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Journal articles on the topic 'Complex Traits Genetics'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Gjessing, Håkon K., and Rolv Terje Lie. "Biometrical modelling in genetics: are complex traits too complex?" Statistical Methods in Medical Research 17, no. 1 (February 2008): 75–96. http://dx.doi.org/10.1177/0962280207081241.

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The field of traditional biometrical genetics uses mixed-effects models to quantify the influence of genetic and environmental factors on a biological trait, based essentially on estimating within-family trait correlations. Such analyses provide a useful preview of what may be discovered with the emerging full-scale genotyping strategies. However, biometrical analyses require unrealistically large sample sizes to obtain a reasonable precision, particularly for dichotomous traits. In addition, it may be very difficult to separate genetic and environmental effects because environmental correlations are poorly understood. We illustrate these and other difficulties using population-based cousins and nuclear family data for birth weight, collected from the Medical Birth Registry of Norway.

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2

Kemper, Kathryn. "59 Insights into Complex Traits from Human Genetics." Journal of Animal Science 99, Supplement_3 (October 8, 2021): 30–31. http://dx.doi.org/10.1093/jas/skab235.052.

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Abstract Genomic selection has been implemented successfully in many livestock industries for genetic improvement. However, genomic selection provides limited insight into the genetic mechanisms underlying variation in complex traits. In contrast, human genetics has a focus on understanding genetic architecture and the origins of quantitative trait variation. This presentation will discuss a number of examples from human genetics which can inform our understanding of the nature of variation in complex traits. So-called ‘monogenic’ conditions, for example, are proving to have more complex genetic architecture than naïve expectations might suggest. Massive data sets of millions of people are also enabling longstanding questions to be addressed. Traits such as height, for example, are affected by a very large but finite number of loci. We can reconcile seemingly disparate heritability estimates from different experimental designs by accounting for assortative mating. The presentation will provide a brief update on current approaches to genomic prediction in human genetics and discuss the implications of these findings for understanding and predicting complex traits in livestock.

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3

Skelly, Daniel A., Narayanan Raghupathy, Raymond F. Robledo, Joel H. Graber, and Elissa J. Chesler. "Reference Trait Analysis Reveals Correlations Between Gene Expression and Quantitative Traits in Disjoint Samples." Genetics 212, no. 3 (May 21, 2019): 919–29. http://dx.doi.org/10.1534/genetics.118.301865.

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Systems genetic analysis of complex traits involves the integrated analysis of genetic, genomic, and disease-related measures. However, these data are often collected separately across multiple study populations, rendering direct correlation of molecular features to complex traits impossible. Recent transcriptome-wide association studies (TWAS) have harnessed gene expression quantitative trait loci (eQTL) to associate unmeasured gene expression with a complex trait in genotyped individuals, but this approach relies primarily on strong eQTL. We propose a simple and powerful alternative strategy for correlating independently obtained sets of complex traits and molecular features. In contrast to TWAS, our approach gains precision by correlating complex traits through a common set of continuous phenotypes instead of genetic predictors, and can identify transcript–trait correlations for which the regulation is not genetic. In our approach, a set of multiple quantitative “reference” traits is measured across all individuals, while measures of the complex trait of interest and transcriptional profiles are obtained in disjoint subsamples. A conventional multivariate statistical method, canonical correlation analysis, is used to relate the reference traits and traits of interest to identify gene expression correlates. We evaluate power and sample size requirements of this methodology, as well as performance relative to other methods, via extensive simulation and analysis of a behavioral genetics experiment in 258 Diversity Outbred mice involving two independent sets of anxiety-related behaviors and hippocampal gene expression. After splitting the data set and hiding one set of anxiety-related traits in half the samples, we identified transcripts correlated with the hidden traits using the other set of anxiety-related traits and exploiting the highest canonical correlation (R = 0.69) between the trait data sets. We demonstrate that this approach outperforms TWAS in identifying associated transcripts. Together, these results demonstrate the validity, reliability, and power of reference trait analysis for identifying relations between complex traits and their molecular substrates.

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4

Yi, Nengjun, and Shizhong Xu. "Bayesian Mapping of Quantitative Trait Loci for Complex Binary Traits." Genetics 155, no. 3 (July 1, 2000): 1391–403. http://dx.doi.org/10.1093/genetics/155.3.1391.

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AbstractA complex binary trait is a character that has a dichotomous expression but with a polygenic genetic background. Mapping quantitative trait loci (QTL) for such traits is difficult because of the discrete nature and the reduced variation in the phenotypic distribution. Bayesian statistics are proved to be a powerful tool for solving complicated genetic problems, such as multiple QTL with nonadditive effects, and have been successfully applied to QTL mapping for continuous traits. In this study, we show that Bayesian statistics are particularly useful for mapping QTL for complex binary traits. We model the binary trait under the classical threshold model of quantitative genetics. The Bayesian mapping statistics are developed on the basis of the idea of data augmentation. This treatment allows an easy way to generate the value of a hypothetical underlying variable (called the liability) and a threshold, which in turn allow the use of existing Bayesian statistics. The reversible jump Markov chain Monte Carlo algorithm is used to simulate the posterior samples of all unknowns, including the number of QTL, the locations and effects of identified QTL, genotypes of each individual at both the QTL and markers, and eventually the liability of each individual. The Bayesian mapping ends with an estimation of the joint posterior distribution of the number of QTL and the locations and effects of the identified QTL. Utilities of the method are demonstrated using a simulated outbred full-sib family. A computer program written in FORTRAN language is freely available on request.

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Goddard, M. E., K. E. Kemper, I. M. MacLeod, A. J. Chamberlain, and B. J. Hayes. "Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture." Proceedings of the Royal Society B: Biological Sciences 283, no. 1835 (July 27, 2016): 20160569. http://dx.doi.org/10.1098/rspb.2016.0569.

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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.

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6

Cooper, Mark, Dean W. Podlich, and Oscar S. Smith. "Gene-to-phenotype models and complex trait genetics." Australian Journal of Agricultural Research 56, no. 9 (2005): 895. http://dx.doi.org/10.1071/ar05154.

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The premise that is explored in this paper is that in some cases, in order to make progress in the design of molecular breeding strategies for complex traits, we will need a theoretical framework for quantitative genetics that is grounded in the concept of gene-networks. We seek to develop a gene-to-phenotype (G→P) modelling framework for quantitative genetics that explicitly deals with the context-dependent gene effects that are attributed to genes functioning within networks, i.e. epistasis, gene × environment interactions, and pleiotropy. The E(NK) model is discussed as a starting point for building such a theoretical framework for complex trait genetics. Applying this framework to a combination of theoretical and empirical G→P models, we find that although many of the context-dependent effects of genetic variation on phenotypic variation can reduce the rate of genetic progress from breeding, it is possible to design molecular breeding strategies for complex traits that on average will outperform phenotypic selection. However, to realise these potential advantages, empirical G→P models of the traits will need to take into consideration the context-dependent effects that are a consequence of epistasis, gene × environment interactions, and pleiotropy. Some promising G→P modelling directions are discussed.

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7

Kučera, L. "D.C. Rao & M.A. Province – Advances in Genetics,Vol. 42, Genetic Dissection of Complex Traits." Czech Journal of Genetics and Plant Breeding 38, No. 1 (July 30, 2012): 64. http://dx.doi.org/10.17221/6112-cjgpb.

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8

Hackinger, Sophie, and Eleftheria Zeggini. "Statistical methods to detect pleiotropy in human complex traits." Open Biology 7, no. 11 (November 2017): 170125. http://dx.doi.org/10.1098/rsob.170125.

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In recent years pleiotropy, the phenomenon of one genetic locus influencing several traits, has become a widely researched field in human genetics. With the increasing availability of genome-wide association study summary statistics, as well as the establishment of deeply phenotyped sample collections, it is now possible to systematically assess the genetic overlap between multiple traits and diseases. In addition to increasing power to detect associated variants, multi-trait methods can also aid our understanding of how different disorders are aetiologically linked by highlighting relevant biological pathways. A plethora of available tools to perform such analyses exists, each with their own advantages and limitations. In this review, we outline some of the currently available methods to conduct multi-trait analyses. First, we briefly introduce the concept of pleiotropy and outline the current landscape of pleiotropy research in human genetics; second, we describe analytical considerations and analysis methods; finally, we discuss future directions for the field.

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9

Belmont, John W., and Suzanne M. Leal. "Complex phenotypes and complex genetics: An introduction to genetic studies of complex traits." Current Atherosclerosis Reports 7, no. 3 (May 2005): 180–87. http://dx.doi.org/10.1007/s11883-005-0004-6.

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10

Parker, Katherine, A. Mesut Erzurumluoglu, and Santiago Rodriguez. "The Y Chromosome: A Complex Locus for Genetic Analyses of Complex Human Traits." Genes 11, no. 11 (October 29, 2020): 1273. http://dx.doi.org/10.3390/genes11111273.

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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.

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11

Swami, Meera. "Networking complex traits." Nature Reviews Genetics 10, no. 4 (April 2009): 219. http://dx.doi.org/10.1038/nrg2566.

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12

Gelernter, Joel. "Genetics of Complex Traits in Psychiatry." Biological Psychiatry 77, no. 1 (January 2015): 36–42. http://dx.doi.org/10.1016/j.biopsych.2014.08.005.

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13

Korol, Abraham B., Yefim I. Ronin, Alexander M. Itskovich, Junhua Peng, and Eviatar Nevo. "Enhanced Efficiency of Quantitative Trait Loci Mapping Analysis Based on Multivariate Complexes of Quantitative Traits." Genetics 157, no. 4 (April 1, 2001): 1789–803. http://dx.doi.org/10.1093/genetics/157.4.1789.

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AbstractAn approach to increase the efficiency of mapping quantitative trait loci (QTL) was proposed earlier by the authors on the basis of bivariate analysis of correlated traits. The power of QTL detection using the log-likelihood ratio (LOD scores) grows proportionally to the broad sense heritability. We found that this relationship holds also for correlated traits, so that an increased bivariate heritability implicates a higher LOD score, higher detection power, and better mapping resolution. However, the increased number of parameters to be estimated complicates the application of this approach when a large number of traits are considered simultaneously. Here we present a multivariate generalization of our previous two-trait QTL analysis. The proposed multivariate analogue of QTL contribution to the broad-sense heritability based on interval-specific calculation of eigenvalues and eigenvectors of the residual covariance matrix allows prediction of the expected QTL detection power and mapping resolution for any subset of the initial multivariate trait complex. Permutation technique allows chromosome-wise testing of significance for the whole trait complex and the significance of the contribution of individual traits owing to: (a) their correlation with other traits, (b) dependence on the chromosome in question, and (c) both a and b. An example of application of the proposed method on a real data set of 11 traits from an experiment performed on an F2/F3 mapping population of tetraploid wheat (Triticum durum × T. dicoccoides) is provided.

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14

Witte, John S., Robert C. Elston, and Nicholas J. Schork. "Genetic dissection of complex traits." Nature Genetics 12, no. 4 (April 1996): 355–56. http://dx.doi.org/10.1038/ng0496-355.

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15

Curtis, David. "Genetic dissection of complex traits." Nature Genetics 12, no. 4 (April 1996): 356–57. http://dx.doi.org/10.1038/ng0496-356.

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Leonid Kruglyak, Eric Lander. "Genetic dissection of complex traits." Nature Genetics 12, no. 4 (April 1996): 357–58. http://dx.doi.org/10.1038/ng0496-357.

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17

Li, Zhikang, Shannon R. M. Pinson, William D. Park, Andrew H. Paterson, and James W. Stansel. "Epistasis for Three Grain Yield Components in Rice (Oryxa sativa L.)." Genetics 145, no. 2 (February 1, 1997): 453–65. http://dx.doi.org/10.1093/genetics/145.2.453.

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The genetic basis for three grain yield components of rice, 1000 kernel weight (KW), grain number per panicle (GN), and grain weight per panicle (GWP), was investigated using restriction fragment length polymorphism markers and F4 progeny testing from a cross between rice subspecies japonica (cultivar Lemont from USA) and indica (cv. Teqing from China). Following identification of 19 QTL affecting these traits, we investigated the role of epistasis in genetic control of these phenotypes. Among 63 markers distributed throughout the genome that appeared to be involved in 79 highly significant (P < 0.001) interactions, most (46 or 73%) did not appear to have “main” effects on the relevant traits, but influenced the trait(s) predominantly through interactions. These results indicate that epistasis is an important genetic basis for complex traits such as yield components, especially traits of low heritability such as GN and GWP. The identification of epistatic loci is an important step toward resolution of discrepancies between quantitative trait loci mapping and classical genetic dogma, contributes to better understanding of the persistence of quantitative genetic variation in populations, and impels reconsideration of optimal mapping methodology and marker-assisted breeding strategies for improvement of complex traits.

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18

Gianola, Daniel, and Rohan L. Fernando. "A Multiple-Trait Bayesian Lasso for Genome-Enabled Analysis and Prediction of Complex Traits." Genetics 214, no. 2 (December 26, 2019): 305–31. http://dx.doi.org/10.1534/genetics.119.302934.

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A multiple-trait Bayesian LASSO (MBL) for genome-based analysis and prediction of quantitative traits is presented and applied to two real data sets. The data-generating model is a multivariate linear Bayesian regression on possibly a huge number of molecular markers, and with a Gaussian residual distribution posed. Each (one per marker) of the T×1 vectors of regression coefficients (T: number of traits) is assigned the same T−variate Laplace prior distribution, with a null mean vector and unknown scale matrix Σ. The multivariate prior reduces to that of the standard univariate Bayesian LASSO when T=1. The covariance matrix of the residual distribution is assigned a multivariate Jeffreys prior, and Σ is given an inverse-Wishart prior. The unknown quantities in the model are learned using a Markov chain Monte Carlo sampling scheme constructed using a scale-mixture of normal distributions representation. MBL is demonstrated in a bivariate context employing two publicly available data sets using a bivariate genomic best linear unbiased prediction model (GBLUP) for benchmarking results. The first data set is one where wheat grain yields in two different environments are treated as distinct traits. The second data set comes from genotyped Pinus trees, with each individual measured for two traits: rust bin and gall volume. In MBL, the bivariate marker effects are shrunk differentially, i.e., “short” vectors are more strongly shrunk toward the origin than in GBLUP; conversely, “long” vectors are shrunk less. A predictive comparison was carried out as well in wheat, where the comparators of MBL were bivariate GBLUP and bivariate Bayes Cπ—a variable selection procedure. A training-testing layout was used, with 100 random reconstructions of training and testing sets. For the wheat data, all methods produced similar predictions. In Pinus, MBL gave better predictions that either a Bayesian bivariate GBLUP or the single trait Bayesian LASSO. MBL has been implemented in the Julia language package JWAS, and is now available for the scientific community to explore with different traits, species, and environments. It is well known that there is no universally best prediction machine, and MBL represents a new resource in the armamentarium for genome-enabled analysis and prediction of complex traits.

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19

Deng, Hong-Wen. "Population Admixture May Appear to Mask, Change or Reverse Genetic Effects of Genes Underlying Complex Traits." Genetics 159, no. 3 (November 1, 2001): 1319–23. http://dx.doi.org/10.1093/genetics/159.3.1319.

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Abstract Association studies using random population samples are increasingly being applied in the identification and inference of genetic effects of genes underlying complex traits. It is well recognized that population admixture may yield false-positive identification of genetic effects for complex traits. However, it is less well appreciated that population admixture can appear to mask, change, or reverse true genetic effects for genes underlying complex traits. By employing a simple population genetics model, we explore the effects and the conditions of population admixture in masking, changing, or even reversing true genetic effects of genes underlying complex traits.

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20

Yi, Nengjun, and Shizhong Xu. "A Random Model Approach to Mapping Quantitative Trait Loci for Complex Binary Traits in Outbred Populations." Genetics 153, no. 2 (October 1, 1999): 1029–40. http://dx.doi.org/10.1093/genetics/153.2.1029.

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Abstract Mapping quantitative trait loci (QTL) for complex binary traits is more challenging than for normally distributed traits due to the nonlinear relationship between the observed phenotype and unobservable genetic effects, especially when the mapping population contains multiple outbred families. Because the number of alleles of a QTL depends on the number of founders in an outbred population, it is more appropriate to treat the effect of each allele as a random variable so that a single variance rather than individual allelic effects is estimated and tested. Such a method is called the random model approach. In this study, we develop the random model approach of QTL mapping for binary traits in outbred populations. An EM-algorithm with a Fisher-scoring algorithm embedded in each E-step is adopted here to estimate the genetic variances. A simple Monte Carlo integration technique is used here to calculate the likelihood-ratio test statistic. For the first time we show that QTL of complex binary traits in an outbred population can be scanned along a chromosome for their positions, estimated for their explained variances, and tested for their statistical significance. Application of the method is illustrated using a set of simulated data.

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21

Wu, Chong. "Multi-trait Genome-Wide Analyses of the Brain Imaging Phenotypes in UK Biobank." Genetics 215, no. 4 (June 15, 2020): 947–58. http://dx.doi.org/10.1534/genetics.120.303242.

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Many genetic variants identified in genome-wide association studies (GWAS) are associated with multiple, sometimes seemingly unrelated, traits. This motivates multi-trait association analyses, which have successfully identified novel associated loci for many complex diseases. While appealing, most existing methods focus on analyzing a relatively small number of traits, and may yield inflated Type 1 error rates when a large number of traits need to be analyzed jointly. As deep phenotyping data are becoming rapidly available, we develop a novel method, referred to as aMAT (adaptive multi-trait association test), for multi-trait analysis of any number of traits. We applied aMAT to GWAS summary statistics for a set of 58 volumetric imaging derived phenotypes from the UK Biobank. aMAT had a genomic inflation factor of 1.04, indicating the Type 1 error rate was well controlled. More important, aMAT identified 24 distinct risk loci, 13 of which were ignored by standard GWAS. In comparison, the competing methods either had a suspicious genomic inflation factor or identified much fewer risk loci. Finally, four additional sets of traits have been analyzed and provided similar conclusions.

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22

Friedrich, Juliane, Erling Strandberg, Per Arvelius, E. Sánchez-Molano, Ricardo Pong-Wong, John M. Hickey, Marie J. Haskell, and Pamela Wiener. "Genetic dissection of complex behaviour traits in German Shepherd dogs." Heredity 123, no. 6 (October 14, 2019): 746–58. http://dx.doi.org/10.1038/s41437-019-0275-2.

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Abstract A favourable genetic structure and diversity of behavioural features highlights the potential of dogs for studying the genetic architecture of behaviour traits. However, behaviours are complex traits, which have been shown to be influenced by numerous genetic and non-genetic factors, complicating their analysis. In this study, the genetic contribution to behaviour variation in German Shepherd dogs (GSDs) was analysed using genomic approaches. GSDs were phenotyped for behaviour traits using the established Canine Behavioural Assessment and Research Questionnaire (C-BARQ). Genome-wide association study (GWAS) and regional heritability mapping (RHM) approaches were employed to identify associations between behaviour traits and genetic variants, while accounting for relevant non-genetic factors. By combining these complementary methods we endeavoured to increase the power to detect loci with small effects. Several behavioural traits exhibited moderate heritabilities, with the highest identified for Human-directed playfulness, a trait characterised by positive interactions with humans. We identified several genomic regions associated with one or more of the analysed behaviour traits. Some candidate genes located in these regions were previously linked to behavioural disorders in humans, suggesting a new context for their influence on behaviour characteristics. Overall, the results support dogs as a valuable resource to dissect the genetic architecture of behaviour traits and also highlight the value of focusing on a single breed in order to control for background genetic effects and thus avoid limitations of between-breed analyses.

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Beissinger, Tim, Jochen Kruppa, David Cavero, Ngoc-Thuy Ha, Malena Erbe, and Henner Simianer. "A Simple Test Identifies Selection on Complex Traits." Genetics 209, no. 1 (March 15, 2018): 321–33. http://dx.doi.org/10.1534/genetics.118.300857.

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Ingvarsson, Pär K., and Nathaniel R. Street. "Association genetics of complex traits in plants." New Phytologist 189, no. 4 (December 23, 2010): 909–22. http://dx.doi.org/10.1111/j.1469-8137.2010.03593.x.

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Neale, David B., and Outi Savolainen. "Association genetics of complex traits in conifers." Trends in Plant Science 9, no. 7 (July 2004): 325–30. http://dx.doi.org/10.1016/j.tplants.2004.05.006.

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26

Civelek, Mete, and Aldons J. Lusis. "Systems genetics approaches to understand complex traits." Nature Reviews Genetics 15, no. 1 (December 3, 2013): 34–48. http://dx.doi.org/10.1038/nrg3575.

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Andrew, Toby. "Genetic Epidemiology of Complex Traits." Twin Research 3, no. 3 (June 1, 2000): 178. http://dx.doi.org/10.1375/twin.3.3.178.

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28

Xu, Shizhong, and William R. Atchley. "Mapping Quantitative Trait Loci for Complex Binary Diseases Using Line Crosses." Genetics 143, no. 3 (July 1, 1996): 1417–24. http://dx.doi.org/10.1093/genetics/143.3.1417.

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Abstract A composite interval gene mapping procedure for complex binary disease traits is proposed in this paper. The binary trait of interest is assumed to be controlled by an underlying liability that is normally distributed. The liability is treated as a typical quantitative character and thus described by the usual quantitative genetics model. Translation from the liability into a binary (disease) phenotype is through the physiological threshold model. Logistic regression analysis is employed to estimate the effects and locations of putative quantitative trait loci (our terminology for a single quantitative trait locus is QTL while multiple loci are referred to as QTLs). Simulation studies show that properties of this mapping procedure mimic those of the composite interval mapping for normally distributed data. Potential utilization of the QTL mapping procedure for resolving alternative genetic models (e.g., single- or two-trait-locus model) is discussed.

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Darvasi, Ariel. "Closing in on complex traits." Nature Genetics 38, no. 8 (August 2006): 861–62. http://dx.doi.org/10.1038/ng0806-861.

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Zhao, Wei, Rongling Wu, Chang-Xing Ma, and George Casella. "A Fast Algorithm for Functional Mapping of Complex Traits." Genetics 167, no. 4 (August 2004): 2133–37. http://dx.doi.org/10.1534/genetics.103.024844.

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Valdar, William, Leah C. Solberg, Dominique Gauguier, William O. Cookson, J. Nicholas P. Rawlins, Richard Mott, and Jonathan Flint. "Genetic and Environmental Effects on Complex Traits in Mice." Genetics 174, no. 2 (August 3, 2006): 959–84. http://dx.doi.org/10.1534/genetics.106.060004.

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Huang, Hanwen, Chevonne D. Eversley, David W. Threadgill, and Fei Zou. "Bayesian Multiple Quantitative Trait Loci Mapping for Complex Traits Using Markers of the Entire Genome." Genetics 176, no. 4 (May 4, 2007): 2529–40. http://dx.doi.org/10.1534/genetics.106.064980.

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Yi, Nengjun, Shizhong Xu, and David B. Allison. "Bayesian Model Choice and Search Strategies for Mapping Interacting Quantitative Trait Loci." Genetics 165, no. 2 (October 1, 2003): 867–83. http://dx.doi.org/10.1093/genetics/165.2.867.

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AbstractMost complex traits of animals, plants, and humans are influenced by multiple genetic and environmental factors. Interactions among multiple genes play fundamental roles in the genetic control and evolution of complex traits. Statistical modeling of interaction effects in quantitative trait loci (QTL) analysis must accommodate a very large number of potential genetic effects, which presents a major challenge to determining the genetic model with respect to the number of QTL, their positions, and their genetic effects. In this study, we use the methodology of Bayesian model and variable selection to develop strategies for identifying multiple QTL with complex epistatic patterns in experimental designs with two segregating genotypes. Specifically, we develop a reversible jump Markov chain Monte Carlo algorithm to determine the number of QTL and to select main and epistatic effects. With the proposed method, we can jointly infer the genetic model of a complex trait and the associated genetic parameters, including the number, positions, and main and epistatic effects of the identified QTL. Our method can map a large number of QTL with any combination of main and epistatic effects. Utility and flexibility of the method are demonstrated using both simulated data and a real data set. Sensitivity of posterior inference to prior specifications of the number and genetic effects of QTL is investigated.

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Mizrachi, Eshchar, Lieven Verbeke, Nanette Christie, Ana C. Fierro, Shawn D. Mansfield, Mark F. Davis, Erica Gjersing, et al. "Network-based integration of systems genetics data reveals pathways associated with lignocellulosic biomass accumulation and processing." Proceedings of the National Academy of Sciences 114, no. 5 (January 17, 2017): 1195–200. http://dx.doi.org/10.1073/pnas.1620119114.

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As a consequence of their remarkable adaptability, fast growth, and superior wood properties, eucalypt tree plantations have emerged as key renewable feedstocks (over 20 million ha globally) for the production of pulp, paper, bioenergy, and other lignocellulosic products. However, most biomass properties such as growth, wood density, and wood chemistry are complex traits that are hard to improve in long-lived perennials. Systems genetics, a process of harnessing multiple levels of component trait information (e.g., transcript, protein, and metabolite variation) in populations that vary in complex traits, has proven effective for dissecting the genetics and biology of such traits. We have applied a network-based data integration (NBDI) method for a systems-level analysis of genes, processes and pathways underlying biomass and bioenergy-related traits using a segregatingEucalyptushybrid population. We show that the integrative approach can link biologically meaningful sets of genes to complex traits and at the same time reveal the molecular basis of trait variation. Gene sets identified for related woody biomass traits were found to share regulatory loci, cluster in network neighborhoods, and exhibit enrichment for molecular functions such as xylan metabolism and cell wall development. These findings offer a framework for identifying the molecular underpinnings of complex biomass and bioprocessing-related traits. A more thorough understanding of the molecular basis of plant biomass traits should provide additional opportunities for the establishment of a sustainable bio-based economy.

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Yi, Nengjun, and Shizhong Xu. "Mapping quantitative trait loci for complex binary traits in outbred populations." Heredity 82, no. 6 (June 1999): 668–76. http://dx.doi.org/10.1046/j.1365-2540.1999.00529.x.

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36

Sillanpää, Mikko J., and Kari Auranen. "Replication in genetic studies of complex traits." Annals of Human Genetics 68, no. 6 (August 31, 2004): 646–57. http://dx.doi.org/10.1046/j.1529-8817.2004.00122.x.

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Thomsen, Simon Francis, Kirsten Ohm Kyvik, and Vibeke Backer. "Etiological Relationships in Atopy: A Review of Twin Studies." Twin Research and Human Genetics 11, no. 2 (April 1, 2008): 112–20. http://dx.doi.org/10.1375/twin.11.2.112.

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AbstractThe genetics of asthma and atopy has been studied frequently in twin populations from various parts of the world. However, emphasis has been put on univariate analysis of questionnaire data, whereas clinical and intermediate traits only sporadically have been studied, especially in multivariate settings. This review focuses on multivariate twin studies of atopy and related traits. We conclude that the genetic liability to most atopic traits is significantly correlated but that trait-specific genes also play a role. Previous studies have estimated the genetic correlation between upper and lower respiratory allergic symptoms, that is, asthma and hay fever, to be between .47 and .95. Furthermore, atopic traits share a portion of their genetic determinants with other complex disorders like obesity and behavioral traits. A correlation of about .3 and .34 has been reported between genes associated with asthma and obesity, and between genes associated with asthma and depression, respectively. We emphasize that multivariate methods applied to twin studies, especially when genetic marker information is available, provide a valuable framework within which complex etiological mechanisms underlying atopy can be disentangled.

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Qadri, Qamar Raza, Qingbo Zhao, Xueshuang Lai, Zhenyang Zhang, Wei Zhao, Yuchun Pan, and Qishan Wang. "Estimation of Complex-Trait Prediction Accuracy from the Different Holo-Omics Interaction Models." Genes 13, no. 9 (September 2, 2022): 1580. http://dx.doi.org/10.3390/genes13091580.

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Statistical models play a significant role in designing competent breeding programs related to complex traits. Recently; the holo-omics framework has been productively utilized in trait prediction; but it contains many complexities. Therefore; it is desirable to establish prediction accuracy while combining the host’s genome and microbiome data. Several methods can be used to combine the two data in the model and study their effectiveness by estimating the prediction accuracy. We validate our holo-omics interaction models with analysis from two publicly available datasets and compare them with genomic and microbiome prediction models. We illustrate that the holo-omics interactive models achieved the highest prediction accuracy in ten out of eleven traits. In particular; the holo-omics interaction matrix estimated using the Hadamard product displayed the highest accuracy in nine out of eleven traits, with the direct holo-omics model and microbiome model showing the highest prediction accuracy in the remaining two traits. We conclude that comparing prediction accuracy in different traits using real data showed important intuitions into the holo-omics architecture of complex traits.

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Robinson, Matthew R., Naomi R. Wray, and Peter M. Visscher. "Explaining additional genetic variation in complex traits." Trends in Genetics 30, no. 4 (April 2014): 124–32. http://dx.doi.org/10.1016/j.tig.2014.02.003.

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Bureau, Alexandre, Josée Dupuis, Brooke Hayward, Kathleen Falls, and Paul Van Eerdewegh. "Mapping complex traits using Random Forests." BMC Genetics 4, Suppl 1 (2003): S64. http://dx.doi.org/10.1186/1471-2156-4-s1-s64.

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Hormozdiari, Farhad, Anthony Zhu, Gleb Kichaev, Chelsea J. T. Ju, Ayellet V. Segrè, Jong Wha J. Joo, Hyejung Won, et al. "Widespread Allelic Heterogeneity in Complex Traits." American Journal of Human Genetics 100, no. 5 (May 2017): 789–802. http://dx.doi.org/10.1016/j.ajhg.2017.04.005.

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Manfredi, E., L. Tusell, and Z. G. Vitezica. "Prediction of complex traits: Conciliating genetics and statistics." Journal of Animal Breeding and Genetics 134, no. 3 (May 15, 2017): 178–83. http://dx.doi.org/10.1111/jbg.12269.

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Tiira, Katriina. "Canine anxiety genetics: challenges of phenotyping complex traits." Journal of Veterinary Behavior 10, no. 5 (September 2015): 442. http://dx.doi.org/10.1016/j.jveb.2015.07.017.

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Ayroles, Julien F., Mary Anna Carbone, Eric A. Stone, Katherine W. Jordan, Richard F. Lyman, Michael M. Magwire, Stephanie M. Rollmann, et al. "Systems genetics of complex traits in Drosophila melanogaster." Nature Genetics 41, no. 3 (February 22, 2009): 299–307. http://dx.doi.org/10.1038/ng.332.

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Kruglyak, Leonid. "Complex Traits and Simple Systems: An Interview with Leonid Kruglyak." Genetics 203, no. 3 (July 2016): 1023–25. http://dx.doi.org/10.1534/genetics.116.191866.

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Bellot, Pau, Gustavo de los Campos, and Miguel Pérez-Enciso. "Can Deep Learning Improve Genomic Prediction of Complex Human Traits?" Genetics 210, no. 3 (August 31, 2018): 809–19. http://dx.doi.org/10.1534/genetics.118.301298.

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Tasnim, Sana, Scott G. Wilson, John P. Walsh, and Dale R. Nyholt. "Cross-Trait Genetic Analyses Indicate Pleiotropy and Complex Causal Relationships between Headache and Thyroid Function Traits." Genes 14, no. 1 (December 21, 2022): 16. http://dx.doi.org/10.3390/genes14010016.

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Epidemiological studies have reported a comorbid relationship between headache and thyroid traits; however, little is known about the shared genetics and causality that contributes to this association. We investigated the genetic overlap and associations between headache and thyroid function traits using genome-wide association study (GWAS) data. We found a significant genetic correlation (rg) with headache and hypothyroidism (rg = 0.09, p = 2.00 × 10−4), free thyroxine (fT4) (rg = 0.08, p = 5.50 × 10−3), and hyperthyroidism (rg = –0.14, p = 1.80 × 10−3), a near significant genetic correlation with secondary hypothyroidism (rg = 0.20, p = 5.24 × 10−2), but not with thyroid stimulating hormone (TSH). Pairwise-GWAS analysis revealed six, 14, four and five shared (pleiotropic) loci with headache and hypothyroidism, hyperthyroidism, secondary hypothyroidism, and fT4, respectively. Cross-trait GWAS meta-analysis identified novel genome-wide significant loci for headache: five with hypothyroidism, three with secondary hypothyroidism, 12 with TSH, and nine with fT4. Of the genes at these loci, six (FAF1, TMX2-CTNND1, AARSD1, PLCD3, ZNF652, and C20orf203; headache-TSH) and six (HMGB1P45, RPL30P1, ZNF462, TMX2-CTNND1, ITPK1, SECISBP2L; headache-fT4) were significant in our gene-based analysis (pFisher’s combined p-value < 2.09 × 10−6). Our causal analysis suggested a positive causal relationship between headache and secondary hypothyroidism (p = 3.64 × 10−4). The results also suggest a positive causal relationship between hypothyroidism and headache (p = 2.45 × 10−3) and a negative causal relationship between hyperthyroidism and headache (p = 1.16 × 10−13). These findings suggest a strong evidence base for a genetic correlation and complex causal relationships between headache and thyroid traits.

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Que, Excel, Kristen L. James, Alisha R. Coffey, Tangi L. Smallwood, Jody Albright, M. Nazmul Huda, Daniel Pomp, Praveen Sethupathy, and Brian J. Bennett. "Genetic Architecture Modulates Diet-Induced Hepatic mRNA and miRNA Expression Profiles in Diversity Outbred Mice." Genetics 216, no. 1 (August 6, 2020): 241–59. http://dx.doi.org/10.1534/genetics.120.303481.

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Genetic approaches in model organisms have consistently demonstrated that molecular traits such as gene expression are under genetic regulation, similar to clinical traits. The resulting expression quantitative trait loci (eQTL) have revolutionized our understanding of genetic regulation and identified numerous candidate genes for clinically relevant traits. More recently, these analyses have been extended to other molecular traits such as protein abundance, metabolite levels, and miRNA expression. Here, we performed global hepatic eQTL and microRNA expression quantitative trait loci (mirQTL) analysis in a population of Diversity Outbred mice fed two different diets. We identified several key features of eQTL and mirQTL, namely differences in the mode of genetic regulation (cis or trans) between mRNA and miRNA. Approximately 50% of mirQTL are regulated by a trans-acting factor, compared to ∼25% of eQTL. We note differences in the heritability of mRNA and miRNA expression and variance explained by each eQTL or mirQTL. In general, cis-acting variants affecting mRNA or miRNA expression explain more phenotypic variance than trans-acting variants. Lastly, we investigated the effect of diet on the genetic architecture of eQTL and mirQTL, highlighting the critical effects of environment on both eQTL and mirQTL. Overall, these data underscore the complex genetic regulation of two well-characterized RNA classes (mRNA and miRNA) that have critical roles in the regulation of clinical traits and disease susceptibility.

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Lin, Wan-Yu, Chang-Chuan Chan, Yu-Li Liu, Albert C. Yang, Shih-Jen Tsai, and Po-Hsiu Kuo. "Sex-specific autosomal genetic effects across 26 human complex traits." Human Molecular Genetics 29, no. 7 (March 11, 2020): 1218–28. http://dx.doi.org/10.1093/hmg/ddaa040.

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Abstract Previous studies have shown that men and women have different genetic architectures across many traits. However, except waist-to-hip ratio (WHR) and waist circumference (WC), it remains unknown whether the genetic effects of a certain trait are weaker or stronger on men/women. With ~18 000 Taiwan Biobank subjects, we comprehensively investigate sexual heterogeneity in autosomal genetic effects, for traits regarding cardiovascular health, diabetes, kidney, liver, anthropometric profiles, blood, etc. ‘Gene-by-sex interactions’ (G $\times$ S) were detected in 18 out of 26 traits, each with an interaction P-value (${{P}}_{{INT}}$) less than $0.05/104={0.00048}$, where 104 is the number of tests conducted in this study. The most significant evidence of G $\times$ S was found in WHR (${{P}}_{{INT}}$ = 3.2 $\times{{10}}^{-{55}}$) and WC (${{P}}_{{INT}}$ = 2.3$\times{{10}}^{-{41}}$). As a novel G$\times$S investigation for other traits, we here find that the autosomal genetic effects are weaker on women than on men, for low-density lipoprotein cholesterol (LDL-C), uric acid (UA) and diabetes-related traits such as fasting glucose and glycated hemoglobin. For LDL-C and UA, the evidence of G$\times$S is especially notable in subjects aged less than 50 years, where estrogen can play a role in attenuating the autosomal genetic effects of these two traits. Men and women have systematically distinct environmental contexts caused by hormonal milieu and their specific society roles, which may trigger diverse gene expressions despite the same DNA materials. As many environmental exposures are difficult to collect and quantify, sex can serve as a good surrogate for these factors.

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Jermstad, Kathleen D., Daniel L. Bassoni, Keith S. Jech, Gary A. Ritchie, Nicholas C. Wheeler, and David B. Neale. "Mapping of Quantitative Trait Loci Controlling Adaptive Traits in Coastal Douglas Fir. III. Quantitative Trait Loci-by-Environment Interactions." Genetics 165, no. 3 (November 1, 2003): 1489–506. http://dx.doi.org/10.1093/genetics/165.3.1489.

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Abstract Quantitative trait loci (QTL) were mapped in the woody perennial Douglas fir (Pseudotsuga menziesii var. menziesii [Mirb.] Franco) for complex traits controlling the timing of growth initiation and growth cessation. QTL were estimated under controlled environmental conditions to identify QTL interactions with photoperiod, moisture stress, winter chilling, and spring temperatures. A three-generation mapping population of 460 cloned progeny was used for genetic mapping and phenotypic evaluations. An all-marker interval mapping method was used for scanning the genome for the presence of QTL and single-factor ANOVA was used for estimating QTL-by-environment interactions. A modest number of QTL were detected per trait, with individual QTL explaining up to 9.5% of the phenotypic variation. Two QTL-by-treatment interactions were found for growth initiation, whereas several QTL-by-treatment interactions were detected among growth cessation traits. This is the first report of QTL interactions with specific environmental signals in forest trees and will assist in the identification of candidate genes controlling these important adaptive traits in perennial plants.

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