Multi-dimensional annotation of porcine variants using genomic and epigenomic features in pigs.

Background: Investigating the functional impact of genomic variants is essential to uncover the molecular mechanisms behind complex traits. This study compiled a comprehensive dataset of 1,817 whole-genome sequences from diverse pig breeds and populations, capturing the global pig genetic diversity.

Results: Our analyses first revealed 27,167 loss-of-function variants (LoFs), the majority of which also influenced gene expression and splicing, and enriched in genomic regions associated with complex traits in pigs.

Integrating large-scale meta-GWAS and PigGTEx resources to decipher the genetic basis of 232 complex traits in pigs.

Understanding the molecular and cellular mechanisms underlying complex traits in pigs is crucial for enhancing genetic gain via artificial selection and utilizing pigs as models for human disease and biology. Here, we conducted comprehensive genome-wide association studies (GWAS) followed by a cross-breed meta-analysis for 232 complex traits and a within-breed meta-analysis for 12 traits, using 28.3 million imputed sequence variants in 70 328 animals across 14 pig breeds.

Genetic regulation of gene expression across multiple tissues in chickens.

The chicken is a valuable model for understanding fundamental biology and vertebrate evolution and is a major global source of nutrient-dense and lean protein. Despite being the first non-mammalian amniote to have its genome sequenced, a systematic characterization of functional variation on the chicken genome remains lacking. Here, we integrated bulk RNA sequencing (RNA-seq) data from 7,015 samples, single-cell RNA-seq data from 127,598 cells and 2,869 whole-genome sequences to present a pilot atlas of regulatory variants across 28 chicken tissues.

The Farm Animal Genotype-Tissue Expression (FarmGTEx) Project.

Genetic mutation and drift, coupled with natural and human-mediated selection and migration, have produced a wide variety of genotypes and phenotypes in farmed animals. We here introduce the Farm Animal Genotype-Tissue Expression (FarmGTEx) Project, which aims to elucidate the genetic determinants of gene expression across 16 terrestrial and aquatic domestic species under diverse biological and environmental contexts. For each species, we aim to collect multiomics data, particularly genomics and transcriptomics, from 50 tissues of 1,000 healthy adults and 200 additional animals representing a specific context.

Comparative analysis of genotype imputation strategies for SNPs calling from RNA-seq.

Background: RNA sequencing (RNA-seq) is a powerful tool for transcriptome profiling, enabling integrative studies of expression quantitative trait loci (eQTL). As it identifies fewer genetic variants than DNA sequencing (DNA-seq), reference panel-based genotype imputation is often required to enhance its utility.

Results: This study evaluated the accuracy of genotype imputation using SNPs called from RNA-seq data (RNA-SNPs).

FarmGTEx TWAS-server: An Interactive Web Server for Customized TWAS Analysis.

Transcriptome-wide association study (TWAS) is a powerful approach for investigating the molecular mechanisms linking genetic loci to complex phenotypes. However, the complexity of the TWAS analytical pipeline, including the construction of gene expression reference panels, gene expression prediction, and association analysis using data from genome-wide association studies (GWASs), poses challenges for genetic studies in many species. In this study, we provide the Farm Animal Genotype-Tissue Expression (FarmGTEx) TWAS-server, an interactive and user-friendly multispecies platform designed to streamline the translation of genetic findings across tissues and species.

Deciphering genetic characteristics of South China and North China indigenous pigs through selection signatures.

Background: Indigenous pig breeds in China have accumulated significant genetic diversity due to regional selection pressures. Investigating the selection signatures of these populations helps to understand their adaptive evolution and contributes to genetic improvement programs.

Results: We collected whole-genome sequencing data from 133 individuals, including South China and North China indigenous pigs and Asian wild boars.

Integrating Multiple Database Resources to Elucidate the Gene Flow in Southeast Asian Pig Populations.

The domestic pig () and its subfamilies have experienced long-term and extensive gene flow, particularly in Southeast Asia. Here, we analyzed 236 pigs, focusing on Yunnan indigenous, European commercial, East Asian, and Southeast Asian breeds, using the Pig Genomics Reference Panel (PGRP v1) of Pig Genotype-Tissue Expression (PigGTEx) to investigate gene flow and associated complex traits by integrating multiple database resources. In this study, we discovered evidence of admixtures from European pigs into the genome of Yunnan indigenous pigs.

Mapping and functional characterization of structural variation in 1060 pig genomes.

Background: Structural variations (SVs) have significant impacts on complex phenotypes by rearranging large amounts of DNA sequence.

Results: We present a comprehensive SV catalog based on the whole-genome sequence of 1060 pigs (Sus scrofa) representing 101 breeds, covering 9.6% of the pig genome.

Evaluating three strategies of genome-wide association analysis for integrating data from multiple populations.

In livestock, genome-wide association studies (GWAS) are usually conducted in a single population (single-GWAS) with limited sample size and detection power. To enhance the detection power of GWAS, meta-analysis of GWAS (meta-GWAS) and mega-analysis of GWAS (mega-GWAS) have been proposed to integrate data from multiple populations at the level of summary statistics or individual data, respectively. However, there is a lack of comparison for these different strategies, which makes it difficult to guide the best practice of GWAS integrating data from multiple study populations.

PigBiobank: a valuable resource for understanding genetic and biological mechanisms of diverse complex traits in pigs.

To fully unlock the potential of pigs as both agricultural species for animal-based protein food and biomedical models for human biology and disease, a comprehensive understanding of molecular and cellular mechanisms underlying various complex phenotypes in pigs and how the findings can be translated to other species, especially humans, are urgently needed. Here, within the Farm animal Genotype-Tissue Expression (FarmGTEx) project, we build the PigBiobank (http://pigbiobank.farmgtex.

Evaluation of six machine learning classification algorithms in pig breed identification using SNPs array data.

Breed identification utilizing multiple information sources and methods is widely applicated in the field of animal genetics and breeding. Simultaneously, with the development of artificial intelligence, the integration of high-throughput genomic data and machine learning techniques is increasingly used for breed identification. In this context, we used 654 individuals from 15 pig breeds, evaluating the performance of machine learning and stacking ensemble learning classifiers, as well as the function of feature selection and anomaly detection in different scenarios.

Meta-analysis of genome-wide association studies uncovers shared candidate genes across breeds for pig fatness trait.

Background: Average backfat thickness (BFT) is a critical complex trait in pig and an important indicator for fat deposition and lean rate. Usually, genome-wide association study (GWAS) was used to discover quantitative trait loci (QTLs) of BFT in a single population. However, the power of GWAS is limited by sample size in a single population.

Model Comparison of Heritability Enrichment Analysis in Livestock Population.

Heritability enrichment analysis is an important means of exploring the genetic architecture of complex traits in human genetics. Heritability enrichment is typically defined as the proportion of an SNP subset explained heritability, divided by the proportion of SNPs. Heritability enrichment enables better study of underlying complex traits, such as functional variant/gene subsets, biological networks and metabolic pathways detected through integrating explosively increased omics data.

Insights into the architecture of human-induced polygenic selection in Duroc pigs.

Background: As one of the most utilized commercial composite boar lines, Duroc pigs have been introduced to China and undergone strongly human-induced selection over the past decades. However, the efficiencies and limitations of previous breeding of Chinese Duroc pigs are largely understudied. The objective of this study was to uncover directional polygenic selection in the Duroc pig genome, and investigate points overlooked in the past breeding process.

Impact of linkage disequilibrium heterogeneity along the genome on genomic prediction and heritability estimation.

Background: Compared to medium-density single nucleotide polymorphism (SNP) data, high-density SNP data contain abundant genetic variants and provide more information for the genetic evaluation of livestock, but it has been shown that they do not confer any advantage for genomic prediction and heritability estimation. One possible reason is the uneven distribution of the linkage disequilibrium (LD) along the genome, i.e.

Utilization Strategies of Two Environment Phenotypes in Genomic Prediction.

Multiple environment phenotypes may be utilized to implement genomic prediction in plant breeding, while it is unclear about optimal utilization strategies according to its different availability. It is necessary to assess the utilization strategies of genomic prediction models based on different availability of multiple environment phenotypes. Here, we compared the prediction accuracy of three genomic prediction models (genomic prediction model (genomic best linear unbiased prediction (GBLUP), genomic best linear unbiased prediction (GFBLUP), and multi-trait genomic best linear unbiased prediction (mtGBLUP)) which leveraged diverse information from multiple environment phenotypes using a rice dataset containing 19 agronomic traits in two disparate seasons.

Impact of Marker Pruning Strategies Based on Different Measurements of Marker Distance on Genomic Prediction in Dairy Cattle.

With the availability of high-density single-nucleotide polymorphism (SNP) data and the development of genotype imputation methods, high-density panel-based genomic prediction (GP) has become possible in livestock breeding. It is generally considered that the genomic estimated breeding value (GEBV) accuracy increases with the marker density, while studies have shown that the GEBV accuracy does not increase or even decrease when high-density panels were used. Therefore, in addition to the SNP number, other measurements of 'marker density' seem to have impacts on the GEBV accuracy, and exploring the relationship between the GEBV accuracy and the measurements of 'marker density' based on high-density SNP or whole-genome sequence data is important for the field of GP.

[Moxibustion therapy in prevention and treatment of coronavirus disease 2019 (COVID-19): construction and application of non-contact diagnosis and treatment mode].

Objective: To establish and promote the non-contact doctor-patient interactive diagnosis and treatment mode based on mobile internet for the treatment of coronavirus disease 2019 (COVID-19) with moxibustion therapy, and to observe the feasibility and effectiveness of the model in the pandemic.

Methods: A total of 43 first-line medical staff and 149 suspected and confirmed cases with COVID-19 [18 cases in medical observation period, 17 cases of mild type (cold dampness and stagnation in the lung), 24 cases of ordinary type (cold-dampness accumulated in the lung) and 90 cases in recovery period ( deficiency of spleen and lung)] were included. A non-contact doctor-patient interactive diagnosis and treatment platform was established for the treatment of COVID-19 with indirect moxibustion plaster based on mobile internet.

Optimizing genomic prediction model given causal genes in a dairy cattle population.

As genotypic data are moving from SNP chip toward whole-genome sequence, the accuracy of genomic prediction (GP) exhibits a marginal gain, although all genetic variation, including causal genes, are contained in whole-genome sequence data. Meanwhile, genetic analyses on complex traits, such as genome-wide association studies, have identified an increasing number of genomic regions, including potential causal genes, which would be reliable prior knowledge for GP. Many studies have tried to improve the performance of GP by modifying the prediction model to incorporate prior knowledge.

New Insights From Imputed Whole-Genome Sequence-Based Genome-Wide Association Analysis and Transcriptome Analysis: The Genetic Mechanisms Underlying Residual Feed Intake in Chickens.

Poultry feed constitutes the largest cost in poultry production, estimated to be up to 70% of the total cost. Moreover, there is pressure on the poultry industry to increase production to meet the protein demand of humans and simultaneously reduce emissions to protect the environment. Therefore, improving feed efficiency plays an important role to improve profits and the environmental footprint in broiler production.