Evaluating epistatic interaction signals in complex traits using quantitative traits

Quantitative traits—be they morphological or physiological characters, aspects of behavior, or genome-level features such as the amount of RNA or protein expression for a specific gene—usually show considerable variation within and among populations. Quantitative genetics, also referred to as the genetics of complex traits, is the study of such characters and is based on mathematical models of evolution in which many genes influence the trait and in which non-genetic factors may also be important. Evolution and Selection of Quantitative Traits presents a holistic treatment of the subject, showing the interplay between theory and data with extensive discussions on statistical issues relating to the estimation of the biologically relevant parameters for these models. Quantitative genetics is viewed as the bridge between complex mathematical models of trait evolution and real-world data, and the authors have clearly framed their treatment as such. This is the second volume in a planned trilogy that summarizes the modern field of quantitative genetics, informed by empirical observations from wide-ranging fields (agriculture, evolution, ecology, and human biology) as well as population genetics, statistical theory, mathematical modeling, genetics, and genomics. Whilst volume 1 (1998) dealt with the genetics of such traits, the main focus of volume 2 is on their evolution, with a special emphasis on detecting selection (ranging from the use of genomic and historical data through to ecological field data) and examining its consequences. This extensive work of reference is suitable for graduate level students as well as professional researchers (both empiricists and theoreticians) in the fields of evolutionary biology, genetics, and genomics. It will also be of particular relevance and use to plant and animal breeders, human geneticists, and statisticians.

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

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2016.0569 ◽

2016 ◽

Vol 283 (1835) ◽

pp. 20160569 ◽

Cited By ~ 52

Author(s):

M. E. Goddard ◽

K. E. Kemper ◽

I. M. MacLeod ◽

A. J. Chamberlain ◽

B. J. Hayes

Keyword(s):

Complex Traits ◽

Genetic Architecture ◽

Quantitative Traits ◽

Association Studies ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Crop Breeding ◽

Single Nucleotide ◽

Genome Wide ◽

Phenotype Identification

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

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PSVIII-26 Gene editing of complex traits

Journal of Animal Science ◽

10.1093/jas/skz258.529 ◽

2019 ◽

Vol 97 (Supplement_3) ◽

pp. 259-260

Author(s):

Ashley Ling ◽

Romdhane Rekaya

Keyword(s):

Genetic Engineering ◽

Complex Traits ◽

Genetic Architecture ◽

Quantitative Traits ◽

Gene Editing ◽

Genetic Model ◽

Variable Number ◽

Production Parameters ◽

Potential Gain ◽

Better Than

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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Optimizing the Power to Identify the Genetic Basis of Complex Traits with Evolve and Resequence Studies

Molecular Biology and Evolution ◽

10.1093/molbev/msz183 ◽

2019 ◽

Vol 36 (12) ◽

pp. 2890-2905 ◽

Cited By ~ 2

Author(s):

Christos Vlachos ◽

Robert Kofler

Keyword(s):

Complex Traits ◽

Quantitative Traits ◽

Genetic Basis ◽

Genome Wide Association Study ◽

Selection Regime ◽

Genome Wide ◽

A Genome ◽

Higher Power ◽

Powerful Approach ◽

Next Generation Sequencing Ngs

Abstract Evolve and resequence (E&R) studies are frequently used to dissect the genetic basis of quantitative traits. By subjecting a population to truncating selection for several generations and estimating the allele frequency differences between selected and nonselected populations using next-generation sequencing (NGS), the loci contributing to the selected trait may be identified. The role of different parameters, such as, the population size or the number of replicate populations has been examined in previous works. However, the influence of the selection regime, that is the strength of truncating selection during the experiment, remains little explored. Using whole genome, individual based forward simulations of E&R studies, we found that the power to identify the causative alleles may be maximized by gradually increasing the strength of truncating selection during the experiment. Notably, such an optimal selection regime comes at no or little additional cost in terms of sequencing effort and experimental time. Interestingly, we also found that a selection regime which optimizes the power to identify the causative loci is not necessarily identical to a regime that maximizes the phenotypic response. Finally, our simulations suggest that an E&R study with an optimized selection regime may have a higher power to identify the genetic basis of quantitative traits than a genome-wide association study, highlighting that E&R is a powerful approach for finding the loci underlying complex traits.

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Mapping quantitative trait loci with epistatic effects

Genetics Research ◽

10.1017/s0016672301005511 ◽

2002 ◽

Vol 79 (2) ◽

pp. 185-198 ◽

Cited By ~ 50

Author(s):

NENGJUN YI ◽

SHIZHONG XU

Keyword(s):

Quantitative Trait Loci ◽

Quantitative Trait ◽

Complex Traits ◽

Quantitative Traits ◽

Threshold Model ◽

Monte Carlo Algorithm ◽

Epistatic Effects ◽

Trait Loci ◽

Robust Statistical Methods ◽

Qtl Effects

Epistatic variance can be an important source of variation for complex traits. However, detecting epistatic effects is difficult primarily due to insufficient sample sizes and lack of robust statistical methods. In this paper, we develop a Bayesian method to map multiple quantitative trait loci (QTLs) with epistatic effects. The method can map QTLs in complicated mating designs derived from the cross of two inbred lines. In addition to mapping QTLs for quantitative traits, the proposed method can even map genes underlying binary traits such as disease susceptibility using the threshold model. The parameters of interest are various QTL effects, including additive, dominance and epistatic effects of QTLs, the locations of identified QTLs and even the number of QTLs. When the number of QTLs is treated as an unknown parameter, the dimension of the model becomes a variable. This requires the reversible jump Markov chain Monte Carlo algorithm. The utility of the proposed method is demonstrated through analysis of simulation data.

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Invited review: Genome-wide association analysis for quantitative traits in livestock – a selective review of statistical models and experimental designs

Archives Animal Breeding ◽

10.5194/aab-60-335-2017 ◽

2017 ◽

Vol 60 (3) ◽

pp. 335-346 ◽

Cited By ~ 17

Author(s):

Markus Schmid ◽

Jörn Bennewitz

Keyword(s):

Statistical Models ◽

Complex Traits ◽

Quantitative Traits ◽

Association Studies ◽

Real Data ◽

Genome Wide Association ◽

Future Research ◽

Genome Wide Association Studies ◽

Livestock Breeding ◽

Genome Wide

Abstract. Quantitative or complex traits are controlled by many genes and environmental factors. Most traits in livestock breeding are quantitative traits. Mapping genes and causative mutations generating the genetic variance of these traits is still a very active area of research in livestock genetics. Since genome-wide and dense SNP panels are available for most livestock species, genome-wide association studies (GWASs) have become the method of choice in mapping experiments. Different statistical models are used for GWASs. We will review the frequently used single-marker models and additionally describe Bayesian multi-marker models. The importance of nonadditive genetic and genotype-by-environment effects along with GWAS methods to detect them will be briefly discussed. Different mapping populations are used and will also be reviewed. Whenever possible, our own real-data examples are included to illustrate the reviewed methods and designs. Future research directions including post-GWAS strategies are outlined.

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Optimizing the power to identify the genetic basis of complex traits with Evolve and Resequence studies

10.1101/583682 ◽

2019 ◽

Cited By ~ 2

Author(s):

Christos Vlachos ◽

Robert Kofler

Keyword(s):

Complex Traits ◽

Quantitative Traits ◽

Genetic Basis ◽

Optimal Selection ◽

Selection Regime ◽

Higher Power ◽

Powerful Approach ◽

Selection For ◽

Generation Sequencing

AbstractEvolve and Resequence (E&R) studies are frequently used to dissect the genetic basis of quantitative traits. By subjecting a population to truncating selection for several generations and estimating the allele frequency differences between selected and non-selected populations using Next Generation Sequencing, the loci contributing to the selected trait may be identified. The role of different parameters, such as, the population size or the number of replicate populations have been examined in previous works. However, the influence of the selection regime, i.e. the strength of truncating selection during the experiment, remains little explored. Using whole genome, individual based forward simulations of E&R studies, we found that the power to identify the causative alleles may be maximized by gradually increasing the strength of truncating selection during the experiment. Notably, such an optimal selection regime comes at no or little additional cost in terms of sequencing effort and experimental time. Interestingly, we also found that a selection regime which optimizes the power to identify the causative loci is not necessarily identical to a regime that maximizes the phenotypic response. Finally, our simulations suggest that an E&R study with an optimized selection regime may have a higher power to identify the genetic basis of quantitative traits than a GWAS, highlighting that E&R is a powerful approach for finding the loci underlying complex traits.

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Discovery of novel haplotypes for complex traits in landraces

10.1101/2020.05.25.114264 ◽

2020 ◽

Author(s):

Manfred Mayer ◽

Armin C. Hölker ◽

Eric González-Segovia ◽

Thomas Presterl ◽

Milena Ouzunova ◽

...

Keyword(s):

Genetic Variation ◽

Complex Traits ◽

Quantitative Traits ◽

Plant Genetic Resources ◽

Breeding Lines ◽

A Genome ◽

Dh Lines ◽

Native Diversity ◽

Elite Germplasm ◽

Novel Variation

AbstractGenetic variation is of crucial importance for selection and genetic improvement of crops. Landraces are valuable sources of diversity for germplasm improvement, but for quantitative traits efficient strategies for their targeted utilization are lacking. Here, we propose a genome-based strategy for making native diversity accessible for traits with limited genetic variation in elite germplasm. We generated ~ 1,000 doubled-haploid (DH) lines from three European maize landraces, pre-selected based on molecular and phenotypic information. Using GWAS, we mapped haplotype-trait associations for early development traits at high resolution in eleven environments. Molecular haplotype inventories of landrace derived DH libraries and a broad panel of 65 breeding lines based on 501,124 SNPs revealed novel variation for target traits in the landraces. DH lines carrying these novel haplotypes outperformed breeding lines not carrying the respective haplotypes. Most haplotypes associated with target traits showed stable effects across populations and environments and only limited correlated effects with undesired traits making them ideal for introgression into elite germplasm. Our strategy was successful in linking molecular variation to meaningful phenotypes and identifying novel variation for quantitative traits in plant genetic resources.

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Development of a multiparent advanced generation intercross (MAGIC) population for genetic exploitation of complex traits in Brassica juncea: glucosinolate content as an example

10.1101/793331 ◽

2019 ◽

Author(s):

Tianya Wang ◽

Wei Wan ◽

Kunjiang Yu ◽

Aimal Nawaz Khattak ◽

Botao Ye ◽

...

Keyword(s):

Association Mapping ◽

Complex Traits ◽

Quantitative Traits ◽

Recombinant Inbred Lines ◽

Glucosinolate Content ◽

Intron Length Polymorphism ◽

Whole Process ◽

Specific Pcr ◽

Advanced Generation ◽

Magic Population

AbstractMultiparent advanced generation intercross (MAGIC) populations have recently been developed to allow the high-resolution mapping of complex quantitative traits. This article describes the development of one MAGIC population and verifies its potential application for mapping quantitative trait loci (QTLs) in B. juncea. The population was developed from eight founders with diverse traits and composed of 408 F6 recombinant inbred lines (RILs). To develop one rapid and simplified way for using the MAGIC population, a subset of 133 RILs as the primary mapping population were genotyped using 346 intron-length polymorphism (ILP) polymorphic markers. The population lacks significant signatures of population structure that are suitable for the analysis of complex traits. Genome-wide association mapping (GWAS) identified three major glucosinolate (GSL) QTLs of QGsl.ig01.1 on J01 for indole GSL (IG), QGsl.atg09.1 on J09 and QGsl.atg11.1 on J11 for aliphatic GSL (AG) and total GSL (TG). The candidate genes for QGsl.ig01.1, QGsl.atg09.1 and QGsl.atg11.1 are GSH1, GSL-ALK and MYB28, which are involved in converting glutamate and cysteine to γ–EC, the accumulation of glucoraphanin, and the whole process of AG metabolism, respectively. One effective method for association mapping of quantitative traits in the B. juncea MAGIC population is also suggested by utilization of the remaining 275 RILs and incorporation of the novel kompetitive allele specific PCR (KASP) technique. In addition to its QTL mapping purpose, the MAGIC population could also be potentially utilized in variety development by breeders.

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What Has a Century of Quantitative Genetics Taught Us About Nature's Genetic Tool Kit?

Annual Review of Genetics ◽

10.1146/annurev-genet-021920-102037 ◽

2020 ◽

Vol 54 (1) ◽

pp. 439-464

Author(s):

Christopher M. Jakobson ◽

Daniel F. Jarosz

Keyword(s):

Genetic Variants ◽

Complex Traits ◽

Quantitative Traits ◽

Genetic Locus ◽

Molecular Variation ◽

Genetic Tool ◽

Standing Variation ◽

Epigenetic State ◽

Large Numbers ◽

Living Organisms

The complexity of heredity has been appreciated for decades: Many traits are controlled not by a single genetic locus but instead by polymorphisms throughout the genome. The importance of complex traits in biology and medicine has motivated diverse approaches to understanding their detailed genetic bases. Here, we focus on recent systematic studies, many in budding yeast, which have revealed that large numbers of all kinds of molecular variation, from noncoding to synonymous variants, can make significant contributions to phenotype. Variants can affect different traits in opposing directions, and their contributions can be modified by both the environment and the epigenetic state of the cell. The integration of prospective (synthesizing and analyzing variants) and retrospective (examining standing variation) approaches promises to reveal how natural selection shapes quantitative traits. Only by comprehensively understanding nature's genetic tool kit can we predict how phenotypes arise from the complex ensembles of genetic variants in living organisms.

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