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A Modified Memetic Algorithm with an Application to Gene Selection in a Sheep Body Weight Study.

ABSTRACT: Selecting the minimal best subset out of a huge number of factors for influencing the response is a fundamental and very challenging NP-hard problem because the presence of many redundant genes results in over-fitting easily while missing an important gene can more detrimental impact on predictions, and computation is prohibitive for exhaust search. We propose a modified memetic algorithm (MA) based on an improved splicing method to overcome the problems in the traditional genetic algorithm exploitation capability and dimension reduction in the predictor variables. The new algorithm accelerates the search in identifying the minimal best subset of genes by incorporating it into the new local search operator and hence improving the splicing method. The improvement is also due to another two novel aspects: (a) updating subsets of genes iteratively until the no more reduction in the loss function by splicing and increasing the probability of selecting the true subsets of genes; and (b) introducing add and del operators based on backward sacrifice into the splicing method to limit the size of gene subsets. Additionally, according to the experimental results, our proposed optimizer can obtain a better minimal subset of genes with a few iterations, compared with all considered algorithms. Moreover, the mutation operator is replaced by it to enhance exploitation capability and initial individuals are improved by it to enhance efficiency of search. A dataset of the body weight of Hu sheep was used to evaluate the superiority of the modified MA against the genetic algorithm. According to our experimental results, our proposed optimizer can obtain a better minimal subset of genes with a few iterations, compared with all considered algorithms including the most advanced adaptive best-subset selection algorithm.

SUBMITTER: Miao M

PROVIDER: S-EPMC8772977 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Understanding how polygenic traits evolve and respond to selection is a major unsolved problem, because challenges exist for identifying genes underlying a complex trait and understanding how multi-locus selection operates in the genome. Here we used artificial selection experiments to study polygenic response to selection. Inbred strains from seven independent long-term selection experiments in mice for extreme bodyweight (M-bM-^@M-^\HighM-bM-^@M-^] lines weigh 77-42g vs. 40-16g in M-bM-^@M-^\ControlsM-bM-^@M-^] lines), were genotyped at 527,572 SNPs to identify genetic variants controlling bodyweight. We identified 67 high-resolution parallel selected regions (PSRs) where multiple High lines share variants rarely found among the Controls. By comparing allele frequencies in one selection experiment against its unselected control, we found classical selective sweep signatures centered on the PSRs. Multiple lines of evidence support two G protein-coupled receptors GPR133 and Prlhr, as positional candidate genes controlling bodyweight. Artificial selection may mimic natural selection in the wild: compared to control loci, we detected reduced heterozygosity in PSRs in wild populations of unusually large mice on islands. Many PSRs overlap loci associated with human height variation, possibly through evolutionary conservation of functional pathways. Our data suggest that parallel selection on complex traits may evoke parallel responses at many genes involved in diverse but relevant pathways. These samples were used to test the enrichment of certain gene functional categories. Mouse tissue samples (muscle and liver) were collected from 44 and 45 days old males from each of the nine out of thirteen lines (5 High lines: BEHi, DUHi, EDHi, MUHi, ROHi and 4 Control lines: BELi, EDLi, MULi and ROLi; n=23 total) at the University of Edinburgh. Frozen tissue samples were stored at -80M-BM-:C until use. Tissues were dissociated using TissueLyser II with steel beads (both Qiagen) following manufacturersM-bM-^@M-^Y instructions. mRNA was extracted and purified using TRIzol Reagent and the PureLink RNA Mini Kit (Life Technologies GmbH, Darmstadt, Germany). cDNA was synthesized using Maxima First Strand cDNA synthesis kit (Thermo Fisher Scientific, St. Leon-Rot, Germany). Muscle and liver cDNA samples were hybridized on a Mouse GE 4x44k Microarray (Agilent Technologies, Inc., Waldbronn, Germany) according to manufacturerM-bM-^@M-^Ys recommendations. Three technical replicate each was done on pooled cDNA samples from each mouse line.

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A Modified Memetic Algorithm with an Application to Gene Selection in a Sheep Body Weight Study.

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