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genomic selection, missing heritability

A J Brea-Fernandez, C Fernandez-Rozadilla, M Alvarez-Barona, D Azuara, M M Ginesta, J Clofent, L de Castro, D Gonzalez, M Andreu, X Bessa, X Llor, R Xicola, R Jover, A Castells, S Castellvi-Bel, G Capella, A Carracedo, C Ruiz-Ponte
PURPOSE: A great proportion of the heritability of colorectal cancer (CRC) still remains unexplained, and rare variants, as well as copy number changes, have been proposed as potential candidates to explain the so-called 'missing heritability'. We aimed to identify rare high-to-moderately penetrant copy number variants (CNVs) in patients suspected of having hereditary CRC due to an early onset. METHODS/PATIENTS: We have selected for genome-wide copy number analysis, 27 MMR-proficient early onset CRC patients (<50 years) without identifiable germline mutations in Mendelian genes related to this phenotype...
November 25, 2016: Clinical & Translational Oncology
Marco Cavalli, Gang Pan, Helena Nord, Ola Wallerman, Emelie Wallén Arzt, Olof Berggren, Ingegerd Elvers, Maija-Leena Eloranta, Lars Rönnblom, Kerstin Lindblad Toh, Claes Wadelius
Genome-wide association studies (GWAS) have identified a large number of disease-associated SNPs, but in few cases the functional variant and the gene it controls have been identified. To systematically identify candidate regulatory variants, we sequenced ENCODE cell lines and used public ChIP-seq data to look for transcription factors binding preferentially to one allele. We found 9962 candidate regulatory SNPs, of which 16 % were rare and showed evidence of larger functional effect than common ones. Functionally rare variants may explain divergent GWAS results between populations and are candidates for a partial explanation of the missing heritability...
May 2016: Human Genetics
Rainer Malik, Matthew Traylor, Sara L Pulit, Steve Bevan, Jemma C Hopewell, Elizabeth G Holliday, Wei Zhao, Patricia Abrantes, Philippe Amouyel, John R Attia, Thomas W K Battey, Klaus Berger, Giorgio B Boncoraglio, Ganesh Chauhan, Yu-Ching Cheng, Wei-Min Chen, Robert Clarke, Ioana Cotlarciuc, Stephanie Debette, Guido J Falcone, Jose M Ferro, Dale M Gamble, Andreea Ilinca, Steven J Kittner, Christina E Kourkoulis, Robin Lemmens, Christopher R Levi, Peter Lichtner, Arne Lindgren, Jingmin Liu, James F Meschia, Braxton D Mitchell, Sofia A Oliveira, Joana Pera, Alex P Reiner, Peter M Rothwell, Pankaj Sharma, Agnieszka Slowik, Cathie L M Sudlow, Turgut Tatlisumak, Vincent Thijs, Astrid M Vicente, Daniel Woo, Sudha Seshadri, Danish Saleheen, Jonathan Rosand, Hugh S Markus, Bradford B Worrall, Martin Dichgans
OBJECTIVE: To investigate the influence of common and low-frequency genetic variants on the risk of ischemic stroke (all IS) and etiologic stroke subtypes. METHODS: We meta-analyzed 12 individual genome-wide association studies comprising 10,307 cases and 19,326 controls imputed to the 1000 Genomes (1 KG) phase I reference panel. We selected variants showing the highest degree of association (p < 1E-5) in the discovery phase for replication in Caucasian (13,435 cases and 29,269 controls) and South Asian (2,385 cases and 5,193 controls) samples followed by a transethnic meta-analysis...
March 29, 2016: Neurology
A Xavier, William M Muir, Katy M Rainey
BACKGROUND: Success in genome-wide association studies and marker-assisted selection depends on good phenotypic and genotypic data. The more complete this data is, the more powerful will be the results of analysis. Nevertheless, there are next-generation technologies that seek to provide genotypic information in spite of great proportions of missing data. The procedures these technologies use to impute genetic data, therefore, greatly affect downstream analyses. This study aims to (1) compare the genetic variance in a single-nucleotide polymorphism panel of soybean with missing data imputed using various methods, (2) evaluate the imputation accuracy and post-imputation quality associated with these methods, and (3) evaluate the impact of imputation method on heritability and the accuracy of genome-wide prediction of soybean traits...
February 2, 2016: BMC Bioinformatics
P M VanRaden
Genetic models convert data into estimated breeding values and other information useful to breeders. The goal is to provide accurate and timely predictions of the future performance for each animal (or embryo). Modeling involves defining traits, editing raw data, removing environmental effects, including genetic by environmental interactions and correlations among traits, and accounting for nonadditive inheritance or nonnormal distributions. Data include phenotypes and pedigrees during the last century and genotypes within the last decade...
March 2016: Journal of Dairy Science
Young Jin Kim, Juyoung Lee, Bong-Jo Kim, Taesung Park
BACKGROUND: Rare variants have gathered increasing attention as a possible alternative source of missing heritability. Since next generation sequencing technology is not yet cost-effective for large-scale genomic studies, a widely used alternative approach is imputation. However, the imputation approach may be limited by the low accuracy of the imputed rare variants. To improve imputation accuracy of rare variants, various approaches have been suggested, including increasing the sample size of the reference panel, using sequencing data from study-specific samples (i...
December 29, 2015: BMC Genomics
Laercio R Porto-Neto, William Barendse, John M Henshall, Sean M McWilliam, Sigrid A Lehnert, Antonio Reverter
BACKGROUND: The success of genomic selection in animal breeding hinges on the availability of a large reference population on which genomic-based predictions of additive genetic or breeding values are built. Here, we explore the benefit of combining two unrelated populations into a single reference population. METHODS: The datasets consisted of 1829 Brahman and 1973 Tropical Composite cattle with measurements on five phenotypes relevant to tropical adaptation and genotypes for 71,726 genome-wide single nucleotide polymorphisms (SNPs)...
2015: Genetics, Selection, Evolution: GSE
Armando Caballero, Albert Tenesa, Peter D Keightley
We use computer simulations to investigate the amount of genetic variation for complex traits that can be revealed by single-SNP genome-wide association studies (GWAS) or regional heritability mapping (RHM) analyses based on full genome sequence data or SNP chips. We model a large population subject to mutation, recombination, selection, and drift, assuming a pleiotropic model of mutations sampled from a bivariate distribution of effects of mutations on a quantitative trait and fitness. The pleiotropic model investigated, in contrast to previous models, implies that common mutations of large effect are responsible for most of the genetic variation for quantitative traits, except when the trait is fitness itself...
December 2015: Genetics
V V Bashinskaya, O G Kulakova, A N Boyko, A V Favorov, O O Favorova
Multiple sclerosis (MS) is a common complex neurodegenerative disease of the central nervous system. It develops with autoimmune inflammation and demyelination. Genome-wide association studies (GWASs) serve as a powerful tool for investigating the genetic architecture of MS and are generally used to identify the genetic factors of disease susceptibility, clinical phenotypes, and treatment response. This review considers the main achievements and challenges of using GWAS to identify the genes involved in MS...
November 2015: Human Genetics
Jian Yang, Andrew Bakshi, Zhihong Zhu, Gibran Hemani, Anna A E Vinkhuyzen, Sang Hong Lee, Matthew R Robinson, John R B Perry, Ilja M Nolte, Jana V van Vliet-Ostaptchouk, Harold Snieder, Tonu Esko, Lili Milani, Reedik Mägi, Andres Metspalu, Anders Hamsten, Patrik K E Magnusson, Nancy L Pedersen, Erik Ingelsson, Nicole Soranzo, Matthew C Keller, Naomi R Wray, Michael E Goddard, Peter M Visscher
We propose a method (GREML-LDMS) to estimate heritability for human complex traits in unrelated individuals using whole-genome sequencing data. We demonstrate using simulations based on whole-genome sequencing data that ∼97% and ∼68% of variation at common and rare variants, respectively, can be captured by imputation. Using the GREML-LDMS method, we estimate from 44,126 unrelated individuals that all ∼17 million imputed variants explain 56% (standard error (s.e.) = 2.3%) of variance for height and 27% (s...
October 2015: Nature Genetics
Madelyn M Gerber, Heather Hampel, Xiao-Ping Zhou, Nathan P Schulz, Adam Suhy, Mehmet Deveci, Ümit V Çatalyürek, Amanda Ewart Toland
Colorectal cancer (CRC) can be classified into different types. Chromosomal instable (CIN) colon cancers are thought to be the most common type of colon cancer. The risk of developing a CIN-related CRC is due in part to inherited risk factors. Genome-wide association studies have yielded over 40 single nucleotide polymorphisms (SNPs) associated with CRC risk, but these only account for a subset of risk alleles. Some of this missing heritability may be due to gene-gene interactions. We developed a strategy to identify interacting candidate genes/loci for CRC risk that utilizes both linkage and RNA-seq data from mouse models in combination with allele-specific imbalance (ASI) studies in human tumors...
November 15, 2015: International Journal of Cancer. Journal International du Cancer
Omnia Gamal El-Dien, Blaise Ratcliffe, Jaroslav Klápště, Charles Chen, Ilga Porth, Yousry A El-Kassaby
BACKGROUND: Genomic selection (GS) in forestry can substantially reduce the length of breeding cycle and increase gain per unit time through early selection and greater selection intensity, particularly for traits of low heritability and late expression. Affordable next-generation sequencing technologies made it possible to genotype large numbers of trees at a reasonable cost. RESULTS: Genotyping-by-sequencing was used to genotype 1,126 Interior spruce trees representing 25 open-pollinated families planted over three sites in British Columbia, Canada...
2015: BMC Genomics
Laura Grange, Jean-François Bureau, Iryna Nikolayeva, Richard Paul, Kristel Van Steen, Benno Schwikowski, Anavaj Sakuntabhai
BACKGROUND: Deciphering the genetic architecture of complex traits is still a major challenge for human genetics. In most cases, genome-wide association studies have only partially explained the heritability of traits and diseases. Epistasis, one potentially important cause of this missing heritability, is difficult to explore at the genome-wide level. Here, we develop and assess a tool based on interactive odds ratios (IOR), Fast Odds Ratio-based sCan for Epistasis (FORCE), as a novel approach for exhaustive genome-wide epistasis search...
2015: BMC Genetics
Ling Wang, Haipeng Shen, Hexuan Liu, Guang Guo
BACKGROUND: Recently mixed linear models are used to address the issue of "missing" heritability in traditional Genome-wide association studies (GWAS). The models assume that all single-nucleotide polymorphisms (SNPs) are associated with the phenotypes of interest. However, it is more common that only a small proportion of SNPs have significant effects on the phenotypes, while most SNPs have no or very small effects. To incorporate this feature, we propose an efficient Hierarchical Bayesian Model (HBM) that extends the existing mixed models to enforce automatic selection of significant SNPs...
February 3, 2015: BMC Genomics
Swetlana Berger, Paulino Pérez-Rodríguez, Yogasudha Veturi, Henner Simianer, Gustavo de los Campos
Genome-wide association studies (GWAS) have detected large numbers of variants associated with complex human traits and diseases. However, the proportion of variance explained by GWAS-significant single nucleotide polymorphisms has been usually small. This brought interest in the use of whole-genome regression (WGR) methods. However, there has been limited research on the factors that affect prediction accuracy (PA) of WGRs when applied to human data of distantly related individuals. Here, we examine, using real human genotypes and simulated phenotypes, how trait complexity, marker-quantitative trait loci (QTL) linkage disequilibrium (LD), and the model used affect the performance of WGRs...
March 2015: Annals of Human Genetics
Xiaoshuai Zhang, Fuzhong Xue, Hong Liu, Dianwen Zhu, Bin Peng, Joseph L Wiemels, Xiaowei Yang
BACKGROUND: Genome-wide Association Studies (GWAS) are typically designed to identify phenotype-associated single nucleotide polymorphisms (SNPs) individually using univariate analysis methods. Though providing valuable insights into genetic risks of common diseases, the genetic variants identified by GWAS generally account for only a small proportion of the total heritability for complex diseases. To solve this "missing heritability" problem, we implemented a strategy called integrative Bayesian Variable Selection (iBVS), which is based on a hierarchical model that incorporates an informative prior by considering the gene interrelationship as a network...
2014: BMC Genetics
Rohan H C Palmer, Leslie Brick, Nicole R Nugent, L Cinnamon Bidwell, John E McGeary, Valerie S Knopik, Matthew C Keller
BACKGROUND AND AIMS: Twin and family studies suggest that genetic influences are shared across substances of abuse. However, despite evidence of heritability, genome-wide association and candidate gene studies have indicated numerous markers of limited effects, suggesting that much of the heritability remains missing. We estimated (1) the aggregate effect of common single nucleotide polymorphisms (SNPs) on multiple indicators of comorbid drug problems that are typically employed across community and population-based samples, and (2) the genetic covariance across these measures...
March 2015: Addiction
Catarina Correia, Yoan Diekmann, Astrid M Vicente, José B Pereira-Leal
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...
2014: International Journal of Molecular Sciences
So-Young Bang, Young-Ji Na, Kwangwoo Kim, Young Bin Joo, Youngho Park, Jaemoon Lee, Sun-Young Lee, Adnan A Ansari, Junghee Jung, Hwanseok Rhee, Jong-Young Lee, Bok-Ghee Han, Sung-Min Ahn, Sungho Won, Hye-Soon Lee, Sang-Cheol Bae
INTRODUCTION: Although it has been suggested that rare coding variants could explain the substantial missing heritability, very few sequencing studies have been performed in rheumatoid arthritis (RA). We aimed to identify novel functional variants with rare to low frequency using targeted exon sequencing of RA in Korea. METHODS: We analyzed targeted exon sequencing data of 398 genes selected from a multifaceted approach in Korean RA patients (n = 1,217) and controls (n = 717)...
2014: Arthritis Research & Therapy
Wolfgang Sadee, Katherine Hartmann, Michał Seweryn, Maciej Pietrzak, Samuel K Handelman, Grzegorz A Rempala
Genetic factors strongly influence risk of common human diseases and treatment outcomes but the causative variants remain largely unknown; this gap has been called the 'missing heritability'. We propose several hypotheses that in combination have the potential to narrow the gap. First, given a multi-stage path from wellness to disease, we propose that common variants under positive evolutionary selection represent normal variation and gate the transition between wellness and an 'off-well' state, revealing adaptations to changing environmental conditions...
October 2014: Human Genetics
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