Whole genome variants across 57 pig breeds enable comprehensive identification of genetic signatures that underlie breed features

Background:A large number of pig breeds are distributed around the world,their features and characteristics vary among breeds,and they are valuable resources.Understanding the underlying genetic mechanisms that explain across-breed variation can help breeders develop improved pig breeds.Results:In this study,we performed GWAS using a standard mixed linear model with three types of genome variants(SNP,InDel,and CNV)that were identified from public,whole-genome,sequencing data sets.We used 469 pigs of 57 breeds,and we identified and analyzed approximately 19 million SNPs,1.8 million InDels,and 18,016 CNVs.We defined six biological phenotypes by the characteristics of breed features to identify the associated genome variants and candidate genes,which included coat color,ear shape,gradient zone,body weight,body length,and body height.A total of 37 candidate genes was identified,which included 27 that were reported previously(e.g.,PLAG1 for body weight),but the other 10 were newly detected candidate genes(e.g.,ADAMTS9 for coat color).Conclusion:Our study indicated that using GWAS across a modest number of breeds with high density genome variants provided efficient mapping of complex traits.

中国手机出口的时空格局演变及其影响因素

近年来,中国在全球手机生产网络中的地位不断上升,逐渐成为全球第一大手机出口国。同时,随着我国国内手机市场渐趋饱和,越来越多的中国手机生产商开始注重海外市场的开发。本文通过贸易流、贸易强度指数、面板数据模型等方法,分析了2007年以来我国手机出口时空格局变化及其影响因素。研究发现(:1)2007年以来,中国手机出口经历缓慢增长、快速增长、波动增长三个阶段,全球占有率不断提升,但受贸易保护主义和新冠疫情的冲击,2018年以来我国手机出口规模出现一定程度的缩水。(2)中国手机出口贸易网络密度不断增强,整体呈现以东亚、美国、欧盟为核心的三极格局,但手机出口贸易网络的集中度较高,存在安全隐患。(3)中国手机出口贸易网络的核心节点与贸易强度较大地区存在空间错位,欧盟是最大市场但贸易强度指数不高,非洲市场份额很小但贸易强度指数极高,东盟是我国手机出口主要市场、也是出口依赖度较高的地区。(4)影响我国手机出口格局的主要因素是供给侧的制造业劳动力价格和R&D经费支出,其次是需求侧的制度质量、市场规模等,而流通方面的影响较小。

An integrative profiling of metabolome and transcriptome in the plasma and skeletal muscle following an exercise intervention in diet-induced obese mice

Exercise intervention at the early stage of type 2 diabetes mellitus(T2DM)can aid in the maintenance of blood glucose homeostasis and prevent the development of macrovascular and microvascular complications.However,the exercise-regulated pathways that prevent the development of T2DM remain largely unclear.In this study,two forms of exercise intervention,treadmill training and voluntary wheel running,were conducted for high-fat diet(HFD)-induced obese mice.We observed that both forms of exercise intervention alleviated HFD-induced insulin resistance and glucose intolerance.Skeletal muscle is recognized as the primary site for postprandial glucose uptake and for responsive alteration beyond exercise training.Metabolomic profiling of the plasma and skeletal muscle in Chow,HFD,and HFD-exercise groups revealed robust alterations in metabolic pathways by exercise intervention in both cases.Overlapping analysis identified nine metabolites,including beta-alanine,leucine,valine,and tryptophan,which were reversed by exercise treatment in both the plasma and skeletal muscle.Transcriptomic analysis of gene expression profiles in the skeletal muscle revealed several key pathways involved in the beneficial effects of exercise on metabolic homeostasis.In addition,integrative transcriptomic and metabolomic analyses uncovered strong correlations between the concentrations of bioactive metabolites and the expression levels of genes involved in energy metabolism,insulin sensitivity,and immune response in the skeletal muscle.This work established two models of exercise intervention in obese mice and provided mechanistic insights into the beneficial effects of exercise intervention on systemic energy homeostasis.

rMVP: A Memory-efficient, Visualization-enhanced, and Parallel-accelerated Tool for Genome-wide Association Study

Along with the develoipment of high-throughput sequencing technologies,both sample size and SNP number are increasing rapidly in genome-wide association studies(GWAS),and the associated computation is more challenging than ever.Here,we present a memory-efficient,visualization-enhanced,and parallel-accelerated R package called“r MVP”to address the need for improved GWAS computation.r MVP can 1)effectively process large GWAS data,2)rapidly evaluate population structure,3)efficiently estimate variance components by Efficient Mixed-Model Association e Xpedited(EMMAX),Factored Spectrally Transformed Linear Mixed Models(Fa ST-LMM),and Haseman-Elston(HE)regression algorithms,4)implement parallel-accelerated association tests of markers using general linear model(GLM),mixed linear model(MLM),and fixed and random model circulating probability unification(Farm CPU)methods,5)compute fast with a globally efficient design in the GWAS processes,and 6)generate various visualizations of GWASrelated information.Accelerated by block matrix multiplication strategy and multiple threads,the association test methods embedded in r MVP are significantly faster than PLINK,GEMMA,and Farm CPU_pkg.r MVP is freely available at https://github.com/xiaolei-lab/r MVP.