Statistical analysis for genome-wide association study

In the past few years, genome-wide association study （GWAS） has made great successes in identifying genetic susceptibility loci underlying many complex diseases and traits. The findings provide important genetic insights into understanding pathogenesis of diseases. In this paper, we present an overview of widely used approaches and strategies for analysis of GWAS, offered a general consideration to deal with GWAS data. The issues regarding data quality control, population structure, association analysis, multiple comparison and visual presentation of GWAS results are discussed; other advanced topics including the issue of missing heritability, meta-analysis, setbased association analysis, copy number variation analysis and GWAS cohort analysis are also briefly introduced.

A compressed variance component mixed model for detecting QTNs and QTN-by-environment and QTN-by-QTN interactions in genome-wide association studies

Although genome-wide association studies are widely used to mine genes for quantitative traits,the effects to be estimated are confounded,and the methodologies for detecting interactions are imperfect.To address these issues,the mixed model proposed here first estimates the genotypic effects for AA,Aa,and aa,and the genotypic polygenic background replaces additive and dominance polygenic backgrounds.Then,the estimated genotypic effects are partitioned into additive and dominance effects using a one-way analysis of variance model.This strategy was further expanded to cover QTN-by-environment interactions(QEIs)and QTN-by-QTN interactions(QQIs)using the same mixed-model framework.Thus,a three-variance-component mixed model was integrated with our multi-locus random-SNP-effect mixed linear model(mrMLM)method to establish a new methodological framework,3VmrMLM,that detects all types of loci and estimates their effects.In Monte Carlo studies,3VmrMLM correctly detected all types of loci and almost unbiasedly estimated their effects,with high powers and accuracies and a low false positive rate.In re-analyses of 10 traits in 1439 rice hybrids,detection of 269 known genes,45 known gene-by-environment interactions,and 20 known gene-by-gene interactions strongly validated 3VmrMLM.Further analyses of known genes showed more small(67.49%),minor-allele-frequency(35.52%),and pleiotropic(30.54%)genes,with higher repeatability across datasets(54.36%)and more dominance loci.In addition,a heteroscedasticity mixed model in multiple environments and dimension reduction methods in quite a number of environments were developed to detect QEIs,and variable selection under a polygenic background was proposed for QQI detection.This study provides a new approach for revealing the genetic architecture of quantitative traits.

mrMLM v4.0.2: An R Platform for Multi-locus Genome-wide Association Studies

Previous studies have reported that some important loci are missed in single-locus genome-wide association studies(GWAS),especially because of the large phenotypic error in field experiments.To solve this issue,multi-locus GWAS methods have been recommended.However,only a few software packages for multi-locus GWAS are available.Therefore,we developed an R software named mr MLM v4.0.2.This software integrates mr MLM,FASTmr MLM,FASTmr EMMA,p LARm EB,p KWm EB,and ISIS EM-BLASSO methods developed by our lab.There are four components in mr MLM v4.0.2,including dataset input,parameter setting,software running,and result output.The fread function in data.table is used to quickly read datasets,especially big datasets,and the do Parallel package is used to conduct parallel computation using multiple CPUs.In addition,the graphical user interface software mr MLM.GUI v4.0.2,built upon Shiny,is also available.To confirm the correctness of the aforementioned programs,all the methods in mr MLM v4.0.2 and three widely-used methods were used to analyze real and simulated datasets.The results confirm the superior performance of mr MLM v4.0.2 to other methods currently available.False positive rates are effectively controlled,albeit with a less stringent significance threshold.mr MLM v4.0.2 is publicly available at Bio Code(https://bigd.big.ac.cn/biocode/tools/BT007077)or R(https://cran.r-project.org/web/packages/mr MLM.GUI/index.html)as an open-source software.

Genome-wide association study of the backfat thickness trait in two pig populations

Backfat thickness is a good predictor of carcass lean content,an economically important trait,and a main breeding target in pig improvement.In this study,the candidate genes and genomic regions associated with the tenth rib backfat thickness trait were identified in two independent pig populations,using a genome-wide association study of porcine 60K SNP genotype data applying the compressed mixed linear model(CMLM)statistical method.For each population,30 most significant single-nucleotide polymorphisms(SNPs)were selected and SNP annotation implemented using Sus scrofa Build 10.2.In the first population,25 significant SNPs were distributed on seven chromosomes,and SNPs on SSC1 and SSC7 showed great significance for fat deposition.The most significant SNP(ALGA0006623)was located on SSC1,upstream of the MC4R gene.In the second population,27 significant SNPs were recognized by annotation,and 12 SNPs on SSC12 were related to fat deposition.Two haplotype blocks,M1GA0016251-MARC0075799 and ALGA0065251-MARC0014203-M1GA0016298-ALGA0065308,were detected in significant regions where the PIPNC1 and GH1 genes were identified as contributing to fat metabolism.The results indicated that genetic mechanism regulating backfat thickness is complex,and that genome-wide associations can be affected by populations with different genetic backgrounds.

Human-like adrenal features in Chinese tree shrews revealed by multi-omics analysis of adrenal cell populations and steroid synthesis

The Chinese tree shrew(Tupaia belangeri chinensis)has emerged as a promising model for investigating adrenal steroid synthesis,but it is unclear whether the same cells produce steroid hormones and whether their production is regulated in the same way as in humans.Here,we comprehensively mapped the cell types and pathways of steroid metabolism in the adrenal gland of Chinese tree shrews using single-cell RNA sequencing,spatial transcriptome analysis,mass spectrometry,and immunohistochemistry.We compared the transcriptomes of various adrenal cell types across tree shrews,humans,macaques,and mice.Results showed that tree shrew adrenal glands expressed many of the same key enzymes for steroid synthesis as humans,including CYP11B2,CYP11B1,CYB5A,and CHGA.Biochemical analysis confirmed the production of aldosterone,cortisol,and dehydroepiandrosterone but not dehydroepiandrosterone sulfate in the tree shrew adrenal glands.Furthermore,genes in adrenal cell types in tree shrews were correlated with genetic risk factors for polycystic ovary syndrome,primary aldosteronism,hypertension,and related disorders in humans based on genome-wide association studies.Overall,this study suggests that the adrenal glands of Chinese tree shrews may consist of closely related cell populations with functional similarity to those of the human adrenal gland.Our comprehensive results(publicly available at http://gxmujyzmolab.cn:16245/scAGMap/)should facilitate the advancement of this animal model for the investigation of adrenal gland disorders.

BLUPmrMLM:A Fast mrMLM Algorithm in Genome-wide Association Studies

Multilocus genome-wide association study has become the state-of-the-art tool for dissecting the genetic architecture of complex and multiomic traits.However,most existing multilocus methods require relatively long computational time when analyzing large datasets.To address this issue,in this study,we proposed a fast mrMLM method,namely,best linear unbiased prediction multilocus random-SNP-effect mixed linear model(BLUPmrMLM).First,genome-wide single-marker scanning in mrMLM was replaced by vectorized Wald tests based on the best linear unbiased prediction(BLUP)values of marker effects and their variances in BLUPmrMLM.Then,adaptive best subset selection(ABESS)was used to identify potentially associated markers on each chromosome to reduce computational time when estimating marker effects via empirical Bayes.Finally,shared memory and parallel computing schemes were used to reduce the computational time.In simulation studies,BLUPmrMLM outperformed GEMMA,EMMAX,mrMLM,and FarmCPU as well as the control method(BLUPmrMLM with ABESS removed),in terms of computational time,power,accuracy for estimating quantitative trait nucleotide positions and effects,false positive rate,false discovery rate,false negative rate,and F1 score.In the reanalysis of two large rice datasets,BLUPmrMLM significantly reduced the computational time and identified more previously reported genes,compared with the aforementioned methods.This study provides an excellent multilocus model method for the analysis of large-scale and multiomic datasets.The software mrMLM v5.1 is available at BioCode(https://ngdc.cncb.ac.cn/biocode/tool/BT007388)or GitHub(https://github.com/YuanmingZhang65/mrMLM).

全基因组关联分析中混合模型的原理、优化与应用

全基因组关联分析(genome-wide association study,GWAS)是定位基因组中与性状显著关联的变异位点的有效方法。随着表型记录的完善、高通量基因型分型技术的发展,以及统计方法的改进,全基因组关联分析在人类疾病、动物植物遗传等领域得到了广泛的应用。假阳性是影响全基因组关联分析结果可靠性的重要因素之一。为了控制假阳性,除了校正P值,GWAS模型从最简单的方差分析(或用于质量性状的卡方检验)到加入固定效应协变量的普通线性模型(general linear model,GLM),再到加入随机效应的混合线性模型(mixed linear model,MLM)持续改进,控制了多种混杂因素导致的假阳性。将个体的遗传效应拟合为由基因组亲缘关系矩阵(genomic relationships matrix,GRM)定义的随机效应是目前常用的方法。由于MLM的参数估计大量消耗计算资源,研究人员不断尝试模型求解优化和GRM的构建优化(GRM的构建优化同时也提高了计算效率),最终将基于MLM计算的时间复杂度由O(MN3)逐步改进到O(MN),实现了计算速度与统计功效的飞跃。针对质量性状病例对照比失衡带来的假阳性问题,研究人员进一步对广义混合线性模型(generalized linear mixed model,GLMM)进行了校正。本文较全面地介绍了GWAS的基本原理和发展,着重阐述了GWAS中MLM模型的改进和优化细节,同时,列举了GWAS在农业中的应用,包括在植物、动物和微生物方面的研究成果,以及基于单倍型的GWAS应用。最后,从进一步提高GWAS统计功效和GWAS试验设计2个角度对GWAS未来的发展进行了展望。

植物关联分析方法的研究进展

关联分析在人类和动植物遗传研究中的应用日益广泛,新方法及其软件包不断涌现。为对其更好选择和应用,本文综述了关联分析的主要方法及其软件包。首先,介绍了群体结构对关联分析的影响;其次,重点介绍了单位点关联分析、多位点关联分析、上位性和多性状关联分析方法及其软件包;最后,展望了关联分析的发展动向。应当指出,基于群体结构和多基因整体背景控制的全基因组单标记快速扫描算法在目前的实际资料分析中应用较广泛,与其结果互补的是假阳性率较高的非参数方法。但是,今后的方法应当是以多位点模型、环境互作、上位性检验和多个相关性状联合分析为主。这为今后的理论与应用研究提供了有益信息。

多歧性状上位性关联分析的分层广义混合线性模型方法

[目的]作物抗性性状是重要的育种目标,通常表现为多个级别,称为多歧性状。但是,这类性状的上位性关联分析方法较少。因此,本研究针对品种资源群体探索了新的多歧性状上位性检测方法。[方法]提出了多歧性状分层广义混合线性模型的上位性关联分析方法。在遗传模型中,主效QTL、QTL间互作和QTL-环境互作为随机效应,群体平均数、群体结构和环境效应为固定效应。应用经验贝叶斯和最大似然方法分别估计这些效应。一系列Monte Carlo模拟试验和大豆耐盐碱性实际数据分析验证了新方法的有效性。[结果]新方法的统计功效高,效应估计值精度好;检测结果会受遗传率、表型分类数、表型分布、Founders数和Non-founders数的影响。用新方法分析了257份大豆品种幼苗根长耐盐碱性指数分级数据,检测到2个主效QTL、4个上位性QTL和3个QTL-环境互作,其中11个标记与其他方法或以前结果一致。[结论]本研究为植物上位性关联分析提供了新方法。

病例-对照设计的似然比罕见变异关联性检验的构建和模拟评价

目的:发展适用于病例-对照设计的似然比罕见变异关联性分析方法。方法:在logistic混合模型的框架下基于PQL算法建立伪数据,将二分类表型转化为连续型表型的关联性分析,然后借助线性混合模型的方差成分检验执行关联性分析。采用Monte Carlo模拟评价该方法的有效性,并与现有方法进行对比。结果:模拟显示,在不同情况下包括似然比检验在内的所有统计检验都能有效控制Ⅰ型错误;在效应方向相同情况下,Burden检验、SKAT-O和Mi ST的统计效能高;在效应方向不同的情况下似然比检验优于其他方法。结论:基于logistic混合模型和PQL算法的似然比检验可有效用于病例-对照设计的罕见变异关联性分析。

玉米出籽率全基因组关联分析

出籽率与玉米单穗产量密切相关,其遗传机制的解析对玉米高产育种具有重要意义。本研究利用309份玉米自交系为关联群体,利用固定和随机模型交替概率统一(FarmCPU)、压缩混合线性模型(CMLM)和多位点混合线性模型(MLMM)对2017年和2019年河南新乡原阳、周口郸城、海南三亚以及最佳线性无偏估计值(BLUE)的出籽率进行全基因组关联分析。共鉴定18个与出籽率显著关联的SNP(P<1.72E-05)。其中,FarmCPU、CMLM和MLMM方法分别检测到14个、5个和2个位点。S2_87292896利用CMLM和MLMM方法在BLUE环境和2019年原阳均检测到;在BLUE环境,S2_111319193利用FarmCPU和CMLM方法均检测到;在2017年郸城,S5_93814060利用CMLM和MLMM方法均检测到。5个位点即S1_304584425、S5_11751831、S5_93814060、S5_186385476和S8_94354503的表型变异解释率介于10.09%~15.43%之间,为出籽率的主效SNP。与前人研究结果比较发现,Bin1.08、Bin2.06、Bin4.09和Bin6.05可能是影响出籽率的重要区段。共挖掘32个候选基因,其中E3泛素蛋白连接酶UPL1、DEAD盒ATP依赖的RNA解旋酶RH52、蛋白激酶同源子4、SNARE互作蛋白KEULE和延伸因子EF1A等可能是影响出籽率的重要基因。

全基因组混合模型关联分析的极速回归扫描法研究

在全基因组混合模型关联分析(GWMMAS)中,利用剩余多基因遗传力代替方差比值(剩余多基因方差/误差方差),将多基因遗传力求解限制在(0,1)区间内。引入R语言Rcpp Armadillo程序包中的极速线性模型拟合函数(fast Lm Pure函数)快速估计单核苷酸多态性(SNP)效应和完整LMM最大似然值。从由GBLUP估计的性状基因组遗传力出发,逐个高通量SNP的GWMMAS约需4次全基因组回归扫描。当仅关注EMMAX法估计的大效应或高显著水准标记时,GWMMAS运行时间缩短在两次扫描之内。与采用lm函数优化剩余多基因方差比的Fa ST-LMM法相比,极速回归扫描法可成倍提高GWMMAS计算效率。计算机模拟试验证实新方法统计和计算效率,运用极速回归扫描法可高效定位与牙鲆生长性状相关基因位点。

多位点关联分析方法学的研究进展

多位点关联分析在人和动植物遗传研究中的应用日益广泛。本文综述了以混合线性模型(mixed linear model,MLM)为框架下多位点关联分析的主要方法及重要软件平台,包括全基因组关联分析(genome-wide association study,GWAS)混合线性模型方法学的建立与发展,多位点模型方法的发展,多位点GWAS混合线性模型方法的发展,以及GWAS方法学研究的影响因素,最后展望了关联分析的发展方向。

玉米盐胁迫相关性状全基因组关联分析及候选基因预测

盐胁迫是影响玉米产量的重要因素,为探究玉米盐胁迫响应的遗传基础,本研究以150份遗传背景丰富的玉米自交系为材料,结合34,342个多态性SNP标记,利用混合线性模型对玉米两个盐胁迫相关性状进行全基因组关联分析。关联分析结果表明:共鉴定8个独立位点与盐胁迫相关性状显著关联,其中3个位点与枯萎度显著关联,分布在4号和9号染色体上,5个SNP位点与株高变化率显著关联,分布在1、2、3和6号染色体上。结合盐胁迫下基因的表达量数据和功能注释,筛选到11个候选基因,利用qRT-PCR验证其中7个基因在盐胁迫下表达量显著上调。本研究结果为玉米耐盐机理的解析奠定了基础,为玉米耐盐种质的培育提供新的靶基因。

基于MLM混合线性模型的大豆全基因组关联分析可行性研究

为研究MLM混合线性模型在大豆全基因组关联分析中应用的可行性,以便精确寻找影响表型性状差异的主要基因标记位点范围,帮助检测分析个体在全基因组水平范围内遗传变异的多态性,采用混合线性模型,引入固定效应和随机效应来分解和计算基因组与多性状之间的相关性,对大豆全基因组数据与对应的表型性状进行关联分析。根据模型计算结果得出P值并展示有效基因位点图,不同性状与对应有效基因标记关联性显著突出。通过设定的模拟有效基因位点检验混合线性模型关联分析的遗传评估力,结果高出一般线形模型的效率,验证该方法在大豆全基因组关联分析上具有可行性,可为其它物种进行关联分析研究提供借鉴。

Heterogeneous genetic architecture by gender for precision medicine of cardiovascular disease

It is well-known that gender differences exist in the onset, progression, and prognosis of cardiovascular diseases （CVDs）, and that risk factors such as high blood pressure and lipid profiles vary between men and women, Cur- rently, sex differences are stressed as important variables to take into account when examining the etiology of CVD. Genome-wide association studies of CVD have employed the sex as a covariate in their analytical models, but generally disregarded potential genetic heterogeneity （GHS） attributable to sex.

Genetic Study Identifies CBLN4 as a Novel Susceptibility Gene for Accident Proneness

Frequent traffic accidents constitute a major danger to human beings.The accident-prone driver who has the stable physiological,psychological,and behavioral characteristics is one of the most prominent causes of traffic accidents.The internal link between the individual characteristics and the accident proneness has been a difficult point in the accident prevention research.The authors selected accident-prone drivers as cases and safe drivers as controls(case-control group) from 18,360 drivers who were enrolled from three public transportation incorporations of China using area stratified sampling method.The case-control groups were 1:1 matched.The authors performed genome-wide association study(GWAS) by 179 cases and 179 controls using the U.S.Affymetrix Genome-Wide Human Mapping SNP 6.0Array.The authors observed that the gene frequencies of34 single-nucleotide polymorphisms(SNPs) in three regions of cases were higher than those in the control(P < 10^(–4)).The authors then tested two independent replication sets for strong association 6 SNPs in 349 pairs of case-control drivers using the U.S.ABI 3730 sequencing method.The results indicated that SNP rs6069499 within linked CBLN4 gene are strongly associated with accident proneness(Pcombined= 6.37×10^(-10)).According to CBLN4 gene mainly involved in adrenal development and the regulation of secretion,the authors performed 12 biochemical parameters of the blood using radioimmunoassay.The levels of dopamine(DA) and adrenocorticotropic(ACTH)hormone showed significant differences between accidentprone drivers and safe drivers(P_(DA)= 0.03,P_(ACTH)= 0.01).It is suggested that the accident-prone drivers may have the idiosyncrasy of susceptibility.

Mapping quantitative trait loci using binned genotypes

Precise mapping of quantitative trait loci(QTLs)is critical for assessing genetic effects and identifying candidate genes for quantitative traits.Interval and composite interval mappings have been the methods of choice for several decades,which have provided tools for identifying genomic regions harboring causal genes for quantitative traits.Historically,the concept was developed on the basis of sparse marker maps where genotypes of loci within intervals could not be observed.Currently,genomes of many organisms have been saturated with markers due to the new sequencing technologies.Genotyping by sequencing usually generates hundreds of thousands of single nucleotide polymorphisms(SNPs),which often include the causal polymorphisms.The concept of interval no longer exists,prompting the necessity of a norm change in QTL mapping technology to make use of the high-volume genomic data.Here we developed a statistical method and a software package to map QTLs by binning markers into haplotype blocks,called bins.The new method detects associations of bins with quantitative traits.It borrows the mixed model methodology with a polygenic control from genome-wide association studies(GWAS)and can handle all kinds of experimental populations under the linear mixed model(LMM)framework.We tested the method using both simulated data and data from populations of rice.The results showed that this method has higher power than the current methods.An R package named binQTL is available from GitHub.

生物信息学分析方法Ⅰ:全基因组关联分析概述

全基因组关联分析(GWAS)是动植物复杂性状相关基因定位的常用手段。高通量基因分型技术的应用极大地推动了GWAS的发展。在植物中,利用GWAS不仅能够以较高的分辨率在全基因组水平鉴定出各种自然群体特定性状相关的基因或区间,而且可揭示表型变异的遗传架构全景图。目前,人们利用GWAS分析方法已在拟南芥(Arabidopsis thaliana)、水稻(Oryza sativa)、小麦(Triticum aestivum)、玉米(Zea mays)和大豆(Glycine max)等模式植物和重要农作物品系中发掘出与各种性状显著相关的数量性状座位(QTL)及其候选基因位点,阐明了这些性状的遗传基础,并为揭示这些性状背后的分子机理提供候选基因,也为作物高产优质品种的选育提供了理论依据。该文对GWAS的方法、影响因素及数据分析流程进行了详细描述,以期为相关研究提供参考。

Polygenic risk scores:the future of cancer risk prediction,screening,and precision prevention

Genome-wide association studies(GWASs)have shown that the genetic architecture of cancers are highly polygenic and enabled researchers to identify genetic risk loci for cancers.The genetic variants associated with a cancer can be combined into a polygenic risk score(PRS),which captures part of an individual’s genetic susceptibility to cancer.Recently,PRSs have been widely used in cancer risk prediction and are shown to be capable of identifying groups of individuals who could benefit from the knowledge of their probabilistic susceptibility to cancer,which leads to an increased interest in understanding the potential utility of PRSs that might further refine the assessment and management of cancer risk.In this context,we provide an overview of the major discoveries from cancer GWASs.We then review the methodologies used for PRS construction,and describe steps for the development and evaluation of risk prediction models that include PRS and/or conventional risk factors.Potential utility of PRSs in cancer risk prediction,screening,and precision prevention are illustrated.Challenges and practical considerations relevant to the implementation of PRSs in health care settings are discussed.