Journal articles: 'Complex traits'

1

Gurwitz, D. "Complex traits, complex answers." Molecular Psychiatry 2, no. 2 (March 1997): 89–90. http://dx.doi.org/10.1038/sj.mp.4000255.

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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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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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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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Phillips, Patrick C. "From complex traits to complex alleles." Trends in Genetics 15, no. 1 (January 1999): 6–8. http://dx.doi.org/10.1016/s0168-9525(98)01622-9.

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Rancelis, Tautvydas, Ingrida Domarkiene, Laima Ambrozaityte, and Algirdas Utkus. "Implementing Core Genes and an Omnigenic Model for Behaviour Traits Prediction in Genomics." Genes 14, no. 8 (August 16, 2023): 1630. http://dx.doi.org/10.3390/genes14081630.

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A high number of genome variants are associated with complex traits, mainly due to genome-wide association studies (GWAS). Using polygenic risk scores (PRSs) is a widely accepted method for calculating an individual’s complex trait prognosis using such data. Unlike monogenic traits, the practical implementation of complex traits by applying this method still falls behind. Calculating PRSs from all GWAS data has limited practical usability in behaviour traits due to statistical noise and the small effect size from a high number of genome variants involved. From a behaviour traits perspective, complex traits are explored using the concept of core genes from an omnigenic model, aiming to employ a simplified calculation version. Simplification may reduce the accuracy compared to a complete PRS encompassing all trait-associated variants. Integrating genome data with datasets from various disciplines, such as IT and psychology, could lead to better complex trait prediction. This review elucidates the significance of clear biological pathways in understanding behaviour traits. Specifically, it highlights the essential role of genes related to hormones, enzymes, and neurotransmitters as robust core genes in shaping these traits. Significant variations in core genes are prominently observed in behaviour traits such as stress response, impulsivity, and substance use.

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Schork, Nicholas J. "Genetically Complex Cardiovascular Traits." Hypertension 29, no. 1 (January 1997): 145–49. http://dx.doi.org/10.1161/01.hyp.29.1.145.

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Womack, James E., Hyun‐Jun Jang, and Mi Ok Lee. "Genomics of complex traits." Annals of the New York Academy of Sciences 1271, no. 1 (October 2012): 33–36. http://dx.doi.org/10.1111/j.1749-6632.2012.06733.x.

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Ofria, Charles, Wei Huang, and Eric Torng. "On the Gradual Evolution of Complexity and the Sudden Emergence of Complex Features." Artificial Life 14, no. 3 (July 2008): 255–63. http://dx.doi.org/10.1162/artl.2008.14.3.14302.

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Evolutionary theory explains the origin of complex organismal features through a combination of reusing and extending information from less-complex traits, and by needing to exploit only one of many unlikely pathways to a viable solution. While the appearance of a new trait may seem sudden, we show that the underlying information associated with each trait evolves gradually. We study this process using digital organisms, self-replicating computer programs that mutate and evolve novel traits, including complex logic operations. When a new complex trait first appears, its proper function immediately requires the coordinated operation of many genomic positions. As the information associated with a trait increases, the probability of its simultaneous introduction drops exponentially, so it is nearly impossible for a significantly complex trait to appear without reusing existing information. We show that the total information stored in the genome increases only marginally when a trait first appears. Furthermore, most of the information associated with a new trait is either correlated with existing traits or co-opted from traits that were lost in conjunction with the appearance of the new trait. Thus, while total genomic information increases incrementally, traits that require much more information can still arise during the evolutionary process.

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Ou, Jen-Hsiang, Tilman Rönneburg, Örjan Carlborg, Christa Ferst Honaker, Paul B. Siegel, and Carl-Johan Rubin. "Complex genetic architecture of the chicken Growth1 QTL region." PLOS ONE 19, no. 5 (May 13, 2024): e0295109. http://dx.doi.org/10.1371/journal.pone.0295109.

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The genetic complexity of polygenic traits represents a captivating and intricate facet of biological inheritance. Unlike Mendelian traits controlled by a single gene, polygenic traits are influenced by multiple genetic loci, each exerting a modest effect on the trait. This cumulative impact of numerous genes, interactions among them, environmental factors, and epigenetic modifications results in a multifaceted architecture of genetic contributions to complex traits. Given the well-characterized genome, diverse traits, and range of genetic resources, chicken (Gallus gallus) was employed as a model organism to dissect the intricate genetic makeup of a previously identified major Quantitative Trait Loci (QTL) for body weight on chromosome 1. A multigenerational advanced intercross line (AIL) of 3215 chickens whose genomes had been sequenced to an average of 0.4x was analyzed using genome-wide association study (GWAS) and variance-heterogeneity GWAS (vGWAS) to identify markers associated with 8-week body weight. Additionally, epistatic interactions were studied using the natural and orthogonal interaction (NOIA) model. Six genetic modules, two from GWAS and four from vGWAS, were strongly associated with the studied trait. We found evidence of both additive- and non-additive interactions between these modules and constructed a putative local epistasis network for the region. Our screens for functional alleles revealed a missense variant in the gene ribonuclease H2 subunit B (RNASEH2B), which has previously been associated with growth-related traits in chickens and Darwin’s finches. In addition, one of the most strongly associated SNPs identified is located in a non-coding region upstream of the long non-coding RNA, ENSGALG00000053256, previously suggested as a candidate gene for regulating chicken body weight. By studying large numbers of individuals from a family material using approaches to capture both additive and non-additive effects, this study advances our understanding of genetic complexities in a highly polygenic trait and has practical implications for poultry breeding and agriculture.

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Qi, Jiandong, Jianfeng Sun, and Jianxin Wang. "E-Index for Differentiating Complex Dynamic Traits." BioMed Research International 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/5761983.

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While it is a daunting challenge in current biology to understand how the underlying network of genes regulates complex dynamic traits, functional mapping, a tool for mapping quantitative trait loci (QTLs) and single nucleotide polymorphisms (SNPs), has been applied in a variety of cases to tackle this challenge. Though useful and powerful, functional mapping performs well only when one or more model parameters are clearly responsible for the developmental trajectory, typically being a logistic curve. Moreover, it does not work when the curves are more complex than that, especially when they are not monotonic. To overcome this inadaptability, we therefore propose a mathematical-biological concept and measurement,E-index (earliness-index), which cumulatively measures the earliness degree to which a variable (or a dynamic trait) increases or decreases its value. Theoretical proofs and simulation studies show thatE-index is more general than functional mapping and can be applied to any complex dynamic traits, including those with logistic curves and those with nonmonotonic curves. Meanwhile,E-index vector is proposed as well to capture more subtle differences of developmental patterns.

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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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13

Ott, Jurg. "Complex traits on the map." Nature 379, no. 6568 (February 1996): 772–73. http://dx.doi.org/10.1038/379772a0.

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14

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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15

Cheng, Jack C. Y., Nelson L. S. Tang, Hiu-Yan Yeung, and Nancy Miller. "Genetic Association of Complex Traits." Clinical Orthopaedics and Related Research 462 (September 2007): 38–44. http://dx.doi.org/10.1097/blo.0b013e3180d09dcc.

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16

Lander, E., and N. Schork. "Genetic dissection of complex traits." Science 265, no. 5181 (September 30, 1994): 2037–48. http://dx.doi.org/10.1126/science.8091226.

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Todorov, Alexandre A. "Genetic Dissection of Complex Traits." American Journal of Human Genetics 71, no. 1 (July 2002): 209–10. http://dx.doi.org/10.1086/341033.

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Marian, A. J. "Genetic Causality in Complex Traits." Journal of the American College of Cardiology 67, no. 4 (February 2016): 417–19. http://dx.doi.org/10.1016/j.jacc.2015.09.109.

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19

Dermitzakis, Emmanouil T. "Cellular genomics for complex traits." Nature Reviews Genetics 13, no. 3 (February 14, 2012): 215–20. http://dx.doi.org/10.1038/nrg3115.

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20

Lee, Michael. "Molecular Dissection of Complex Traits." Crop Science 38, no. 4 (July 1998): 1114–15. http://dx.doi.org/10.2135/cropsci1998.0011183x003800040039x.

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21

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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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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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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Olson, Jane M., John S. Witte, and Robert C. Elston. "Genetic mapping of complex traits." Statistics in Medicine 18, no. 21 (November 15, 1999): 2961–81. http://dx.doi.org/10.1002/(sici)1097-0258(19991115)18:21<2961::aid-sim206>3.0.co;2-u.

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Lander, Eric S., and Nicholas J. Schork. "Genetic Dissection of Complex Traits." Focus 4, no. 3 (August 2006): 442–58. http://dx.doi.org/10.1176/foc.4.3.442.

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Segura, V., C. Denancé, C. E. Durel, and E. Costes. "Wide range QTL analysis for complex architectural traits in a 1-year-old apple progeny." Genome 50, no. 2 (February 2007): 159–71. http://dx.doi.org/10.1139/g07-002.

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The present study aimed at investigating the genetic determinisms of architectural traits in a 1-year-old apple ( Malus × domestica Borkh.). F1 progeny. A precise phenotyping including both tree topology and geometry was performed on 123 offspring. For a wide range of developmental traits, broad-sense heritability was estimated and quantitative trait loci (QTLs) were investigated. Several loci controlling geometry were identified (i) for integrated traits, such as tree surface and volume; (ii) for traits related to the form of long sylleptic axillary shoots (LSAS), such as bending and basis angle; and (iii) for traits of finer components, such as internode length of the trunk and LSAS. Considering topology, 4 QTLs were mapped for the total number of sylleptic branching in the tree, suggesting a strong and complex genetic control that was analysed through colocalisations between QTLs mapped for the different shoot types (long, medium, short). Two QTLs were also mapped for a phenological trait (date of bud break). When several QTLs were detected for a trait, a linear model was built to test epistatic effects and estimate the whole percentage of variability explained. The discussion focuses on particular colocalisations and on the relevance of traits to further tree development.

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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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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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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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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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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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Ling, Ashley, and Romdhane Rekaya. "PSVIII-26 Gene editing of complex traits." Journal of Animal Science 97, Supplement_3 (December 2019): 259–60. http://dx.doi.org/10.1093/jas/skz258.529.

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Abstract Gene editing (GE) is a form of genetic engineering in which DNA is removed, inserted or replaced. For simple monogenic traits, the technology has been successfully implemented to create heritable modifications in animals and plants. The benefits of these niche applications are undeniable. For quantitative traits the benefits of GE are hard to quantify mainly because these traits are not genetic enough (low to moderate heritability) and their genetic architecture is often complex. Because its impact on the gene pool through the introduction of heritable modifications, the potential gain from GE must be evaluated within reasonable production parameters and in comparison, with available tools used in animal selection. A simulation was performed to compare GE with genomic selection (GS) and QTN-assisted selection (QAS) under four experimental factors: 1) heritability (0.1 or 0.4), 2) number of QTN affecting the trait (1000 or 10000) and their effect distribution (Gamma or uniform); 3) Percentage of selected females (100% or 33%); and 4) fixed or variable number of edited QTNs. Three models GS (M1), GS and GE (M2), and GS and QAS (M3) were implemented and compared. When the QTN effects were sampled from a Gamma distribution, all females were selected, and non-segregating QTNs were replaced, M2 clearly outperformed M1 and M3, with superiority ranging from 19 to 61%. Under the same scenario, M3 was 7 to 23% superior to M1. As the complexity of the genetic model increased (10000 QTN; uniform distribution), only one third of the females were selected, and the number of edited QTNs was fixed, the superiority of M2 was significantly reduced. In fact, M2 was only slightly better than M3 (2 to 6%). In all cases, M2 and M3 were better than M1. These results indicate that under realistic scenarios, GE for complex traits might have only limited advantages.

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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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Sun, Wu. "On Hair Follicle Development and Wool Production Traits in Sheep: A Review." International Journal of Agriculture and Biology 25, no. 02 (February 1, 2021): 450–54. http://dx.doi.org/10.17957/ijab/15.1687.

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Hair follicle and skin development is a complex biological process involving many regulatory molecules. Wool trait is a complex quantitative trait controlled by multiple genes and affected by environment. In this paper, the histomorphology of hair follicle development in sheep and the molecular mechanism of hair follicle and wool traits formation were reviewed in order to provide theoretical basis for breeding and selection of sheep wool traits. © 2021 Friends Science Publishers

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Khatiwada, Aastha, Ayse Selen Yilmaz, Bethany J. Wolf, Maciej Pietrzak, and Dongjun Chung. "multi-GPA-Tree: Statistical approach for pleiotropy informed and functional annotation tree guided prioritization of GWAS results." PLOS Computational Biology 19, no. 12 (December 7, 2023): e1011686. http://dx.doi.org/10.1371/journal.pcbi.1011686.

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Genome-wide association studies (GWAS) have successfully identified over two hundred thousand genotype-trait associations. Yet some challenges remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), most with small or moderate effect sizes, making them difficult to detect. Second, many complex traits share a common genetic basis due to ‘pleiotropy’ and and though few methods consider it, leveraging pleiotropy can improve statistical power to detect genotype-trait associations with weaker effect sizes. Third, currently available statistical methods are limited in explaining the functional mechanisms through which genetic variants are associated with specific or multiple traits. We propose multi-GPA-Tree to address these challenges. The multi-GPA-Tree approach can identify risk SNPs associated with single as well as multiple traits while also identifying the combinations of functional annotations that can explain the mechanisms through which risk-associated SNPs are linked with the traits. First, we implemented simulation studies to evaluate the proposed multi-GPA-Tree method and compared its performance with existing statistical approaches. The results indicate that multi-GPA-Tree outperforms existing statistical approaches in detecting risk-associated SNPs for multiple traits. Second, we applied multi-GPA-Tree to a systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA), and to a Crohn’s disease (CD) and ulcertive colitis (UC) GWAS, and functional annotation data including GenoSkyline and GenoSkylinePlus. Our results demonstrate that multi-GPA-Tree can be a powerful tool that improves association mapping while facilitating understanding of the underlying genetic architecture of complex traits and potential mechanisms linking risk-associated SNPs with complex traits.

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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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Abraham, Abin, Abigail L. LaBella, John A. Capra, and Antonis Rokas. "Mosaic patterns of selection in genomic regions associated with diverse human traits." PLOS Genetics 18, no. 11 (November 7, 2022): e1010494. http://dx.doi.org/10.1371/journal.pgen.1010494.

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Natural selection shapes the genetic architecture of many human traits. However, the prevalence of different modes of selection on genomic regions associated with variation in traits remains poorly understood. To address this, we developed an efficient computational framework to calculate positive and negative enrichment of different evolutionary measures among regions associated with complex traits. We applied the framework to summary statistics from >900 genome-wide association studies (GWASs) and 11 evolutionary measures of sequence constraint, population differentiation, and allele age while accounting for linkage disequilibrium, allele frequency, and other potential confounders. We demonstrate that this framework yields consistent results across GWASs with variable sample sizes, numbers of trait-associated SNPs, and analytical approaches. The resulting evolutionary atlas maps diverse signatures of selection on genomic regions associated with complex human traits on an unprecedented scale. We detected positive enrichment for sequence conservation among trait-associated regions for the majority of traits (>77% of 290 high power GWASs), which included reproductive traits. Many traits also exhibited substantial positive enrichment for population differentiation, especially among hair, skin, and pigmentation traits. In contrast, we detected widespread negative enrichment for signatures of balancing selection (51% of GWASs) and absence of enrichment for evolutionary signals in regions associated with late-onset Alzheimer’s disease. These results support a pervasive role for negative selection on regions of the human genome that contribute to variation in complex traits, but also demonstrate that diverse modes of evolution are likely to have shaped trait-associated loci. This atlas of evolutionary signatures across the diversity of available GWASs will enable exploration of the relationship between the genetic architecture and evolutionary processes in the human genome.

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Mitchell-Olds, Thomas. "Selection on QTL and complex traits in complex environments." Molecular Ecology 22, no. 13 (June 26, 2013): 3427–29. http://dx.doi.org/10.1111/mec.12345.

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Zhu, Bin, Allison E. Ashley-Koch, and David B. Dunson. "Generalized Admixture Mapping for Complex Traits." G3: Genes|Genomes|Genetics 3, no. 7 (May 11, 2013): 1165–75. http://dx.doi.org/10.1534/g3.113.006478.

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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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Fu, Jingyuan, Eleonora A. M. Festen, and Cisca Wijmenga. "Multi-ethnic studies in complex traits." Human Molecular Genetics 20, R2 (September 2, 2011): R206—R213. http://dx.doi.org/10.1093/hmg/ddr386.

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Rubattu, Speranza. "Genetic Analysis of Complex Cardiovascular Traits." High Blood Pressure & Cardiovascular Prevention 11, no. 1 (2004): 29–33. http://dx.doi.org/10.2165/00151642-200411010-00005.

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Glazier, A. M. "Finding Genes That Underlie Complex Traits." Science 298, no. 5602 (December 20, 2002): 2345–49. http://dx.doi.org/10.1126/science.1076641.

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Giegling, I., A. Hartmann, J. Genius, J. Benninghoff, H. J. Möller, and D. Rujescu. "Systems Biology and Complex Neurobehavioral Traits." Pharmacopsychiatry 41, S 01 (September 2008): S32—S36. http://dx.doi.org/10.1055/s-2008-1081200.

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Spain, Sarah L., and Jeffrey C. Barrett. "Strategies for fine-mapping complex traits." Human Molecular Genetics 24, R1 (July 8, 2015): R111—R119. http://dx.doi.org/10.1093/hmg/ddv260.

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Wang, Xuefeng, Chenwu Xu, Rongling Wu, and Brian A. Larkins. "Genetic dissection of complex endosperm traits." Trends in Plant Science 14, no. 7 (July 2009): 391–98. http://dx.doi.org/10.1016/j.tplants.2009.04.004.

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Nathan, Brandon M., Joel N. Hirschhorn, and Mark R. Palmert. "Strategies for Studying Complex Genetic Traits." Endocrinologist 14, no. 6 (November 2004): 346–52. http://dx.doi.org/10.1097/01.ten.0000146242.75018.a9.

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Johnson, Mark L. "Identifying disease genes underlying complex traits." Clinical Reviews in Allergy & Immunology 22, no. 1 (February 2002): 3–10. http://dx.doi.org/10.1007/s12016-002-0002-1.

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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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Journal articles on the topic 'Complex traits'

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