Background Multibreed genomic prediction(MBGP)is crucial for improving prediction accuracy for breeds with small populations,for which limited data are often available.Recent studies have demonstrated that partitionin...Background Multibreed genomic prediction(MBGP)is crucial for improving prediction accuracy for breeds with small populations,for which limited data are often available.Recent studies have demonstrated that partitioning the genome into nonoverlapping blocks to model heterogeneous genetic(co)variance in multitrait models can achieve higher joint prediction accuracy.However,the block partitioning method,a key factor influencing model performance,has not been extensively explored.Results We introduce mbBayesABLD,a novel Bayesian MBGP model that partitions each chromosome into nonoverlapping blocks on the basis of linkage disequilibrium(LD)patterns.In this model,marker effects within each block are assumed to follow normal distributions with block-specific parameters.We employ simulated data as well as empirical datasets from pigs and beans to assess genomic prediction accuracy across different models using cross-validation.The results demonstrate that mbBayesABLD significantly outperforms conventional MBGP models,such as GBLUP and BayesR.For the meat marbling score trait in pigs,compared with GBLUP,which does not account for heterogeneous genetic(co)variance,mbBayesABLD improves the prediction accuracy for the small-population breed Landrace by 15.6%.Furthermore,our findings indicate that a moderate level of similarity in LD patterns between breeds(with an average correlation of 0.6)is sufficient to improve the prediction accuracy of the target breed.Conclusions This study presents a novel LD block-based approach for multibreed genomic prediction.Our work provides a practical tool for livestock breeding programs and offers new insights into leveraging genetic diversity across breeds for improved genomic prediction.展开更多
目的:对青少年行为评定量表第三版(Behavior Assessment System for Children,Third Edition,Adolescent,BASC-3-A)进行中文修订,检验其信、效度。方法:中文版BASC-3-A,包括自评(SRP)、家长评定(PRS)和教师评定(TRS)三个版本。采用分层...目的:对青少年行为评定量表第三版(Behavior Assessment System for Children,Third Edition,Adolescent,BASC-3-A)进行中文修订,检验其信、效度。方法:中文版BASC-3-A,包括自评(SRP)、家长评定(PRS)和教师评定(TRS)三个版本。采用分层抽样法,抽取来自华北、东北等7大地理区域覆盖25个省份的青少年600人,并匹配家长及教师样本。以适应性行为评定量表第二版(ABAS-Ⅱ)作为PRS和TRS的效标,儿童青少年社会适应问卷作为SRP的效标。结果:(1)各版本Cronbach'sα系数(0.84~0.97)均符合测量学要求;(2)探索性结构方程模型结果支持PRS、TRS与SRP的原理论模型,拟合情况均良好;(3)PRS、TRS和SRP中的适应技能维度分别与ABAS-II和儿童青少年社会适应问卷得分呈显著正相关,而临床维度则呈显著负相关。结论:中文版青少年行为评定量表第三版具有良好的信、效度,可用于我国青少年行为状态的评定。展开更多
Different psychiatric disorders share genetic relationships and pleiotropic loci to certain extent.We integrated and analyzed datasets related to major depressive disorder(MDD),bipolar disorder(BIP),and schizophrenia(...Different psychiatric disorders share genetic relationships and pleiotropic loci to certain extent.We integrated and analyzed datasets related to major depressive disorder(MDD),bipolar disorder(BIP),and schizophrenia(SCZ)from the Psychiatric Genomics Consortium using multitrait analysis of genome-wide association analysis(MTAG).MTAG significantly increased the effective sample size from 99,773 to 119,754 for MDD,from 909,061 to 1,450,972 for BIP,and from 856,677 to 940,613 for SCZ.We discovered 7,32,and 43 novel lead single nucleotide polymorphisms(SNPs)and 1,6,and 3 novel causal SNPs for MDD,BIP,and SCZ,respectively,after fine-mapping.We identified rs8039305 in the FURIN gene as a novel pleiotropic locus across the three disorders.We performed marker analysis of genomic annotation(MAGMA)and Hi-C-coupled MAGMA(H-MAGMA)based gene-set analysis and identified 101 genes associated with the three disorders,which were enriched in the regulation of postsynaptic membranes,postsynaptic membrane dopaminergic synapses,and Notch signaling pathway.Next,we performed Mendelian randomization analysis using different tools and detected a causal effect of BIP on SCZ.Overall,we demonstrated the usage of combined genome-wide association studies summary statistics for exploring potential novel mechanisms of the three psychiatric disorders,providing an alternative approach to integrate publicly available summary data.展开更多
基金supported by the Biological Breeding-Major Projects in National Science and Technology(No.2023ZD0404405)the Earmarked Fund for China Agriculture Research System(No.CARS-pig-35)+2 种基金the National Natural Science Foundation of China(No.3227284,32302708)the 2115 Talent Development Program of China Agricultural University,the Chinese Universities Scientific Fund(No.2023TC196)the Seed Industry Revitalization Action Project of Guangdong Province(No.2024-XPY-06-001)。
摘要Background Multibreed genomic prediction(MBGP)is crucial for improving prediction accuracy for breeds with small populations,for which limited data are often available.Recent studies have demonstrated that partitioning the genome into nonoverlapping blocks to model heterogeneous genetic(co)variance in multitrait models can achieve higher joint prediction accuracy.However,the block partitioning method,a key factor influencing model performance,has not been extensively explored.Results We introduce mbBayesABLD,a novel Bayesian MBGP model that partitions each chromosome into nonoverlapping blocks on the basis of linkage disequilibrium(LD)patterns.In this model,marker effects within each block are assumed to follow normal distributions with block-specific parameters.We employ simulated data as well as empirical datasets from pigs and beans to assess genomic prediction accuracy across different models using cross-validation.The results demonstrate that mbBayesABLD significantly outperforms conventional MBGP models,such as GBLUP and BayesR.For the meat marbling score trait in pigs,compared with GBLUP,which does not account for heterogeneous genetic(co)variance,mbBayesABLD improves the prediction accuracy for the small-population breed Landrace by 15.6%.Furthermore,our findings indicate that a moderate level of similarity in LD patterns between breeds(with an average correlation of 0.6)is sufficient to improve the prediction accuracy of the target breed.Conclusions This study presents a novel LD block-based approach for multibreed genomic prediction.Our work provides a practical tool for livestock breeding programs and offers new insights into leveraging genetic diversity across breeds for improved genomic prediction.
摘要目的:对青少年行为评定量表第三版(Behavior Assessment System for Children,Third Edition,Adolescent,BASC-3-A)进行中文修订,检验其信、效度。方法:中文版BASC-3-A,包括自评(SRP)、家长评定(PRS)和教师评定(TRS)三个版本。采用分层抽样法,抽取来自华北、东北等7大地理区域覆盖25个省份的青少年600人,并匹配家长及教师样本。以适应性行为评定量表第二版(ABAS-Ⅱ)作为PRS和TRS的效标,儿童青少年社会适应问卷作为SRP的效标。结果:(1)各版本Cronbach'sα系数(0.84~0.97)均符合测量学要求;(2)探索性结构方程模型结果支持PRS、TRS与SRP的原理论模型,拟合情况均良好;(3)PRS、TRS和SRP中的适应技能维度分别与ABAS-II和儿童青少年社会适应问卷得分呈显著正相关,而临床维度则呈显著负相关。结论:中文版青少年行为评定量表第三版具有良好的信、效度,可用于我国青少年行为状态的评定。
基金supported by the National Key Research and Development Program of China(2015AA020108)the National Natural Science Foundation of China(31671377,81671326)+3 种基金Shanghai Municipal Science and Technology Major Project(2017SHZDZX01)Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science(East China Normal University)of Ministry of Educationthe Fundamental Research Funds for the Central Universities,Beihang University&Capital Medical University Advanced Innovation Center for Big Data-Based Precision Medicine Plan(BHME-201804,BHME-201904)The Special Fund of the Pediatric Medical Coordinated Development Center of Beijing Hospitals。
摘要Different psychiatric disorders share genetic relationships and pleiotropic loci to certain extent.We integrated and analyzed datasets related to major depressive disorder(MDD),bipolar disorder(BIP),and schizophrenia(SCZ)from the Psychiatric Genomics Consortium using multitrait analysis of genome-wide association analysis(MTAG).MTAG significantly increased the effective sample size from 99,773 to 119,754 for MDD,from 909,061 to 1,450,972 for BIP,and from 856,677 to 940,613 for SCZ.We discovered 7,32,and 43 novel lead single nucleotide polymorphisms(SNPs)and 1,6,and 3 novel causal SNPs for MDD,BIP,and SCZ,respectively,after fine-mapping.We identified rs8039305 in the FURIN gene as a novel pleiotropic locus across the three disorders.We performed marker analysis of genomic annotation(MAGMA)and Hi-C-coupled MAGMA(H-MAGMA)based gene-set analysis and identified 101 genes associated with the three disorders,which were enriched in the regulation of postsynaptic membranes,postsynaptic membrane dopaminergic synapses,and Notch signaling pathway.Next,we performed Mendelian randomization analysis using different tools and detected a causal effect of BIP on SCZ.Overall,we demonstrated the usage of combined genome-wide association studies summary statistics for exploring potential novel mechanisms of the three psychiatric disorders,providing an alternative approach to integrate publicly available summary data.