A new approach to detect epistasis utilizing parallel implementation of ant colony optimization by MapReduce framework

Genome-wide association studies (GWAS) involve the detection and interpretation of epistasis, which is responsible for the ‘missing heritability’ and influences common complex disease susceptibility. Many epistasis detection algorithms cannot be directly applied into GWAS as many combinations of genetic components are present in only a small amount of samples or even none at all. For a huge number of single nucleotide polymorphisms and inappropriate statistical tests, epistasis detection remains a computational and statistical challenge in genetic epidemiology. Here, we develop a novel method to identify epistatic interactions related to disease susceptibility utilizing an ant colony optimization strategy implemented by Google's MapReduce platform. We incorporate expert knowledge used to guide ants to make the best choice in the search process into the pheromone updating rule. We conduct sufficient experiments using simulated and real genome-wide data sets and experimental results demonstrate excellent performance of our algorithm compared with its competitors.     

Gene–gene and gene–environment interactions in complex traits in yeast

One of the fundamental questions in biology is how the genotype regulates the phenotype. An increasing number of studies indicate that, in most cases, the effect of a genetic locus on the phenotype is context‐dependent, i.e. it is influenced by the genetic background and the environment in which the phenotype is measured. Still, the majority of the studies, in both model organisms and humans, that map the genetic regulation of phenotypic variation in complex traits primarily identify additive loci with independent effects. This does not reflect an absence of the contribution of genetic interactions to phenotypic variation, but instead is a consequence of the technical limitations in mapping gene–gene interactions (GGI) and gene–environment interactions (GEI). Yeast, with its detailed molecular understanding, diverse population genomics and ease of genetic manipulation, is a unique and powerful resource to study the contributions of GGI and GEI in the regulation of phenotypic variation. Here we review studies in yeast that have identified GGI and GEI that regulate phenotypic variation, and discuss the contribution of these findings in explaining missing heritability of complex traits, and how observations from these GGI and GEI studies enhance our understanding of the mechanisms underlying genetic robustness and adaptability that shape the architecture of the genotype–phenotype map.     

对遗传算法困难度测量的基因关联法的研究

针对基因关联测度法的本质及意义进行了详细的理论剖析和实证研究.首先分析了反映遗传算法基因关联程度的标准化的基因关联方差与基因关联相关系数,进一步归纳出两个基本定理,并给予了严格的数学证明.最后用一些初等函数及NNK-模型对这一方法进行了实证分析,实验结果表明,对于困难问题该方法能够给予准确的判别,而对于某些较为容易的问题可能产生误判.     

Machine Learning Enables Selection of Epistatic Enzyme Mutants for Stability Against Unfolding and Detrimental Aggregation

Machine learning (ML) has pervaded most areas of protein engineering, including stability and stereoselectivity. Using limonene epoxide hydrolase as the model enzyme and innov'SAR as the ML platform, comprising a digital signal process, we achieved high protein robustness that can resist unfolding with concomitant detrimental aggregation. Fourier transform (FT) allows us to take into account the order of the protein sequence and the nonlinear interactions between positions, and thus to grasp epistatic phenomena. The innov'SAR approach is interpolative, extrapolative and makes outside-the-box, predictions not found in other state-of-the-art ML or deep learning approaches. Equally significant is the finding that our approach to ML in the present context, flanked by advanced molecular dynamics simulations, uncovers the connection between epistatic mutational interactions and protein robustness.     

Genetic Analysis of Quantitative Signs of White Lupin (Lupinus albus L.) Genotypes

Genetic variability in the base population plays an important role in crop-breeding program. A comprehensive assessment on the quantitative signs of white lupin genotypes was performed to be included in the selection process. Two limits of the environment were applied, i.e., dense and sparse sowing. Positive true heterosis was established in PI533704×Zuter and PI533704×Lucky801 crosses by using the traits of the weight of nodules and fresh root mass weight at both limits of the environment. In more favorable growing conditions, the number and weight of nodules, as well as fresh aboveground mass weight had greater influence on the inheritance of epistatic gene interactions. In the case of dense sowing, the dominant gene actions for the weight of seeds were found more significant. With a high coefficient of inheritance in both environments, the hybrids of PI533704×Zuter and Lucky801×PI533704 were characterized by number and weight of nodules, Zuter×PI533704 by fresh root and aboveground mass weight, and almost all hybrids by seed weight per plant. The assessment of the initial material makes it highly likely to speed up the process of creating new varieties of white lupin.     

进化算法的困难性理论研究进展

进化算法困难性是进化计算研究领域的重要分支,旨在研究进化算法的性能表现与优化问题特性之间的联系,其目的是利用有限信息估计进化算法在求解优化问题时的性能表现.本文主要介绍进化算法困难性研究的几种典型方法及近年来的研究进展,主要包括适应值—距离模型、适应值曲面模型、曲面自动机模型、最优吸引子理论和基因关联模型等六种分析优化问题难度的理论,以及相应的八种难度指标.此外,本文还通过对比分析指出现有方法存在的优缺点,并展望了该领域未来的发展趋势.     

The statistical mechanics of a polygenic character under stabilizing selection,mutation and drift

By exploiting an analogy between population genetics and statistical mechanics, we study the evolution of a polygenic trait under stabilizing selection, mutation and genetic drift. This requires us to track only four macroscopic variables, instead of the distribution of all the allele frequencies that influence the trait. These macroscopic variables are the expectations of: the trait mean and its square, the genetic variance, and of a measure of heterozygosity, and are derived from a generating function that is in turn derived by maximizing an entropy measure. These four macroscopics are enough to accurately describe the dynamics of the trait mean and of its genetic variance (and in principle of any other quantity). Unlike previous approaches that were based on an infinite series of moments or cumulants, which had to be truncated arbitrarily, our calculations provide a well-defined approximation procedure. We apply the framework to abrupt and gradual changes in the optimum, as well as to changes in the strength of stabilizing selection. Our approximations are surprisingly accurate, even for systems with as few as five loci. We find that when the effects of drift are included, the expected genetic variance is hardly altered by directional selection, even though it fluctuates in any particular instance. We also find hysteresis, showing that even after averaging over the microscopic variables, the macroscopic trajectories retain a memory of the underlying genetic states.     

Short communication: evidence for a major gene by polygene interaction for milk production traits in German Holstein dairy cattle

The present study investigated putative interaction effects between the DGAT1 K232A mutation and the polygenic term (i.e., all genes except DGAT1) for 5 milk production traits in the German Holstein dairy cattle population. Mixed models were used, and the test for interaction relied on the comparison of polygenic variance components depending on the sire's genotypes at DGAT1 K232A. Substitution effects were highly significant for all traits. Significant interaction effects were found for milk fat and protein percentage.     

Testing candidate gene effects on milk production traits in dairy cattle under various parameterizations and modes of inheritance

The major objectives of this study were 1) to assess the statistical properties of models commonly used for the estimation of single nucleotide polymorphism (SNP) effects under the assumption of various modes of inheritance and various parameterizations of SNP genotypes using simulated data, and 2) to compare effects of the selected polymorphisms located within butyrophilin (BTN1A1), diacylglycerol acyltransferase 1 (DGAT1), leptin (LEP), and leptin receptor (LEPR) candidate genes on milk production traits using data from 2 dairy cattle breeds (190 Jersey cows and 475 Polish Holstein-Friesian cows). Simulation results showed that type I error and power were not dependent on the assumed parameterization, but differences were observed regarding confidence intervals of estimated SNP effects. In the presence of epistasis, correct confidence intervals for all (epistatic and nonepistatic) SNP and all modes of inheritance were provided only by the parameterization proposed by C. H. Kao and Z. B. Zeng in 2002. However, if no dominance effect was included in the model, confidence intervals for SNP effects were correct for all parameterizations. Results based on real data showed that for both breeds the additive effects of polymorphisms were generally similar, except for LEPR, which had a different allele associated with increased fat content in Holstein-Friesians than in Jerseys. In both breeds, DGAT1 had the largest additive effect of the polymorphisms considered, but its effect on most milk traits was more pronounced in Jerseys than in Holstein-Friesians. Evidence of epistasis was found between LEPR and DGAT1, as well as between LEPR and BTN1A1, but only for milk content traits and only in the Holstein-Friesian breed. There was also more evidence for dominance in the Holstein-Friesian breed than in the Jersey breed.     

Mapping Resistance to Argentinean Fusarium (Graminearum) Head Blight Isolates in Wheat

Fusarium head blight (FHB) of wheat, caused by Fusarium graminearum (Schwabe), is a destructive disease worldwide, reducing wheat yield and quality. To accelerate the improvement of scab tolerance in wheat, we assessed the International Triticeae Mapping Initiative mapping population (ITMI/MP) for Type I and II resistance against a wide population of Argentinean isolates of F. graminearum. We discovered a total of 27 additive QTLs on ten different (2A, 2D, 3B, 3D, 4B, 4D, 5A, 5B, 5D and 6D) wheat chromosomes for Type I and Type II resistances explaining a maximum of 15.99% variation. Another four and two QTLs for thousand kernel weight in control and for Type II resistance, respectively, involved five different chromosomes (1B, 2D, 6A, 6D and 7D). Furthermore, three, three and five QTLs for kernel weight per spike in control, for Type I resistance and for Type II resistance, correspondingly, involved ten chromosomes (2A, 2D, 3B, 4A, 5A, 5B, 6B, 7A, 7B, 7D). We were also able to detect five and two epistasis pairs of QTLs for Type I and Type II resistance, respectively, in addition to additive QTLs that evidenced that FHB resistance in wheat is controlled by a complex network of additive and epistasis QTLs.