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Epigenetic Inheritance and the Missing Heritability Problem

Slatkin, Montgomery
Fonte: Genetics Society of America Publicador: Genetics Society of America
Tipo: Artigo de Revista Científica
Publicado em /07/2009 EN
Relevância na Pesquisa
46.53%
Epigenetic phenomena, and in particular heritable epigenetic changes, or transgenerational effects, are the subject of much discussion in the current literature. This article presents a model of transgenerational epigenetic inheritance and explores the effect of epigenetic inheritance on the risk and recurrence risk of a complex disease. The model assumes that epigenetic modifications of the genome are gained and lost at specified rates and that each modification contributes multiplicatively to disease risk. The potentially high rate of loss of epigenetic modifications causes the probability of identity in state in close relatives to be smaller than is implied by their relatedness. As a consequence, the recurrence risk to close relatives is reduced. Although epigenetic modifications may contribute substantially to average risk, they will not contribute much to recurrence risk and heritability unless they persist on average for many generations. If they do persist for long times, they are equivalent to mutations and hence are likely to be in linkage disequilibrium with SNPs surveyed in genomewide association studies. Thus epigenetic modifications are a potential solution to the problem of missing causality of complex diseases but not to the problem of missing heritability. The model highlights the need for empirical estimates of the persistence times of heritable epialleles.

Finding the missing heritability of complex diseases

Manolio, Teri A.; Collins, Francis S.; Cox, Nancy J.; Goldstein, David B.; Hindorff, Lucia A.; Hunter, David J.; McCarthy, Mark I.; Ramos, Erin M.; Cardon, Lon R.; Chakravarti, Aravinda; Cho, Judy H.; Guttmacher, Alan E.; Kong, Augustine; Kruglyak, Leonid
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em 08/10/2009 EN
Relevância na Pesquisa
46.44%
Genome-wide association studies have identified hundreds of genetic variants associated with complex human diseases and traits, and have provided valuable insights into their genetic architecture. Most variants identified so far confer relatively small increments in risk, and explain only a small proportion of familial clustering, leading many to question how the remaining, ‘missing’ heritability can be explained. Here we examine potential sources of missing heritability and propose research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.

Beyond Missing Heritability: Prediction of Complex Traits

Makowsky, Robert; Pajewski, Nicholas M.; Klimentidis, Yann C.; Vazquez, Ana I.; Duarte, Christine W.; Allison, David B.; de los Campos, Gustavo
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
46.44%
Despite rapid advances in genomic technology, our ability to account for phenotypic variation using genetic information remains limited for many traits. This has unfortunately resulted in limited application of genetic data towards preventive and personalized medicine, one of the primary impetuses of genome-wide association studies. Recently, a large proportion of the “missing heritability” for human height was statistically explained by modeling thousands of single nucleotide polymorphisms concurrently. However, it is currently unclear how gains in explained genetic variance will translate to the prediction of yet-to-be observed phenotypes. Using data from the Framingham Heart Study, we explore the genomic prediction of human height in training and validation samples while varying the statistical approach used, the number of SNPs included in the model, the validation scheme, and the number of subjects used to train the model. In our training datasets, we are able to explain a large proportion of the variation in height (h2 up to 0.83, R2 up to 0.96). However, the proportion of variance accounted for in validation samples is much smaller (ranging from 0.15 to 0.36 depending on the degree of familial information used in the training dataset). While such R2 values vastly exceed what has been previously reported using a reduced number of pre-selected markers (<0.10)...

Lessons from Model Organisms: Phenotypic Robustness and Missing Heritability in Complex Disease

Queitsch, Christine; Carlson, Keisha D.; Girirajan, Santhosh
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
46.69%
Genetically tractable model organisms from phages to mice have taught us invaluable lessons about fundamental biological processes and disease-causing mutations. Owing to technological and computational advances, human biology and the causes of human diseases have become accessible as never before. Progress in identifying genetic determinants for human diseases has been most remarkable for Mendelian traits. In contrast, identifying genetic determinants for complex diseases such as diabetes, cancer, and cardiovascular and neurological diseases has remained challenging, despite the fact that these diseases cluster in families. Hundreds of variants associated with complex diseases have been found in genome-wide association studies (GWAS), yet most of these variants explain only a modest amount of the observed heritability, a phenomenon known as “missing heritability.” The missing heritability has been attributed to many factors, mainly inadequacies in genotyping and phenotyping. We argue that lessons learned about complex traits in model organisms offer an alternative explanation for missing heritability in humans. In diverse model organisms, phenotypic robustness differs among individuals, and those with decreased robustness show increased penetrance of mutations and express previously cryptic genetic variation. We propose that phenotypic robustness also differs among humans and that individuals with lower robustness will be more responsive to genetic and environmental perturbations and hence susceptible to disease. Phenotypic robustness is a quantitative trait that can be accurately measured in model organisms...

Finding Missing Heritability in Less Significant Loci and Allelic Heterogeneity: Genetic Variation in Human Height

Zhang, Ge; Karns, Rebekah; Sun, Guangyun; Indugula, Subba Rao; Cheng, Hong; Havas-Augustin, Dubravka; Novokmet, Natalija; Durakovic, Zijad; Missoni, Sasa; Chakraborty, Ranajit; Rudan, Pavao; Deka, Ranjan
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 12/12/2012 EN
Relevância na Pesquisa
46.44%
Genome-wide association studies (GWAS) have identified many common variants associated with complex traits in human populations. Thus far, most reported variants have relatively small effects and explain only a small proportion of phenotypic variance, leading to the issues of ‘missing’ heritability and its explanation. Using height as an example, we examined two possible sources of missing heritability: first, variants with smaller effects whose associations with height failed to reach genome-wide significance and second, allelic heterogeneity due to the effects of multiple variants at a single locus. Using a novel analytical approach we examined allelic heterogeneity of height-associated loci selected from SNPs of different significance levels based on the summary data of the GIANT (stage 1) studies. In a sample of 1,304 individuals collected from an island population of the Adriatic coast of Croatia, we assessed the extent of height variance explained by incorporating the effects of less significant height loci and multiple effective SNPs at the same loci. Our results indicate that approximately half of the 118 loci that achieved stringent genome-wide significance (p-value<5×10−8) showed evidence of allelic heterogeneity. Additionally...

The Missing Heritability in T1D and Potential New Targets for Prevention

Pierce, Brian G.; Eberwine, Ryan; Noble, Janelle A.; Habib, Michael; Shulha, Hennady P.; Weng, Zhiping; Blankenhorn, Elizabeth P.; Mordes, John P.
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
46.53%
Type 1 diabetes (T1D) is a T cell-mediated disease. It is strongly associated with susceptibility haplotypes within the major histocompatibility complex, but this association accounts for an estimated 50% of susceptibility. Other studies have identified as many as 50 additional susceptibility loci, but the effect of most is very modest (odds ratio (OR) <1.5). What accounts for the “missing heritability” is unknown and is often attributed to environmental factors. Here we review new data on the cognate ligand of MHC molecules, the T cell receptor (TCR). In rats, we found that one allele of a TCR variable gene, Vβ13A, is strongly associated with T1D (OR >5) and that deletion of Vβ13+ T cells prevents diabetes. A role for the TCR is also suspected in NOD mice, but TCR regions have not been associated with human T1D. To investigate this disparity, we tested the hypothesis in silico that previous studies of human T1D genetics were underpowered to detect MHC-contingent TCR susceptibility. We show that stratifying by MHC markedly increases statistical power to detect potential TCR susceptibility alleles. We suggest that the TCR regions are viable candidates for T1D susceptibility genes, could account for “missing heritability,” and could be targets for prevention.

Finding the sources of missing heritability in a yeast cross

Bloom, Joshua S.; Ehrenreich, Ian M.; Loo, Wesley; Võ Lite, Thúy-Lan; Kruglyak, Leonid
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
46.59%
For many traits, including susceptibility to common diseases in humans, causal loci uncovered by genetic mapping studies explain only a minority of the heritable contribution to trait variation. Multiple explanations for this “missing heritability” have been proposed1. Here we use a large cross between two yeast strains to accurately estimate different sources of heritable variation for 46 quantitative traits and to detect underlying loci with high statistical power. We find that the detected loci explain nearly the entire additive contribution to heritable variation for the traits studied. We also show that the contribution to heritability of gene-gene interactions varies among traits, from near zero to approximately 50%. Detected two-locus interactions explain only a minority of this contribution. These results substantially advance our understanding of the missing heritability problem and have important implications for future studies of complex and quantitative traits.

Measuring missing heritability: Inferring the contribution of common variants

Golan, David; Lander, Eric S.; Rosset, Saharon
Fonte: National Academy of Sciences Publicador: National Academy of Sciences
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
46.59%
Studies have identified thousands of common genetic variants associated with hundreds of diseases. Yet, these common variants typically account for a minority of the heritability, a problem known as “missing heritability.” Geneticists recently proposed indirect methods for estimating the total heritability attributable to common variants, including those whose effects are too small to allow identification in current studies. Here, we show that these methods seriously underestimate the true heritability when applied to case–control studies of disease. We describe a method that provides unbiased estimates. Applying it to six diseases, we estimate that common variants explain an average of 60% of the heritability for these diseases. The framework also may be applied to case–control studies, extreme-phenotype studies, and other settings.

The missing heritability of behavior: The search continues

GOLDMAN, DAVID
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /12/2014 EN
Relevância na Pesquisa
46.51%
Genetic variation altering behavior is elusive. This commentary discusses implications for the search for “missing heritability” posed by a unified series of studies from the Minnesota Center for Twin and Family Research. Endophenotypes are measured in a longitudinal cohort including twins, analyzed for heritability and genetically mapped via genome-wide association and genome sequencing. The genes identified account for a fraction of the heritability, but the manner in which the studies were conducted points to explanations other than methodology. The MCTFR data are an unprecedented addition to the research information commons. Other gene discoveries will follow when they are analyzed in new ways and in combination with other studies. Even larger samples may be needed. Alternatively or in addition, locus identification, especially rare alleles, may require the study of families and population isolates with founder characteristics.

Epigenetic inheritance and the missing heritability

Trerotola, Marco; Relli, Valeria; Simeone, Pasquale; Alberti, Saverio
Fonte: BioMed Central Publicador: BioMed Central
Tipo: Artigo de Revista Científica
Publicado em 28/07/2015 EN
Relevância na Pesquisa
46.53%
Genome-wide association studies of complex physiological traits and diseases consistently found that associated genetic factors, such as allelic polymorphisms or DNA mutations, only explained a minority of the expected heritable fraction. This discrepancy is known as “missing heritability”, and its underlying factors and molecular mechanisms are not established. Epigenetic programs may account for a significant fraction of the “missing heritability.” Epigenetic modifications, such as DNA methylation and chromatin assembly states, reflect the high plasticity of the genome and contribute to stably alter gene expression without modifying genomic DNA sequences. Consistent components of complex traits, such as those linked to human stature/height, fertility, and food metabolism or to hereditary defects, have been shown to respond to environmental or nutritional condition and to be epigenetically inherited. The knowledge acquired from epigenetic genome reprogramming during development, stem cell differentiation/de-differentiation, and model organisms is today shedding light on the mechanisms of (a) mitotic inheritance of epigenetic traits from cell to cell, (b) meiotic epigenetic inheritance from generation to generation, and (c) true transgenerational inheritance. Such mechanisms have been shown to include incomplete erasure of DNA methylation...

Hope for GWAS: Relevant Risk Genes Uncovered from GWAS Statistical Noise

Correia, Catarina; Diekmann, Yoan; Vicente, Astrid; Pereira-Leal, José
Fonte: MDPI Publicador: MDPI
Tipo: Artigo de Revista Científica
Publicado em 29/09/2014 ENG
Relevância na Pesquisa
46.65%
Hundreds of genetic variants have been associated to common diseases through genome-wide association studies (GWAS), yet there are limits to current approaches in detecting true small effect risk variants against a background of false positive findings. Here we addressed the missing heritability problem, aiming to test whether there are indeed risk variants within GWAS statistical noise and to develop a systematic strategy to retrieve these hidden variants. Employing an integrative approach, which combines protein-protein interactions with association data from GWAS for 6 common diseases, we found that associated-genes at less stringent significance levels (p < 0.1) with any of these diseases are functionally connected beyond noise expectation. This functional coherence was used to identify disease-relevant subnetworks, which were shown to be enriched in known genes, outperforming the selection of top GWAS genes. As a proof of principle, we applied this approach to breast cancer, supporting well-known breast cancer genes, while pinpointing novel susceptibility genes for experimental validation. This study reinforces the idea that GWAS are under-analyzed and that missing heritability is rather hidden. It extends the use of protein networks to reveal this missing heritability...

Hope for GWAS: Relevant Risk Genes Uncovered from GWAS Statistical Noise

Correia, C.; Diekmann, Y.; Vicente, A.M.; Pereira-Leal, J.B.
Fonte: Molecular Diversity Preservation International (MDPI) Publicador: Molecular Diversity Preservation International (MDPI)
Tipo: Artigo de Revista Científica
Publicado em /09/2014 ENG
Relevância na Pesquisa
46.65%
Hundreds of genetic variants have been associated to common diseases through genome-wide association studies (GWAS), yet there are limits to current approaches in detecting true small effect risk variants against a background of false positive findings. Here we addressed the missing heritability problem, aiming to test whether there are indeed risk variants within GWAS statistical noise and to develop a systematic strategy to retrieve these hidden variants. Employing an integrative approach, which combines protein-protein interactions with association data from GWAS for 6 common diseases, we found that associated-genes at less stringent significance levels (p < 0.1) with any of these diseases are functionally connected beyond noise expectation. This functional coherence was used to identify disease-relevant subnetworks, which were shown to be enriched in known genes, outperforming the selection of top GWAS genes. As a proof of principle, we applied this approach to breast cancer, supporting well-known breast cancer genes, while pinpointing novel susceptibility genes for experimental validation. This study reinforces the idea that GWAS are under-analyzed and that missing heritability is rather hidden. It extends the use of protein networks to reveal this missing heritability...

Quantifying Missing Heritability at Known GWAS Loci

Gusev, Alexander; Bhatia, Gaurav; Zaitlen, Noah; Vilhjalmsson, Bjarni J.; Diogo, Dorothée; Stahl, Eli A.; Gregersen, Peter K.; Worthington, Jane; Klareskog, Lars; Raychaudhuri, Soumya; Plenge, Robert M.; Pasaniuc, Bogdan; Price, Alkes L.
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
EN_US
Relevância na Pesquisa
46.76%
Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain more heritability than GWAS-associated SNPs on average (). For some diseases, this increase was individually significant: for Multiple Sclerosis (MS) () and for Crohn's Disease (CD) (); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained more MS heritability than known MS SNPs () and more CD heritability than known CD SNPs (), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with more heritability from all SNPs at GWAS loci () and more heritability from all autoimmune disease loci () compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs...

The mystery of missing heritability: Genetic interactions create phantom heritability

Zuk, Or; Hechter, Eliana; Sunyaev, Shamil R.; Lander, Eric S.
Fonte: National Academy of Sciences Publicador: National Academy of Sciences
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
46.92%
Human genetics has been haunted by the mystery of “missing heritability” of common traits. Although studies have discovered >1,200 variants associated with common diseases and traits, these variants typically appear to explain only a minority of the heritability. The proportion of heritability explained by a set of variants is the ratio of (i) the heritability due to these variants (numerator), estimated directly from their observed effects, to (ii) the total heritability (denominator), inferred indirectly from population data. The prevailing view has been that the explanation for missing heritability lies in the numerator—that is, in as-yet undiscovered variants. While many variants surely remain to be found, we show here that a substantial portion of missing heritability could arise from overestimation of the denominator, creating “phantom heritability.” Specifically, (i) estimates of total heritability implicitly assume the trait involves no genetic interactions (epistasis) among loci; (ii) this assumption is not justified, because models with interactions are also consistent with observable data; and (iii) under such models, the total heritability may be much smaller and thus the proportion of heritability explained much larger. For example...

Quantifying Missing Heritability at Known GWAS Loci

Gusev, Alexander; Bhatia, Gaurav; Zaitlen, Noah; Vilhjalmsson, Bjarni J.; Diogo, Dorothée; Stahl, Eli A.; Gregersen, Peter K.; Worthington, Jane; Klareskog, Lars; Raychaudhuri, Soumya; Plenge, Robert M.; Pasaniuc, Bogdan; Price, Alkes L.
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
EN
Relevância na Pesquisa
46.76%
Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain more heritability than GWAS-associated SNPs on average (). For some diseases, this increase was individually significant: for Multiple Sclerosis (MS) () and for Crohn's Disease (CD) (); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained more MS heritability than known MS SNPs () and more CD heritability than known CD SNPs (), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with more heritability from all SNPs at GWAS loci () and more heritability from all autoimmune disease loci () compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs...

easyGWAS: An Integrated Computational Framework for Advanced Genome-Wide Association Studies

Grimm, Dominik Gerhard
Fonte: Universität Tübingen Publicador: Universität Tübingen
Tipo: Dissertation; info:eu-repo/semantics/doctoralThesis
EN
Relevância na Pesquisa
46.9%
Recent advances in sequencing technologies have made it possible for the first time to sequence and analyse the genomes of whole populations of individuals in both a cost-effective manner and in a reasonable amount of time. One of the primary applications of this data is to better understand and investigate the genetic basis of common traits or diseases. For this purpose, genome-wide association studies (GWASs) are often used to find loci that are associated with a phenotype of interest. However, conducting GWASs is a challenging endeavour: first, different types of hidden confounding factors, such as population structure, environmental or technical influences, could lead to spurious associations. Second, it has been shown in several studies that associated loci often fail to explain much of the phenotypic variability — a phenomenon referred to as the problem of missing heritability. Many tools have been developed to partly address these challenges. The large diversity of these tools, however, have led to a highly fragmented and confusing landscape of tools. In addition, most of these tools do not share a common data format and do not provide straightforward solutions to visualise and annotate their results. In this thesis...

Rare Variants in Transcript and Potential Regulatory Regions Explain a Small Percentage of the Missing Heritability of Complex Traits in Cattle

Gonzalez-Recio, Oscar; Daetwyler, Hans D.; MacLeod, Iona M.; Pryce, Jennie E.; Bowman, Phil J.; Hayes, Ben J.; Goddard, Michael E.
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 07/12/2015 EN
Relevância na Pesquisa
46.53%
The proportion of genetic variation in complex traits explained by rare variants is a key question for genomic prediction, and for identifying the basis of “missing heritability”–the proportion of additive genetic variation not captured by common variants on SNP arrays. Sequence variants in transcript and regulatory regions from 429 sequenced animals were used to impute high density SNP genotypes of 3311 Holstein sires to sequence. There were 675,062 common variants (MAF>0.05), 102,549 uncommon variants (0.01

Hope for GWAS: Relevant Risk Genes Uncovered from GWAS Statistical Noise

Correia, Catarina; Diekmann, Yoan; Vicente, Astrid; Pereira-Leal, José
Fonte: MDPI AG Publicador: MDPI AG
Tipo: Artigo de Revista Científica
Publicado em 29/09/2014 ENG
Relevância na Pesquisa
46.65%
Hundreds of genetic variants have been associated to common diseases through genome-wide association studies (GWAS), yet there are limits to current approaches in detecting true small effect risk variants against a background of false positive findings. Here we addressed the missing heritability problem, aiming to test whether there are indeed risk variants within GWAS statistical noise and to develop a systematic strategy to retrieve these hidden variants. Employing an integrative approach, which combines protein-protein interactions with association data from GWAS for 6 common diseases, we found that associated-genes at less stringent significance levels (p < 0.1) with any of these diseases are functionally connected beyond noise expectation. This functional coherence was used to identify disease-relevant subnetworks, which were shown to be enriched in known genes, outperforming the selection of top GWAS genes. As a proof of principle, we applied this approach to breast cancer, supporting well-known breast cancer genes, while pinpointing novel susceptibility genes for experimental validation. This study reinforces the idea that GWAS are under-analyzed and that missing heritability is rather hidden. It extends the use of protein networks to reveal this missing heritability...

Finding the sources of missing heritability in a yeast cross

Bloom, Joshua S.; Ehrenreich, Ian M.; Loo, Wesley; Lite, Thúy-Lan Võ; Kruglyak, Leonid
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 14/08/2012
Relevância na Pesquisa
46.59%
For many traits, including susceptibility to common diseases in humans, causal loci uncovered by genetic mapping studies explain only a minority of the heritable contribution to trait variation. Multiple explanations for this "missing heritability" have been proposed. Here we use a large cross between two yeast strains to accurately estimate different sources of heritable variation for 46 quantitative traits and to detect underlying loci with high statistical power. We find that the detected loci explain nearly the entire additive contribution to heritable variation for the traits studied. We also show that the contribution to heritability of gene-gene interactions varies among traits, from near zero to 50%. Detected two-locus interactions explain only a minority of this contribution. These results substantially advance our understanding of the missing heritability problem and have important implications for future studies of complex and quantitative traits.

Revealing the missing heritability via cross-validated genome-wide association studies

Shen, Xia
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
46.53%
Presented here is a simple method for cross-validated genome-wide association studies (cvGWAS). Focusing on phenotype prediction, the method is able to reveal a significant amount of missing heritability by properly selecting a small number of loci with implicit predictive ability. The results provide new insights into the missing heritability problem and the underlying genetic architecture of complex traits.; Comment: 7 pages main text, 2 figures, 2 supplementary tables, 49 supplementary figures