Efficient Multiple-Trait Association and Estimation of Genetic Correlation Using the Matrix-Variate Linear Mixed Model

Multiple-trait association mapping, in which multiple traits are used simultaneously in the identification of genetic variants affecting those traits, has recently attracted interest. One class of approaches for this problem builds on classical variance component methodology, utilizing a multitrait version of a linear mixed model. These approaches both increase power and provide insights into the genetic architecture of multiple traits. In particular, it is possible to estimate the genetic correlation, which is a measure of the portion of the total correlation between traits that is due to additive genetic effects. Unfortunately, the practical utility of these methods is limited since they are computationally intractable for large sample sizes. In this article, we introduce a reformulation of the multiple-trait association mapping approach by defining the matrix-variate linear mixed model. Our approach reduces the computational time necessary to perform maximum-likelihood inference in a multiple-trait model by utilizing a data transformation. By utilizing a well-studied human cohort, we show that our approach provides more than a 10-fold speedup, making multiple-trait association feasible in a large population cohort on the genome-wide scale. We take advantage of the efficiency of our approach to analyze gene expression data. By decomposing gene coexpression into a genetic and environmental component, we show that our method provides fundamental insights into the nature of coexpressed genes. An implementation of this method is available at http://genetics.cs.ucla.edu/mvLMM.     

Genetic estimations for body size characters,development period and development rate in a coccinelid beetle,Harmonia axyridis

Heritabilities and genetic correlations for body size characters and development period in a coccinellid beetle,Harmonia axyridis were estimated by a sib-analysis experiment. Positive genetic correlations were detected between size characters and development rate. If this is upheld in the field, genetic variation would be eliminated, as the loci affecting the characters are supposed to be fixed. However, the results indicated moderate heritabilities for all characters. Possible explanations for the results are discussed.     

Success and failure in replication of genotype–phenotype associations: How does replication help in understanding the genetic basis of phenotypic variation in outbred populations?

Recent developments in sequencing technologies have facilitated genomewide mapping of phenotypic variation in natural populations. Such mapping efforts face a number of challenges potentially leading to low reproducibility. However, reproducible research forms the basis of scientific progress. We here discuss the options for replication and the reasons for potential nonreproducibility. We then review the evidence for reproducible quantitative trait loci (QTL) with a focus on natural animal populations. Existing case studies of replication fall into three categories: (i) traits that have been mapped to major effect loci (including chromosomal inversion and supergenes) by independent research teams; (ii) QTL fine‐mapped in discovery populations; and (iii) attempts to replicate QTL across multiple populations. Major effect loci, in particular those associated with inversions, have been successfully replicated in several cases within and across populations. Beyond such major effect variants, replication has been more successful within than across populations, suggesting that QTL discovered in natural populations may often be population‐specific. This suggests that biological causes (differences in linkage patterns, allele frequencies or context‐dependencies of QTL) contribute to nonreproducibility. Evidence from other fields, notably animal breeding and QTL mapping in humans, suggests that a significant fraction of QTL is indeed reproducible in direction and magnitude at least within populations. However, there is also a large number of QTL that cannot be easily reproduced. We put forward that more studies should explicitly address the causes and context‐dependencies of QTL signals, in particular to disentangle linkage differences, allele frequency differences and gene‐by‐environment interactions as biological causes of nonreproducibility of QTL, especially between populations.     

Do Molecular Markers Inform About Pleiotropy?

The availability of dense panels of common single-nucleotide polymorphisms and sequence variants has facilitated the study of statistical features of the genetic architecture of complex traits and diseases via whole-genome regressions (WGRs). At the onset, traits were analyzed trait by trait, but recently, WGRs have been extended for analysis of several traits jointly. The expectation is that such an approach would offer insight into mechanisms that cause trait associations, such as pleiotropy. We demonstrate that correlation parameters inferred using markers can give a distorted picture of the genetic correlation between traits. In the absence of knowledge of linkage disequilibrium relationships between quantitative or disease trait loci and markers, speculating about genetic correlation and its causes (e.g., pleiotropy) using genomic data is conjectural.     

全基因组关联分析的进展与反思

全基因组关联分析(genomewide association study,GWAS)是应用人类基因组中数以百万计的单核苷酸多态性(single nucleotide polymorphism,SNP)为标记进行病例-对照关联分析,以期发现影响复杂性疾病发生的遗传特征的一种新策略。近年来,随着人类基因组计划和基因组单倍体图谱计划的实施,人们已通过GWAS方法发现并鉴定了大量与人类性状或复杂性疾病关联的遗传变异,为进一步了解控制人类复杂性疾病发生的遗传特征提供了重要的线索。然而,由于造成复杂性疾病/性状的因素较多,而且GWAS研究系统较为复杂,因此目前GWAS本身亦存在诸多的问题。本文将从研究方式、研究对象、遗传标记,以及统计分析等方面,探讨GWAS的研究现状以及存在的潜在问题,并展望GWAS今后的发展方向。     

Shared quantitative trait loci underlying the genetic correlation between continuous traits

We review genetic correlations among quantitative traits in light of their underlying quantitative trait loci (QTL). We derive an expectation of genetic correlation from the effects of underlying loci and test whether published genetic correlations can be explained by the QTL underlying the traits. While genetically correlated traits shared more QTL (33%) on average than uncorrelated traits (11%), the actual number of shared QTL shared was small. QTL usually predicted the sign of the correlation with good accuracy, but the quantitative prediction was poor. Approximately 25% of trait pairs in the data set had at least one QTL with antagonistic effects. Yet a significant minority (20%) of such trait pairs have net positive genetic correlations due to such antagonistic QTL 'hidden' within positive genetic correlations. We review the evidence on whether shared QTL represent single pleiotropic loci or closely linked monotropic genes, and argue that strict pleiotropy can be viewed as one end of a continuum of recombination rates where r=0. QTL studies of genetic correlation will likely be insufficient to predict evolutionary trajectories over long time spans in large panmictic populations, but will provide important insights into the trade-offs involved in population and species divergence.     

GWAS研究大肠杆菌和金黄色葡萄球菌种间互作进化机制

【目的】通过实验室培养模拟自然环境微生物相互作用,进而找到影响细菌基因型和表型的基因。【方法】将大肠杆菌和金黄色葡萄球菌在实验室条件下进行单独培养和两两混合培养并连续转接,通过得到的数量表型与最大生长速率表型做全基因组关联分析(GWAS),对得到的与表型相关的SNP进行注释与分析。【结果】162个SNP位点影响到大肠杆菌原始菌株与共培养菌株的生长,36个SNP位点影响大肠杆菌菌株在单独培养和共同培养的生长。总共有85个SNP位点影响金黄色葡萄球菌的原始菌株与单独培养。其中5个基因在之前文献中已有报道。对影响不同时间点细菌数量变化形状的SNP位点进行功能注释,大肠杆菌中有706个与生长性能相关。金黄色葡萄球菌中,129个和不同的生长性能相关。大肠杆菌SNP位点的13个基因在之前的研究中已有报道。【结论】混合培养和单独培养都检测到与生长相关的显著基因,本研究表明了GWAS在研究细菌互作进化机制方面的潜力。     

基因测序技术的发展以及对个体化用药水平提高的影响

自基因测序技术发明之时起,就已开始运用在生命科学的研究中,对揭示生命本质的研究起到了关键作用。基因测序技术的运用推动了生命科学的发展,并由此引申了更多的科学问题;人们对未知领域的渴求又推动了基因测序技术的进步,发展出更高速、更低价的新技术。随着测序技术的逐步应用,临床个体化用药的水平有了极大的提高。基因测序技术目前已经成功应用于遗传基因多态性标志物的筛选中,使基因导向的合理用药成为可能;还成功应用于疾病组织突变位点标志物的筛查中,使肿瘤靶向用药成为可能;在病原体耐药基因突变检测中的应用,使基于细菌或病毒耐药突变的个体化用药成为可能。随着测序技术向更高通量、更高精度、更低成本的方向发展,基于基因检测的个体化健康时代将会到来。