scholarly journals A Two-State Epistasis Model Reduces Missing Heritability of Complex Traits

2015 ◽  
Author(s):  
Kerry L. Bubb ◽  
Christine Queitsch
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Additive Model ◽  
Twin Studies ◽  
Additive Models ◽  
Genome Wide Association Studies ◽  
Missing Heritability ◽  
Epistatic Interactions ◽  
Epistasis Model ◽  
Genome Wide

ABSTRACTDespite decade-long efforts, the genetic underpinnings of many complex traits and diseases remain largely elusive. It is increasingly recognized that a purely additive model, upon which most genome-wide association studies (GWAS) rely, is insufficient. Although thousands of significant trait-associated loci have been identified, purely additive models leave much of the inferred genetic variance unexplained. Several factors have been invoked to explain the ‘missing heritability’, including epistasis. Accounting for all possible epistatic interactions is computationally complex and requires very large samples. Here, we propose a simple two-state epistasis model, in which individuals show either high or low variant penetrance with respect to a certain trait. The use of this model increases the power to detect additive trait-associated loci. We show that this model is consistent with current GWAS results and improves fit with heritability observations based on twin studies. We suggest that accounting for variant penetrance will significantly increase our power to identify underlying additive loci.

scholarly journals Recessive Genome-wide Meta-analysis Illuminates Genetic Architecture of Type 2 Diabetes

2021 ◽  
Author(s):  
Mark J. O’Connor ◽  
Philip Schroeder ◽  
Alicia Huerta-Chagoya ◽  
Paula Cortés-Sánchez ◽  
Silvía Bonàs-Guarch ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Complex Traits ◽  
Association Studies ◽  
Meta Analysis ◽  
Low Frequency ◽  
Additive Models ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Genome Wide

Most genome-wide association studies (GWAS) of complex traits are performed using models with additive allelic effects. Hundreds of loci associated with type 2 diabetes have been identified using this approach. Additive models, however, can miss loci with recessive effects, thereby leaving potentially important genes undiscovered. We conducted the largest GWAS meta-analysis using a recessive model for type 2 diabetes. Our discovery sample included 33,139 cases and 279,507 controls from seven European-ancestry cohorts including the UK Biobank. We identified 51 loci associated with type 2 diabetes, including five variants undetected by prior additive analyses. Two of the five had minor allele frequency less than 5% and were each associated with more than doubled risk in homozygous carriers. Using two additional cohorts, FinnGen and a Danish cohort, we replicated three of the variants, including one of the low-frequency variants, rs115018790, which had an odds ratio in homozygous carriers of 2.56 (95% CI 2.05-3.19, <i>P</i>=1´10<sup>-16</sup>) and a stronger effect in men than in women (interaction <i>P</i>=7´10<sup>-7</sup>). The signal was associated with multiple diabetes-related traits, with homozygous carriers showing a 10% decrease in LDL and a 20% increase in triglycerides, and colocalization analysis linked this signal to reduced expression of the nearby <i>PELO</i> gene. These results demonstrate that recessive models, when compared to GWAS using the additive approach, can identify novel loci, including large-effect variants with pathophysiological consequences relevant to type 2 diabetes.

scholarly journals Recessive Genome-wide Meta-analysis Illuminates Genetic Architecture of Type 2 Diabetes

2021 ◽  
Author(s):  
Mark J. O’Connor ◽  
Philip Schroeder ◽  
Alicia Huerta-Chagoya ◽  
Paula Cortés-Sánchez ◽  
Silvía Bonàs-Guarch ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Complex Traits ◽  
Association Studies ◽  
Meta Analysis ◽  
Low Frequency ◽  
Additive Models ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Genome Wide

Most genome-wide association studies (GWAS) of complex traits are performed using models with additive allelic effects. Hundreds of loci associated with type 2 diabetes have been identified using this approach. Additive models, however, can miss loci with recessive effects, thereby leaving potentially important genes undiscovered. We conducted the largest GWAS meta-analysis using a recessive model for type 2 diabetes. Our discovery sample included 33,139 cases and 279,507 controls from seven European-ancestry cohorts including the UK Biobank. We identified 51 loci associated with type 2 diabetes, including five variants undetected by prior additive analyses. Two of the five had minor allele frequency less than 5% and were each associated with more than doubled risk in homozygous carriers. Using two additional cohorts, FinnGen and a Danish cohort, we replicated three of the variants, including one of the low-frequency variants, rs115018790, which had an odds ratio in homozygous carriers of 2.56 (95% CI 2.05-3.19, <i>P</i>=1´10<sup>-16</sup>) and a stronger effect in men than in women (interaction <i>P</i>=7´10<sup>-7</sup>). The signal was associated with multiple diabetes-related traits, with homozygous carriers showing a 10% decrease in LDL and a 20% increase in triglycerides, and colocalization analysis linked this signal to reduced expression of the nearby <i>PELO</i> gene. These results demonstrate that recessive models, when compared to GWAS using the additive approach, can identify novel loci, including large-effect variants with pathophysiological consequences relevant to type 2 diabetes.

scholarly journals A time-varying group sparse additive model for genome-wide association studies of dynamic complex traits

2016 ◽  
Vol 32 (19) ◽  
pp. 2903-2910 ◽  
Author(s):  
Micol Marchetti-Bowick ◽  
Junming Yin ◽  
Judie A. Howrylak ◽  
Eric P. Xing
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Additive Model ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Time Varying ◽  
Genome Wide

scholarly journals Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

2016 ◽  
Vol 283 (1835) ◽  
pp. 20160569 ◽  
Author(s):  
M. E. Goddard ◽  
K. E. Kemper ◽  
I. M. MacLeod ◽  
A. J. Chamberlain ◽  
B. J. Hayes
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Quantitative Traits ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Crop Breeding ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Phenotype Identification

Complex or quantitative traits are important in medicine, agriculture and evolution, yet, until recently, few of the polymorphisms that cause variation in these traits were known. Genome-wide association studies (GWAS), based on the ability to assay thousands of single nucleotide polymorphisms (SNPs), have revolutionized our understanding of the genetics of complex traits. We advocate the analysis of GWAS data by a statistical method that fits all SNP effects simultaneously, assuming that these effects are drawn from a prior distribution. We illustrate how this method can be used to predict future phenotypes, to map and identify the causal mutations, and to study the genetic architecture of complex traits. The genetic architecture of complex traits is even more complex than previously thought: in almost every trait studied there are thousands of polymorphisms that explain genetic variation. Methods of predicting future phenotypes, collectively known as genomic selection or genomic prediction, have been widely adopted in livestock and crop breeding, leading to increased rates of genetic improvement.

Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies

2021 ◽  
Vol 42 (1) ◽  
Author(s):  
Dinesh K. Saini ◽  
Yuvraj Chopra ◽  
Jagmohan Singh ◽  
Karansher S. Sandhu ◽  
Anand Kumar ◽  
...  
Keyword(s):  
Complex Traits ◽  
Comprehensive Evaluation ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Mapping Complex Traits

Abstract 1443: Systems-Based Analysis of HDL Metabolism in a Mouse Intercross between Strains CAST and C57BL6/J (CxB)

Circulation ◽  
2008 ◽  
Vol 118 (suppl_18) ◽  
Author(s):  
Margarete Mehrabian ◽  
Charles Farber ◽  
Peter Langfelder ◽  
Anatole Ghazalpour ◽  
Zhiqiang Zhou ◽  
...  
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Plasma Lipid ◽  
Heart Association ◽  
Hdl Cholesterol ◽  
Chow Diet ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Genomic Study ◽  
Hdl Metabolism

A recent meta-analysis of three large genome-wide association studies for HDL cholesterol levels revealed several highly significant associations, but altogether these explained less than 10% of the population variance of HDL. Since HDL levels are highly heritable, with heritability estimated at 50–70% in many studies, there are clearly many additional genes, and probably complex genetic and environmental interactions, involved in HDL metabolism. Thus, if “personalized medicine” is to become a reality, these complex factors must be addressed. Combined genetic-genomic approaches have rejuvenated the analysis of complex traits using mouse models, and here report an integrative genomic study of HDL in a large mouse cross. We previously reported the identification of loci associated with HDL cholesterol concentrations using a CXB F2 intercross. We have now generated a much larger CXB cross, consisting of 438 mice, and have integrated genome wide gene expression analysis of liver and adipose with quantitative trait locus (QTL) mapping and causality modeling. These studies were carried out on mice fed a low fat, chow diet and then switched to a high fat, ’Western’ diet. QTL analysis on the clinical traits using R/QTL (http://cran.r-project.org/) revealed a complex inheritance pattern with significant LOD scores at 9 loci, on chromosomes 1,2,4,5,8,9,10,16,18. Of these loci, 6 (chr: 1,4,5,10,16,18) were seen to be involved in genetic-dietary regulation of HDL cholesterol. Expression QTLs (eQTL) were determined using Agilent microarrays for 23,624 transcripts. Genes expressed within a 1-LOD support interval or correlated with HDL (p<2.7E-11) in both adipose and liver were identified. Using Network Edge Orienting (NEO) methods, causal relationships between the identified genes, related QTL peak markers and HDL levels were accessed. The genes were then ranked based on the NEO scores. In liver the highest ranked genes were associated with mitochondrial, ER and golgi trafficking. In adipose, on the other hand, pathways associated with cell signaling, transcription regulation and protein ubiquitation were predicted to be causal for HDL levels. In conclusion, our results reveal a large number of novel pathways and candidate genes for plasma lipid metabolism. This research has received full or partial funding support from the American Heart Association, AHA Western States Affiliate (California, Nevada & Utah).

scholarly journals Insights into Dyslexia Genetics Research from the Last Two Decades

2021 ◽  
Vol 12 (1) ◽  
pp. 27
Author(s):  
Florina Erbeli ◽  
Marianne Rice ◽  
Silvia Paracchini
Keyword(s):  
Language Disorder ◽  
Association Studies ◽  
Neurodevelopmental Disorder ◽  
Molecular Genetic ◽  
Genetic Research ◽  
Twin Studies ◽  
Genome Wide Association Studies ◽  
Genetics Research ◽  
Genome Wide ◽  
Genetic Risks

Dyslexia, a specific reading disability, is a common (up to 10% of children) and highly heritable (~70%) neurodevelopmental disorder. Behavioral and molecular genetic approaches are aimed towards dissecting its significant genetic component. In the proposed review, we will summarize advances in twin and molecular genetic research from the past 20 years. First, we will briefly outline the clinical and educational presentation and epidemiology of dyslexia. Next, we will summarize results from twin studies, followed by molecular genetic research (e.g., genome-wide association studies (GWASs)). In particular, we will highlight converging key insights from genetic research. (1) Dyslexia is a highly polygenic neurodevelopmental disorder with a complex genetic architecture. (2) Dyslexia categories share a large proportion of genetics with continuously distributed measures of reading skills, with shared genetic risks also seen across development. (3) Dyslexia genetic risks are shared with those implicated in many other neurodevelopmental disorders (e.g., developmental language disorder and dyscalculia). Finally, we will discuss the implications and future directions. As the diversity of genetic studies continues to increase through international collaborate efforts, we will highlight the challenges in advances of genetics discoveries in this field.

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.

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