  
Molecular Level ◽  
Higher Order ◽  
Genetic Interactions ◽  
Current Understanding ◽  
Specific Interactions ◽  
Concerted Effort ◽  
Genetic Prediction ◽  
Genetic Context

The same mutation can have different effects in different individuals. One important reason for this is that the outcome of a mutation can depend on the genetic context in which it occurs. This dependency is known as epistasis. In recent years, there has been a concerted effort to quantify the extent of pairwise and higher-order genetic interactions between mutations through deep mutagenesis of proteins and RNAs. This research has revealed two major components of epistasis: nonspecific genetic interactions caused by nonlinearities in genotype-to-phenotype maps, and specific interactions between particular mutations. Here, we provide an overview of our current understanding of the mechanisms causing epistasis at the molecular level, the consequences of genetic interactions for evolution and genetic prediction, and the applications of epistasis for understanding biology and determining macromolecular structures.

scholarly journals Proteostasis environment shapes higher-order epistasis operating on antibiotic resistance

2018 ◽  
Author(s):  
Rafael F. Guerrero ◽  
Samuel V. Scarpino ◽  
João V. Rodrigues ◽  
Daniel L. Hartl ◽  
C. Brandon Ogbunugafor
Keyword(s):  
Quality Control ◽  
Complex Traits ◽  
Bacterial Resistance ◽  
Protein Quality ◽  
Bacterial Species ◽  
Protein Quality Control ◽  
Higher Order ◽  
Cell Physiology ◽  
Resistance To Antibiotics ◽  
Species Specific

ABSTRACTRecent studies have shown that higher-order epistasis is ubiquitous and can have large effects on complex traits. Yet, we lack frameworks for understanding how epistatic interactions are influenced by basic aspects of cell physiology. In this study, we assess how protein quality control machinery—a critical component of cell physiology—affects epistasis for different traits related to bacterial resistance to antibiotics. Specifically, we attempt to disentangle the interactions between different protein quality control genetic backgrounds and two sets of mutations: (i) SNPs associated with resistance to antibiotics in an essential bacterial enzyme (dihydrofolate reductase, or DHFR) and (ii) differing DHFR bacterial species-specific amino acid background sequences (Escherichia coli, Listeria grayi, and Chlamydia muridarum). In doing so, we add nuance to the generic observation that non-linear genetic interactions are widespread and capricious in nature, by proposing a mechanistically-grounded analysis of how proteostasis shapes epistasis. These findings simultaneously fortify and demystify the role of environmental context in modulating higher-order epistasis, with direct implications for evolutionary theory, genetic modification technology, and efforts to manage antimicrobial resistance.

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