Accounting for Population Structure in Gene-by-Environment Interactions in Genome-Wide Association Studies Using Mixed Models

Although genome-wide association studies (GWASs) have discovered numerous novel genetic variants associated with many complex traits and diseases, those genetic variants typically explain only a small fraction of phenotypic variance. Factors that account for phenotypic variance include environmental factors and gene-by-environment interactions (GEIs). Recently, several studies have conducted genome-wide gene-by-environment association analyses and demonstrated important roles of GEIs in complex traits. One of the main challenges in these association studies is to control effects of population structure that may cause spurious associations. Many studies have analyzed how population structure influences statistics of genetic variants and developed several statistical approaches to correct for population structure. However, the impact of population structure on GEI statistics in GWASs has not been extensively studied and nor have there been methods designed to correct for population structure on GEI statistics. In this paper, we show both analytically and empirically that population structure may cause spurious GEIs and use both simulation and two GWAS datasets to support our finding. We propose a statistical approach based on mixed models to account for population structure on GEI statistics. We find that our approach effectively controls population structure on statistics for GEIs as well as for genetic variants.     

Unraveling epistasis with triple testcross progenies of near-isogenic lines

Libraries of near-isogenic lines (NILs) are a powerful plant genetic resource to map quantitative trait loci (QTL). Nevertheless, QTL mapping with NILs is mostly restricted to genetic main effects. Here we propose a two-step procedure to map additive-by-additive digenic epistasis with NILs. In the first step, a generation means analysis of parents, their F₁ hybrid, and one-segment NILs and their triple testcross (TTC) progenies is used to identify in a one-dimensional scan loci exhibiting QTL-by-background interactions. In a second step, one-segment NILs with significant additive-by-additive background interactions are used to produce particular two-segment NILs to test for digenic epistatic interactions between these segments. We evaluated our approach by analyzing a random subset of a genomewide Arabidopsis thaliana NIL library for growth-related traits. The results of our experimental study illustrated the potential of the presented two-step procedure to map additive-by-additive digenic epistasis with NILs. Furthermore, our findings suggested that additive main effects as well as additive-by-additive digenic epistasis strongly influence the genetic architecture underlying growth-related traits of A. thaliana.     

揭示群落结构及其环境响应的联合物种分布模型的研究进展

物种分布模型通常用于基础生态和应用生态研究,用来确定影响生物分布和物种丰富度的因素,量化物种与非生物条件的关系,预测物种对土地利用和气候变化的反应,并确定潜在的保护区.在传统的物种分布模型中,生物的相互作用很少被纳入,而联合物种分布模型(JSDMs)作为近年提出的一种新的可行方法,可以同时考虑环境因素和生物交互作用,因而成为分析生物群落结构和种间相互作用过程的有力工具.JSDMs以物种分布模型(SDMs)为基础,通常采用广义线性回归模型建立物种对环境变量的多变量响应,以随机效应的形式获取物种间的关联,同时结合隐变量模型(LVMs),并基于Laplace近似和马尔科夫蒙脱卡罗模拟的最大似然估计或贝叶斯方法来估算模型参数.本文对JSDMs的产生及理论基础进行归纳总结,重点介绍了不同类型JSDMs的特点及其在现代生态学中的应用,阐述了JSDMs的应用前景、使用过程中存在的问题及发展方向.随着对环境因素与多物种种间关系研究的深入,JSDMs将是今后物种分布模型研究的重点.     

Epistatic association mapping in homozygous crop cultivars

The genetic dissection of complex traits plays a crucial role in crop breeding. However, genetic analysis and crop breeding have heretofore been performed separately. In this study, we designed a new approach that integrates epistatic association analysis in crop cultivars with breeding by design. First, we proposed an epistatic association mapping (EAM) approach in homozygous crop cultivars. The phenotypic values of complex traits, along with molecular marker information, were used to perform EAM. In our EAM, all the main-effect quantitative trait loci (QTLs), environmental effects, QTL-by-environment interactions and QTL-by-QTL interactions were included in a full model and estimated by empirical Bayes approach. A series of Monte Carlo simulations was performed to confirm the reliability of the new method. Next, the information from all detected QTLs was used to mine novel alleles for each locus and to design elite cross combination. Finally, the new approach was adopted to dissect the genetic basis of seed length in 215 soybean cultivars obtained, by stratified random sampling, from 6 geographic ecotypes in China. As a result, 19 main-effect QTLs and 3 epistatic QTLs were identified, more than 10 novel alleles were mined and 3 elite parental combinations, such as Daqingdou and Zhengzhou790034, were predicted.     

Characterization of the main effects,epistatic effects and their environmental interactions of QTLs on the genetic basis of yield traits in rice

Main effects, epistatic effects and their environmental interactions of QTLs are all important genetic components of quantitative traits. In this study, we analyzed the main effects, epistatic effects of the QTLs, and QTL by environment interactions (QEs) underlying four yield traits, using a population of 240 recombinant inbred lines from a cross between two rice varieties tested in replicated field trials. A genetic linkage map with 220 DNA marker loci was constructed. A mixed linear model approach was used to detect QTLs with main effects, QTLs involved in digenic interactions and QEs. In total, 29 QTLs of main effects, and 35 digenic interactions involving 58 loci were detected for the four traits. Thirteen QTLs with main effects showed QEs; no QE was detected for the QTLs involved in epistatic interactions. The amount of variations explained by the QTLs of main effect were larger than the QTLs involved in epistatic interactions, which in turn were larger than QEs for all four traits. This study illustrates the ability of the analysis to assess the genetic components underlying the quantitative traits, and demonstrates the relative importance of the various components as the genetic basis of yield traits in this population.     

Evidence for Fine-Scale Natal Homing Among Island Beach Spawning Sockeye Salmon, Oncorhynchus nerka

Salmonid fishes aggregate for breeding at spatially defined, suitable habitats. These aggregations may evolve into discrete populations when precise natal homing leads to reproductive isolation, and local regimes of selection lead to adaptation. Population structure is often defined by persistent differences in selectively neutral genetic markers and in mean values of morphological and life-history traits between locations. This approach is limited by the spatial scale at which traits diverge; low levels of reproductively successful straying, combined with similar selective pressures on life-history traits resulting from similar habitat features and environmental conditions, can significantly reduce the power of these discriminatory methods. We compared data on three life-history traits and polymorphism of DNA microsatellites for evidence of population subdivision among sockeye salmon spawning on spatially discrete but physically similar beaches on islands in Iliamna Lake, Alaska. We found small but significant differences in average body length, body depth and age composition between sites as well as significant interactions between site and year. These interactions, reflecting random variation in growth or recruitment among sites, are a powerful tool for discriminating populations with similar mean trait values. These results suggest fine-scale homing to natal sites, but the microsatellite data revealed no evidence of restricted gene flow among sites. There seems to be enough straying among the populations to prevent differentiation at neutral traits but enough homing for them to be functionally distinct.     

Multivariate whole genome average interval mapping: QTL analysis for multiple traits and/or environments

A major aim in some plant-based studies is the determination of quantitative trait loci (QTL) for multiple traits or across multiple environments. Understanding these QTL by trait or QTL by environment interactions can be of great value to the plant breeder. A whole genome approach for the analysis of QTL is presented for such multivariate applications. The approach is an extension of whole genome average interval mapping in which all intervals on a linkage map are included in the analysis simultaneously. A random effects working model is proposed for the multivariate (trait or environment) QTL effects for each interval, with a variance-covariance matrix linking the variates in a particular interval. The significance of the variance-covariance matrix for the QTL effects is tested and if significant, an outlier detection technique is used to select a putative QTL. This QTL by variate interaction is transferred to the fixed effects. The process is repeated until the variance-covariance matrix for QTL random effects is not significant; at this point all putative QTL have been selected. Unlinked markers can also be included in the analysis. A simulation study was conducted to examine the performance of the approach and demonstrated the multivariate approach results in increased power for detecting QTL in comparison to univariate methods. The approach is illustrated for data arising from experiments involving two doubled haploid populations. The first involves analysis of two wheat traits, α-amylase activity and height, while the second is concerned with a multi-environment trial for extensibility of flour dough. The method provides an approach for multi-trait and multi-environment QTL analysis in the presence of non-genetic sources of variation.     

Towards genetic markers in animal populations as biomonitors for human-induced environmental change

Genetic markers provide potentially sensitive indicators of changes in environmental conditions because the genetic constitution of populations is normally altered well before populations become extinct. Genetic indicators in populations include overall genetic diversity, genetic changes in traits measured at the phenotypic level, and evolution at specific loci under selection. While overall genetic diversity has rarely been successfully related to environmental conditions, genetically based changes in traits have now been linked to the presence of toxins and both local and global temperature shifts. Candidate loci for monitoring stressors are emerging from information on how specific genes influence traits, and from screens of random loci across environmental gradients. Drosophila research suggests that chromosomal regions under recent intense selection can be identified from patterns of molecular variation and a high frequency of transposable element insertions. Allele frequency changes at candidate loci have been linked to pesticides, pollutants and climate change. Nevertheless, there are challenges in interpreting allele frequencies in populations, particularly when a large number of loci control a trait and when interactions between alleles influence trait expression. To meet these challenges, population samples should be collected for longitudinal studies, and experimental programmes should be undertaken to link variation at candidate genes to ecological processes.     

Genetic effects at pleiotropic loci are context-dependent with consequences for the maintenance of genetic variation in populations

Context-dependent genetic effects, including genotype-by-environment and genotype-by-sex interactions, are a potential mechanism by which genetic variation of complex traits is maintained in populations. Pleiotropic genetic effects are also thought to play an important role in evolution, reflecting functional and developmental relationships among traits. We examine context-dependent genetic effects at pleiotropic loci associated with normal variation in multiple metabolic syndrome (MetS) components (obesity, dyslipidemia, and diabetes-related traits). MetS prevalence is increasing in Western societies and, while environmental in origin, presents substantial variation in individual response. We identify 23 pleiotropic MetS quantitative trait loci (QTL) in an F₁₆ advanced intercross between the LG/J and SM/J inbred mouse strains (Wustl:LG,SM-G16; n = 1002). Half of each family was fed a high-fat diet and half fed a low-fat diet; and additive, dominance, and parent-of-origin imprinting genotypic effects were examined in animals partitioned into sex, diet, and sex-by-diet cohorts. We examine the context-dependency of the underlying additive, dominance, and imprinting genetic effects of the traits associated with these pleiotropic QTL. Further, we examine sequence polymorphisms (SNPs) between LG/J and SM/J as well as differential expression of positional candidate genes in these regions. We show that genetic associations are different in different sex, diet, and sex-by-diet settings. We also show that over- or underdominance and ecological cross-over interactions for single phenotypes may not be common, however multidimensional synthetic phenotypes at loci with pleiotropic effects can produce situations that favor the maintenance of genetic variation in populations. Our findings have important implications for evolution and the notion of personalized medicine.     

Functional traits determine plant co-occurrence more than environment or evolutionary relatedness in global drylands

Plant–plant interactions are driven by environmental conditions, evolutionary relationships (ER) and the functional traits of the plants involved. However, studies addressing the relative importance of these drivers are rare, but crucial to improve our predictions of the effects of plant–plant interactions on plant communities and of how they respond to differing environmental conditions. To analyze the relative importance of – and interrelationships among – these factors as drivers of plant–plant interactions, we analyzed perennial plant co-occurrence at 106 dryland plant communities established across rainfall gradients in nine countries. We used structural equation modelling to disentangle the relationships between environmental conditions (aridity and soil fertility), functional traits extracted from the literature, and ER, and to assess their relative importance as drivers of the 929 pairwise plant–plant co-occurrence levels measured. Functional traits, specifically facilitated plants’ height and nurse growth form, were of primary importance, and modulated the effect of the environment and ER on plant–plant interactions. Environmental conditions and ER were important mainly for those interactions involving woody and graminoid nurses, respectively. The relative importance of different plant–plant interaction drivers (ER, functional traits, and the environment) varied depending on the region considered, illustrating the difficulty of predicting the outcome of plant–plant interactions at broader spatial scales. In our global-scale study on drylands, plant–plant interactions were more strongly related to functional traits of the species involved than to the environmental variables considered. Thus, moving to a trait-based facilitation/competition approach help to predict that: (1) positive plant–plant interactions are more likely to occur for taller facilitated species in drylands, and (2) plant–plant interactions within woody-dominated ecosystems might be more sensitive to changing environmental conditions than those within grasslands. By providing insights on which species are likely to better perform beneath a given neighbour, our results will also help to succeed in restoration practices involving the use of nurse plants.     

A subset-based approach improves power and interpretation for the combined analysis of genetic association studies of heterogeneous traits

Pooling genome-wide association studies (GWASs) increases power but also poses methodological challenges because studies are often heterogeneous. For example, combining GWASs of related but distinct traits can provide promising directions for the discovery of loci with small but common pleiotropic effects. Classical approaches for meta-analysis or pooled analysis, however, might not be suitable for such analysis because individual variants are likely to be associated with only a subset of the traits or might demonstrate effects in different directions. We propose a method that exhaustively explores subsets of studies for the presence of true association signals that are in either the same direction or possibly opposite directions. An efficient approximation is used for rapid evaluation of p values. We present two illustrative applications, one for a meta-analysis of separate case-control studies of six distinct cancers and another for pooled analysis of a case-control study of glioma, a class of brain tumors that contains heterogeneous subtypes. Both the applications and additional simulation studies demonstrate that the proposed methods offer improved power and more interpretable results when compared to traditional methods for the analysis of heterogeneous traits. The proposed framework has applications beyond genetic association studies.     

On individual genome-wide association studies and their meta-analysis

Individual genome-wide association (GWA) studies and their meta-analyses represent two approaches for identifying genetic loci associated with complex diseases/traits. Inconsistent findings and non-replicability between individual GWA studies and meta-analyses are commonly observed, hence posing the critical question as to how to interpret their respective results properly. In this study, we performed a series of simulation studies to investigate and compare the statistical properties of the two approaches. Our results show that (1) as expected, meta-analysis of larger sample size is more powerful than individual GWA studies under the ideal setting of population homogeneity among individual studies; (2) under the realistic setting of heterogeneity among individual studies, detection of heterogeneity is usually difficult and meta-analysis (even with the random-effects model) may introduce elevated false positive and/or negative rates; (3) despite relatively small sample size, well-designed individual GWA study has the capacity to identify novel loci for complex traits; (4) replicability between meta-analysis and independent individual studies or between independent meta-analyses is limited, and thus inconsistent findings are not unexpected.     

Regional heritability mapping method helps explain missing heritability of blood lipid traits in isolated populations

Single single-nucleotide polymorphism (SNP) genome-wide association studies (SSGWAS) may fail to identify loci with modest effects on a trait. The recently developed regional heritability mapping (RHM) method can potentially identify such loci. In this study, RHM was compared with the SSGWAS for blood lipid traits (high-density lipoprotein (HDL), low-density lipoprotein (LDL), plasma concentrations of total cholesterol (TC) and triglycerides (TG)). Data comprised 2246 adults from isolated populations genotyped using ∼300 000 SNP arrays. The results were compared with large meta-analyses of these traits for validation. Using RHM, two significant regions affecting HDL on chromosomes 15 and 16 and one affecting LDL on chromosome 19 were identified. These regions covered the most significant SNPs associated with HDL and LDL from the meta-analysis. The chromosome 19 region was identified in our data despite the fact that the most significant SNP in the meta-analysis (or any SNP tagging it) was not genotyped in our SNP array. The SSGWAS identified one SNP associated with HDL on chromosome 16 (the top meta-analysis SNP) and one on chromosome 10 (not reported by RHM or in the meta-analysis and hence possibly a false positive association). The results further confirm that RHM can have better power than SSGWAS in detecting causal regions including regions containing crucial ungenotyped variants. This study suggests that RHM can be a useful tool to explain some of the ‘missing heritability'' of complex trait variation.     

Shared genetic risk factors for obstructive sleep apnea and obesity.

Both obesity and obstructive sleep apnea (OSA) are complex disorders with multiple risk factors, which interact in a complicated fashion to determine the overall phenotype. In addition to environmental risk factors, each disorder has a strong genetic basis that is likely due to the summation of small to moderate effects from a large number of genetic loci. Obesity is a strong risk factor for sleep apnea, and there are some data to suggest sleep apnea may influence obesity. It is therefore not surprising that many susceptibility genes for obesity and OSA should be shared. Current research suggests that approximately half of the genetic variance in the apnea hypopnea index is shared with obesity phenotypes. Genetic polymorphisms that increase weight will also be risk factors for apnea. In addition, given the interrelated pathways regulating both weight and other intermediate phenotypes for sleep apnea such as ventilatory control, upper airway muscle function, and sleep characteristics, it is likely that there are genes with pleiotropic effects independently impacting obesity and OSA traits. Other genetic loci likely interact with obesity to influence development of OSA in a gene-by-environment type of effect. Conversely, environmental stressors such as intermittent hypoxia and sleep fragmentation produced by OSA may interact with obesity susceptibility genes to modulate the importance that these loci have on defining obesity-related traits.     

Identification of main effects, epistatic effects and their environmental interactions of QTLs for yield traits in rice

Quantitative trait loci (QTLs) controlling yield and yield components were identified by using a doubled haploid (DH) population of 120 lines from a sub-specific cross between ‘Samgang’ (Indica) and ‘Nagdong’ (Japonica). Main effects, epistatic effects, their environment interactions of QTLs were analyzed via mixed linear model approach across different environments. A total of 17 putative QTLs were identified on 8 chromosomes and five QTLs were detected over two years. 7 QTLs of main effects and 23 epistatic interactions were observed for five traits. Epistatic interactions played an important role in controlling the expression of yield related traits. The epistatic effects explained higher percentages of phenotype variation for panicles per plant, seed set percentage and yield. Significant QTL×environment (QE) interactions effects were identified for all traits, including 5 main effect QTLs. However, the present study failed to identify the significant interactions between epistatic loci containing main effect QTLs and the environment. The information provided in the present study could be used in the marker-assisted selection to enhance selection efficiency and to improve yield in rice.     

Signals of demographic expansion in Drosophila virilis

Background

The antagonistic co-evolution of hosts and their parasites is considered to be a potential driving force in maintaining host genetic variation including sexual reproduction and recombination. The examination of this hypothesis calls for information about the genetic basis of host-parasite interactions – such as how many genes are involved, how big an effect these genes have and whether there is epistasis between loci. We here examine the genetic architecture of quantitative resistance in animal and plant hosts by concatenating published studies that have identified quantitative trait loci (QTL) for host resistance in animals and plants.

Results

Collectively, these studies show that host resistance is affected by few loci. We particularly show that additional epistatic interactions, especially between loci on different chromosomes, explain a majority of the effects. Furthermore, we find that when experiments are repeated using different host or parasite genotypes under otherwise identical conditions, the underlying genetic architecture of host resistance can vary dramatically – that is, involves different QTLs and epistatic interactions. QTLs and epistatic loci vary much less when host and parasite types remain the same but experiments are repeated in different environments.

Conclusion

This pattern of variability of the genetic architecture is predicted by strong interactions between genotypes and corroborates the prevalence of varying host-parasite combinations over varying environmental conditions. Moreover, epistasis is a major determinant of phenotypic variance for host resistance. Because epistasis seems to occur predominantly between, rather than within, chromosomes, segregation and chromosome number rather than recombination via cross-over should be the major elements affecting adaptive change in host resistance.     

The drivers of woody species richness and density in a Neotropical savannah

Environmental filtering prevents species without certain attributes from occurring in local communities. Traits respond differently to different abiotic factors, assembling communities with varying composition along environmental gradients. Here, we measured proxies of soil fertility, disturbance by fire, response and physiological traits to assess how these variables interact to determine woody species richness and density in a Neotropical savannah. We explicitly incorporated our assumptions about how different abiotic filters influence different subsets of traits into a statistical model using structural equation modelling, yielding a more accurate representation of the assembly process. Fire had an effect on resistance traits, whereas soil fertility influenced physiological traits. Resistance traits explained both the richness and density of plots, whereas physiological traits explained only the density. Fewer fire events led to richer and denser plots. Similarly, areas with lower cation exchange capacity assembled less dense communities. Furthermore, we showed that structural equation modelling yielded a realistic representation of the bivariate interactions of distinct environmental filters with different subsets of traits.     

An Alternative Nonlinear Genetic Model

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Genetic architecture of highly complex chemical resistance traits across four yeast strains

Many questions about the genetic basis of complex traits remain unanswered. This is in part due to the low statistical power of traditional genetic mapping studies. We used a statistically powerful approach, extreme QTL mapping (X-QTL), to identify the genetic basis of resistance to 13 chemicals in all 6 pairwise crosses of four ecologically and genetically diverse yeast strains, and we detected a total of more than 800 loci. We found that the number of loci detected in each experiment was primarily a function of the trait (explaining 46% of the variance) rather than the cross (11%), suggesting that the level of genetic complexity is a consistent property of a trait across different genetic backgrounds. Further, we observed that most loci had trait-specific effects, although a small number of loci with effects in many conditions were identified. We used the patterns of resistance and susceptibility alleles in the four parent strains to make inferences about the allele frequency spectrum of functional variants. We also observed evidence of more complex allelic series at a number of loci, as well as strain-specific signatures of selection. These results improve our understanding of complex traits in yeast and have implications for study design in other organisms.