Genetic basis of phenotypic convergence in pig terminal sires using pathway-based selection signature detection methods.

The primary purpose of genetic improvement in lean pig breeds is to enhance production performance. Owing to their similar breeding directions, Duroc and Pietrain pigs are ideal models for investigating the phenotypic convergence underlying artificial selection. However, most important economic traits are controlled by a polygenic basis, so traditional strategies for detecting selection signatures may not fully reveal the genetic basis of complex traits. The pathway-based gene network analysis method utilizes each pathway as a unit, overcoming the limitations of traditional strategies for detecting selection signatures by revealing the selection of complex biological processes. Here, we utilized 13 122 398 high-quality SNPs from whole-genome sequencing data of 48 Pietrain pigs, 156 Duroc pigs and 36 European wild boars to detect selective signatures. After calculating FST and iHS scores, we integrated the pathway information and utilized the r/bioconductor graphite and signet packages to construct gene networks, identify subnets and uncover candidate genes underlying selection. Using the traditional strategy, a total of 47 genomic regions exhibiting parallel selection were identified. The enriched genes, including INO80, FZR1, LEPR and FAF1, may be associated with reproduction, fat deposition and skeletal development. Using the pathway-based selection signatures detection method, we identified two significant biological pathways and eight potential candidate genes underlying parallel selection, such as VTN, FN1 and ITGAV. This study presents a novel strategy for investigating the genetic basis of complex traits and elucidating the phenotypic convergence underlying artificial selection, by integrating traditional selection signature methods with pathway-based gene network analysis.

A Novel Approach Utilizing Domain Adversarial Neural Networks for the Detection and Classification of Selective Sweeps.

The identification and classification of selective sweeps are of great significance for improving the understanding of biological evolution and exploring opportunities for precision medicine and genetic improvement. Here, a domain adaptation sweep detection and classification (DASDC) method is presented to balance the alignment of two domains and the classification performance through a domain-adversarial neural network and its adversarial learning modules. DASDC effectively addresses the issue of mismatch between training data and real genomic data in deep learning models, leading to a significant improvement in its generalization capability, prediction robustness, and accuracy. The DASDC method demonstrates improved identification performance compared to existing methods and excels in classification performance, particularly in scenarios where there is a mismatch between application data and training data. The successful implementation of DASDC in real data of three distinct species highlights its potential as a useful tool for identifying crucial functional genes and investigating adaptive evolutionary mechanisms, particularly with the increasing availability of genomic data.

AGIDB: a versatile database for genotype imputation and variant decoding across species.

The high cost of large-scale, high-coverage whole-genome sequencing has limited its application in genomics and genetics research. The common approach has been to impute whole-genome sequence variants obtained from a few individuals for a larger population of interest individually genotyped using SNP chip. An alternative involves low-coverage whole-genome sequencing (lcWGS) of all individuals in the larger population, followed by imputation to sequence resolution. To overcome limitations of processing lcWGS data and meeting specific genotype imputation requirements, we developed AGIDB (https://agidb.pro), a website comprising tools and database with an unprecedented sample size and comprehensive variant decoding for animals. AGIDB integrates whole-genome sequencing and chip data from 17 360 and 174 945 individuals, respectively, across 89 species to identify over one billion variants, totaling a massive 688.57 TB of processed data. AGIDB focuses on integrating multiple genotype imputation scenarios. It also provides user-friendly searching and data analysis modules that enable comprehensive annotation of genetic variants for specific populations. To meet a wide range of research requirements, AGIDB offers downloadable reference panels for each species in addition to its extensive dataset, variant decoding and utility tools. We hope that AGIDB will become a key foundational resource in genetics and breeding, providing robust support to researchers.

Tissue-specific regulatory mechanism of LncRNAs and methylation in sheep adipose and muscle induced by Allium mongolicum Regel extracts.

Allium mongolicum Regel (A. mongolicum) is a perennial and xerophytic Liliaceous allium plant in high altitude desert steppe and desert areas. Feeding A. mongolicum greatly reduced unpleasant mutton flavor and improves meat quality of sheep. We analyzed epigenetic regulatory mechanisms of water extracts of A. mongolicum (WEA) on sheep muscle and adipose using RNA-Seq and whole-genome Bisulfite sequencing. Feeding WEA reduced differentially expressed genes and long non-coding RNAs (lncRNAs) between two tissues but increased differentially methylation regions (DMRs). LncRNA and DMR targets were both involved in ATP binding, ubiquitin, protein kinase binding, regulation of cell proliferation, and related signaling pathways, but not unsaturated fatty acids metabolism. Besides, tissue specific targets were involved in distinct functional annotations, e.g., Golgi membrane and endoplasmic reticulum for muscle lncRNA, oxidative phosphorylation metabolism for adipose lncRNA, dsRNA binding for muscle DMRs. Epigenetic regulatory networks were also discovered to discovered essential co-regulated modules, e.g., co-regulated insulin secretion module (PDPK1, ATP1A2, CACNA1S and CAMK2D) in adipose. The results indicated that WEA induced distinct epigenetic regulation on muscle and adipose to diminish transcriptome differences between tissues, which highlights biological functions of A. mongolicum, tissue similarity and specificity, as well as regulatory mechanism of mutton odor.