scholarly journals Multiple ancestral haplotypes harboring regulatory mutations cumulatively contribute to a QTL affecting chicken growth traits

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Yuzhe Wang ◽  
Xuemin Cao ◽  
Chenglong Luo ◽  
Zheya Sheng ◽  
Chunyuan Zhang ◽  
...  
Keyword(s):  
Complex Traits ◽  
Quantitative Traits ◽  
Genetic Variance ◽  
Growth Traits ◽  
Major Effect ◽  
Chromosome 1 ◽  
Chicken Chromosome ◽  
Regulatory Variants ◽  
Regulatory Mutations ◽  
Ancestral Haplotypes

AbstractIn depth studies of quantitative trait loci (QTL) can provide insights to the genetic architectures of complex traits. A major effect QTL at the distal end of chicken chromosome 1 has been associated with growth traits in multiple populations. This locus was fine-mapped in a fifteen-generation chicken advanced intercross population including 1119 birds and explored in further detail using 222 sequenced genomes from 10 high/low body weight chicken stocks. We detected this QTL that, in total, contributed 14.4% of the genetic variance for growth. Further, nine mosaic precise intervals (Kb level) which contain ancestral regulatory variants were fine-mapped and we chose one of them to demonstrate the key regulatory role in the duodenum. This is the first study to break down the detail genetic architectures for the well-known QTL in chicken and provides a good example of the fine-mapping of various of quantitative traits in any species.

Evolution and Selection of Quantitative Traits

2018 ◽  
Author(s):  
Bruce Walsh ◽  
Michael Lynch
Keyword(s):  
Mathematical Models ◽  
Quantitative Genetics ◽  
Complex Traits ◽  
Evolutionary Biology ◽  
Quantitative Traits ◽  
Specific Gene ◽  
Real World Data ◽  
Holistic Treatment ◽  
Genetics And Genomics ◽  
Selection Of

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.

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

2016 ◽  
Vol 283 (1835) ◽  
pp. 20160569 ◽  
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.

scholarly journals Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals

2021 ◽  
Author(s):  
Valentin Hivert ◽  
Julia Sidorenko ◽  
Florian Rohart ◽  
Michael E. Goddard ◽  
Jian Yang ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Large Sample ◽  
Human Complex

scholarly journals Genetic variance of metabolomic features and their relationship with body weight and body weight gain in Holstein cattle1

2019 ◽  
Vol 97 (9) ◽  
pp. 3832-3844 ◽  
Author(s):  
Amir Aliakbari ◽  
Alireza Ehsani ◽  
Rasoul Vaez Torshizi ◽  
Peter Løvendahl ◽  
Hadi Esfandyari ◽  
...  
Keyword(s):  
Body Weight ◽  
Quantitative Traits ◽  
Genetic Variance ◽  
Body Weight Gain ◽  
Genetic Correlations ◽  
Alternative Methods ◽  
Data Set ◽  
Phenotypic Correlations ◽  
Binned Data ◽  
Univariate Analyses

Abstract In recent years, metabolomics has been used to clarify the biology underlying biological samples. In the field of animal breeding, investigating the magnitude of genetic control on the metabolomic profiles of animals and their relationships with quantitative traits adds valuable information to animal improvement schemes. In this study, we analyzed metabolomic features (MFs) extracted from the metabolomic profiles of 843 male Holstein calves. The metabolomic profiles were obtained using nuclear magnetic resonance (NMR) spectroscopy. We investigated 2 alternative methods to control for peak shifts in the NMR spectra, binning and aligning, to determine which approach was the most efficient for assessing genetic variance. Series of univariate analyses were implemented to elucidate the heritability of each MF. Furthermore, records on BW and ADG from 154 to 294 d of age (ADG154–294), 294 to 336 d of age (ADG294–336), and 154 to 336 d of age (ADG154–336) were used in a series of bivariate analyses to establish the genetic and phenotypic correlations with MFs. Bivariate analyses were only performed for MFs that had a heritability significantly different from zero. The heritabilities obtained in the univariate analyses for the MFs in the binned data set were low (<0.2). In contrast, in the aligned data set, we obtained moderate heritability (0.2 to 0.5) for 3.5% of MFs and high heritability (more than 0.5) for 1% of MFs. The bivariate analyses showed that ~12%, ~3%, ~9%, and ~9% of MFs had significant additive genetic correlations with BW, ADG154–294, ADG294–336, and ADG154–336, respectively. In all of the bivariate analyses, the percentage of significant additive genetic correlations was higher than the percentage of significant phenotypic correlations of the corresponding trait. Our results provided insights into the influence of the underlying genetic mechanisms on MFs. Further investigations in this field are needed for better understanding of the genetic relationship among the MFs and quantitative traits.

scholarly journals Mapping QTL for Grain Yield under Moisture Stress Environments in Rice (Oryza sativa L.)

2011 ◽  
Vol 3 (4) ◽  
pp. 129-133
Author(s):  
Supriyo CHAKRABORTY ◽  
Sheng-Chu WANG ◽  
Zhao-Bang ZENG
Keyword(s):  
Grain Yield ◽  
Genetic Variance ◽  
Oryza Sativa L ◽  
Moisture Stress ◽  
Interval Mapping ◽  
Chromosome 1 ◽  
Phenotypic Variance ◽  
Common Qtl ◽  
Rice Chromosomes ◽  
Stress Environments

Polygenes (QTLs) for grain yield were mapped on rice chromosomes under two moisture stress environments by multiple interval mapping (MIM) method in a double haploid (DH) population derived from a cross between a deep-rooted japonica and a shallow-rooted indica genotype. In environment 1 (E1), the MIM detected a total of six QTLs for grain yield on chromosomes-two QTLs on chromosome 1 and four QTLs on chromosome 5 along with one additive x additive epistasis. But in environment 2 (E2), the MIM detected five QTLs for grain yield on two chromosomes-three QTLs on chromosome 1 and two QTLs on chromosome 7. One common QTL on chromosome 1 flanked by the markers RG109-ME1014 was detected in both the environments, although the other detected QTLs differed between environments. The magnitude of QTL effect, percent genetic variance and percent phenotypic variance explained by each QTL was also estimated in both environments. The common QTL explained about 26.05 and 13.93% of genetic variance in E1 and E2, respectively. Estimated broad sense heritability for grain yield was 48.01 in E1 and 25.27% in E2.

scholarly journals Evaluating epistatic interaction signals in complex traits using quantitative traits

2009 ◽  
Vol 3 (S7) ◽  
Author(s):  
Odity Mukherjee ◽  
Krishna Rao Sanapala ◽  
Padmanabhan Anbazhagana ◽  
Saurabh Ghosh
Keyword(s):  
Complex Traits ◽  
Epistatic Interaction ◽  
Quantitative Traits

scholarly journals PSVIII-26 Gene editing of complex traits

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.

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

2019 ◽  
Vol 36 (12) ◽  
pp. 2890-2905 ◽  
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.

scholarly journals Explaining the heritability of an ecologically significant trait in terms of individual quantitative trait loci

2011 ◽  
Vol 7 (6) ◽  
pp. 896-898 ◽  
Author(s):  
Alison G. Scoville ◽  
Young Wha Lee ◽  
John H. Willis ◽  
John K. Kelly
Keyword(s):  
Qtl Mapping ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Genetic Variance ◽  
Balancing Selection ◽  
Natural Populations ◽  
Population Based ◽  
Population Variation ◽  
Trait Locus ◽  
Genomic Regions

Most natural populations display substantial genetic variation in behaviour, morphology, physiology, life history and the susceptibility to disease. A major challenge is to determine the contributions of individual loci to variation in complex traits. Quantitative trait locus (QTL) mapping has identified genomic regions affecting ecologically significant traits of many species. In nearly all cases, however, the importance of these QTLs to population variation remains unclear. In this paper, we apply a novel experimental method to parse the genetic variance of floral traits of the annual plant Mimulus guttatus into contributions of individual QTLs. We first use QTL-mapping to identify nine loci and then conduct a population-based breeding experiment to estimate V Q , the genetic variance attributable to each QTL. We find that three QTLs with moderate effects explain up to one-third of the genetic variance in the natural population. Variation at these loci is probably maintained by some form of balancing selection. Notably, the largest effect QTLs were relatively minor in their contribution to heritability.

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