Epistasis Interactions Research Articles

Dissection of genetic architecture of rice plant height and heading date by multiple-strategy-based association studies.

Xieyou9308 is a certified super hybrid rice cultivar with a high grain yield. To investigate its underlying genetic basis of high yield potential, a recombinant inbred line (RIL) population derived from the cross between the maintainer line XieqingzaoB (XQZB) and the restorer line Zhonghui9308 (ZH9308) was constructed for identification of quantitative trait SNPs (QTSs) associated with two important agronomic traits, plant height (PH) and heading date (HD). By re-sequencing of 138 recombinant inbred lines (RILs), a total of ~0.7 million SNPs were identified for the association studies on the PH and HD. Three association mapping strategies (including hypothesis-free genome-wide association and its two complementary hypothesis-engaged ones, QTL-based association and gene-based association) were adopted for data analysis. Using a saturated mixed linear model including epistasis and environmental interaction, we identified a total of 31 QTSs associated with either the PH or the HD. The total estimated heritability across three analyses ranged from 37.22% to 45.63% and from 37.53% to 55.96% for the PH and HD, respectively. In this study we examined the feasibility of association studies in an experimental population (RIL) and identified several common loci through multiple strategies which could be preferred candidates for further research.

Incorporating epistasis interaction of genetic susceptibility single nucleotide polymorphisms in a lung cancer risk prediction model.

Incorporation of genetic variants such as single nucleotide polymorphisms (SNPs) into risk prediction models may account for a substantial fraction of attributable disease risk. Genetic data, from 2385 subjects recruited into the Liverpool Lung Project (LLP) between 2000 and 2008, consisting of 20 SNPs independently validated in a candidate-gene discovery study was used. Multifactor dimensionality reduction (MDR) and random forest (RF) were used to explore evidence of epistasis among 20 replicated SNPs. Multivariable logistic regression was used to identify similar risk predictors for lung cancer in the LLP risk model for the epidemiological model and extended model with SNPs. Both models were internally validated using the bootstrap method and model performance was assessed using area under the curve (AUC) and net reclassification improvement (NRI). Using MDR and RF, the overall best classifier of lung cancer status were SNPs rs1799732 (DRD2), rs5744256 (IL-18), rs2306022 (ITGA11) with training accuracy of 0.6592 and a testing accuracy of 0.6572 and a cross-validation consistency of 10/10 with permutation testing P<0.0001. The apparent AUC of the epidemiological model was 0.75 (95% CI 0.73–0.77). When epistatic data were incorporated in the extended model, the AUC increased to 0.81 (95% CI 0.79–0.83) which corresponds to 8% increase in AUC (DeLong's test P=2.2e-16); 17.5% by NRI. After correction for optimism, the AUC was 0.73 for the epidemiological model and 0.79 for the extended model. Our results showed modest improvement in lung cancer risk prediction when the SNP epistasis factor was added.

Gene-gene and gene-sex epistatic interactions of DNMT1, DNMT3A and DNMT3B in autoimmune thyroid disease.

The aim of this study was to investigate the associations of DNA methyltransferases (DNMTs) polymorphisms with susceptibility to autoimmune thyroid diseases (AITDs) and to test gene-gene/gene-sex epistasis interactions. Eight single-nucleotide polymorphisms (SNPs) in DNMT1, DNMT3A and DNMT3B were selected and genotyped by multiplex polymerase chain reaction combined with ligase detection reaction method (PCR-LDR). A total of 685 Graves' disease (GD) patients, 353 Hashimoto's thyroiditis (HT) patients and 909 healthy controls were included in the final analysis. Epistasis was tested by additive model, multiplicative model and general multifactor dimensionality reduction (general MDR). Rs2424913 (DNMT3B) and rs2228611 (DNMT1) were associated with susceptibility to AITD and GD in the dominant and overdominant model, respectively (rs2424913: P=0.009 for AITD, P=0.0041 for GD; rs2228611: P=0.035 for AITD, P=0.043 for GD). Multiplicative and multiple high dimensional gene-gene or gene-sex interactions were also observed in this study. We have found evidence for a potential role of rs2424913 (DNMT3B) and rs2228611 (DNMT1) in AITD susceptibility and identified novel gene-gene/gene-sex interactions in AITD. Our study may highlight sex and genes of DNMTs family as contributors to the pathogenesis of AITD.

Genome-wide dissection of the maize ear genetic architecture using multiple populations.

Improvement of grain yield is an essential long-term goal of maize (Zea mays) breeding to meet continual and increasing food demands worldwide, but the genetic basis remains unclear. We used 10 different recombination inbred line (RIL) populations genotyped with high-density markers and phenotyped in multiple environments to dissect the genetic architecture of maize ear traits. Three methods were used to map the quantitative trait loci (QTLs) affecting ear traits. We found 17-34 minor- or moderate-effect loci that influence ear traits, with little epistasis and environmental interactions, totally accounting for 55.4-82% of the phenotypic variation. Four novel QTLs were validated and fine mapped using candidate gene association analysis, expression QTL analysis and heterogeneous inbred family validation. The combination of multiple different populations is a flexible and manageable way to collaboratively integrate widely available genetic resources, thereby boosting the statistical power of QTL discovery for important traits in agricultural crops, ultimately facilitating breeding programs.

An Efficient Genome-Wide Multilocus Epistasis Search.

There has been a continuing interest in approaches that analyze pairwise locus-by-locus (epistasis) interactions using multilocus association models in genome-wide data sets. In this paper, we suggest an approach that uses sure independence screening to first lower the dimension of the problem by considering the marginal importance of each interaction term within the huge loop. Subsequent multilocus association steps are executed using an extended Bayesian least absolute shrinkage and selection operator (LASSO) model and fast generalized expectation-maximization estimation algorithms. The potential of this approach is illustrated and compared with PLINK software using data examples where phenotypes have been simulated conditionally on marker data from the Quantitative Trait Loci Mapping and Marker Assisted Selection (QTLMAS) Workshop 2008 and real pig data sets.

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Objective: To investigate for the first time in the South-Eastern population the possible association of seven polymorphic variants from genome-wide association studies (GWASs) with essential hypertension (EH): rs890283, rs2229238, rs1173771, rs9349379, rs3918226, rs2681472, rs579459, in or near the genes CYP2J2, IL6R, NPR3 - C5orf23, PHACTR1, eNOS, ATP2B1, ABO, respectively, and to further explore the epistasis interactions that are involved in the pathophysiology of high blood pressure. Design and method: 794 Bulgarian subjects were included: 496 normotensive (population controls) and 298 hypertensive. Genomic DNA was extracted from venous blood samples. Chemagic Magnetic Separation Module I was used to prepare the isolation step. All variants were genotyped with TaqMan Allelic Discrimination Assays and the epistasis interaction was analyzed. We used χ2 test and generalized multifactor dimensionality reduction (GMDR) analysis to identify the models of genotype interaction. Data were analyzed with PLINK v1.07. Results: Genotype frequencies for all polymorphisms in controls and in patients with EH were in Hardy-Weinberg equilibrium (p > 0.05). In single-locus analysis, the rs9349379 in PHACTR1 gene demonstrated significant association in recessive model with susceptibility to EH in the female subgroup (OR 1.68, 95%CI 1.15–2.46; p = 0.009), whereas rs11646213 in CDH13 gene was more common in female controls than in female hypertensive patients in the same model (OR 0.36, 95%CI 0.14–0.95; p = 0.035). Generalized multifactor dimensionality reduction analysis demonstrated that there exist significant interactions between the studied polymorphisms (Table).Conclusions: The results of this epistasis study indicates a significant association between the studied polymorphic variants and essential hypertension in the Bulgarian population.

Genome-wide search followed by replication reveals genetic interaction of CD80 and ALOX5AP associated with systemic lupus erythematosus in Asian populations

ObjectivesGenetic interaction has been considered as a hallmark of the genetic architecture of systemic lupus erythematosus (SLE). Based on two independent genome-wide association studies (GWAS) on Chinese populations, we performed...

Genetics and Anthocyanin Analysis of Flower Color in Mexican Petunia

The genetics and anthocyanins responsible for flower color were studied in Ruellia simplex Wright (mexican petunia). An F2 population with 153 individuals segregating for four flower colors was developed from a cross between a maternal individual with white corolla with purple throat (WP) and a paternal individual with pink corolla (PK). All the F1 generation had fully purple flowers (P). The F2 generation segregated 94 P:30 PK:24 WP:5 WPK (WPK is a new color combination of white corolla limb and pink throat). These data were separated into groups for corolla limb color and for throat color. The ratio for corolla limb color segregated 94 P:30 PK:29 W, which fits a 9:3:4 recessive epistasis interaction (P = 0.22). The data for corolla throat segregated 118 P:35 PK, which fits a 3:1 ratio (P = 0.54). High-performance liquid chromatography mass spectrometry analyses were performed to elucidate the anthocyanins responsible for the four obtained flower colors. We found that delphinidin derivatives conferred purple corolla color, whereas pelargonidin derivatives were responsible for the pink corolla color. Purple corolla throat color was the result of delphinidin derivatives, whereas the pink color was the result of peonidin derivatives.

Heuristic identification of biological architectures for simulating complex hierarchical genetic interactions.

Simulation plays an essential role in the development of new computational and statistical methods for the genetic analysis of complex traits. Most simulations start with a statistical model using methods such as linear or logistic regression that specify the relationship between genotype and phenotype. This is appealing due to its simplicity and because these statistical methods are commonly used in genetic analysis. It is our working hypothesis that simulations need to move beyond simple statistical models to more realistically represent the biological complexity of genetic architecture. The goal of the present study was to develop a prototype genotype–phenotype simulation method and software that are capable of simulating complex genetic effects within the context of a hierarchical biology-based framework. Specifically, our goal is to simulate multilocus epistasis or gene–gene interaction where the genetic variants are organized within the framework of one or more genes, their regulatory regions and other regulatory loci. We introduce here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating data in this manner. This approach combines a biological hierarchy, a flexible mathematical framework, a liability threshold model for defining disease endpoints, and a heuristic search strategy for identifying high-order epistatic models of disease susceptibility. We provide several simulation examples using genetic models exhibiting independent main effects and three-way epistatic effects.

Finding the epistasis needles in the genome-wide haystack.

Genome-wide association studies (GWAS) have dominated the field of human genetics for the past 10 years. This study design allows for an unbiased, dense exploration of the genome and provides researchers with a vast array of SNPs to look for association with their trait or disease of interest. GWAS has been referred to as finding needles in a haystack and while many of these "needles," or SNPs associating with disease, have been identified, there is still a great deal of heritability yet to be explained. The missing or phantom heritability is due, at least in part, to epistasis or gene-gene interactions, which have not been extensively explored in GWAS. Part of the challenge for epistasis analysis in GWAS is the sheer magnitude of the search and the computational complexity associated with it. An exhaustive search for epistasis models is not computationally feasible; thus, alternate approaches must be considered. In this chapter, these approaches will be reviewed briefly, and the incorporation of biological knowledge to guide this process will be further expanded upon. Real biological data examples where this approach has yielded successful identification of epistasis will also be provided. Epistasis has been known to be important since the early 1900s; however, its prevalence in mainstream research has been somewhat overshadowed by molecular technology advances. Due to the increasing evidence of epistasis in complex traits, it continues to emerge as a likely explanation for missing heritability.

Two rules for the detection and quantification of epistasis and other interaction effects.

Traditionally statistical interactions and epistasis are defined with respect to the ANOVA model of additive effects; that is, epistasis is defined as a deviation from the additive mode of combining main effects of gene substitutions. Furthermore, the definition is relative to a particular scale and epistasis can potentially be eliminated by a non-linear transformation of the underlying phenotype variables. The latter fact raises questions of the scientific validity of the concept if interaction, given its presumed arbitrariness. Here I am arguing that the arbitrariness in the definition and detection of epistasis, and any other interaction, can be eliminated if we observe measurement theoretical constraints on the treatment of quantitative data. I propose two principles for determining the appropriate reference model for the detection of epistasis. The first is the principle of effect propagation stating that the scale type of the effect measure determines the reference model for defining epistasis. For instance, if effects are measured as differences, then the reference model has to be additive. If the reference effects are fold differences, then the reference model has to be multiplicative. A mathematical justification for this rule is provided. The second principle is called irrelevant effects and derives from the principle of meaningfulness in measurement theory. In short, the rule says that the reference model is determined by the allowable scale transformations of the variables measured. The justification for this rule is that any mathematical model in which these variables figure have to be invariant to allowable scale transformations. These two rules can effectively eliminate the arbitrariness in the definition, detection, and quantification of epistasis or any other interaction effect.

Family-based association study of common variants, rare mutation study and epistatic interaction detection in HDAC genes in schizophrenia

BackgroundHistone deacetylases (HDACs) are key enzymes of histone acetylation, and abnormalities in histone modifications and in the level of HDAC proteins have been reported in schizophrenia. The objective of the present study was to systematically test the HDAC genes for its association with schizophrenia. MethodsA family-based genetic association study (951 Caucasian subjects in 313 nuclear families) using 601 tag-single nucleotide polymorphisms in HDAC genes was conducted followed by a replication study of top-ranked markers in a sample of 1427 Caucasian subjects from 241 multiplex families and 176 trios. Epistasis interaction was tested by using the pedigree-based generalized multifactor dimensionality reduction (GMDR). Furthermore, we analyzed exome sequencing data of 1134 subjects for detection of rare mutations in HDAC genomic regions. ResultsIn the exploratory study, ten markers were in significant association with schizophrenia (P<0.01). One maker rs14251 (HDAC3) was replicated (P=0.04) and remained significant in the whole sample (P=0.004). GMDR identified that a significant three-locus interaction model was detected involving rs17265596 (HDAC9), rs7290710 (HDAC10) and rs7634112 (HDAC11) with a good testing accuracy (0.58). No rare mutations were found associated with schizophrenia. ConclusionThis first exploratory systematic study of the HDAC genes provides consistent support for the involvement of the HDAC3 gene in the etiology of schizophrenia. A statistical epistatic interaction between HDAC9, HDAC10, and HDAC11 was detected and seems biologically plausible.

Signaling pathway genes for blood pressure, folate and cholesterol levels among hypertensives: an epistasis analysis.

Irregular atrial pressure, defective folate and cholesterol metabolism contribute to the pathogenesis of hypertension. However, little is known about the combined roles of the methylenetetrahydrofolate reductase (MTHFR), apolipoprotein-E (ApoE) and angiotensin-converting enzyme (ACE) genes, which are involved in metabolism and homeostasis. The objective of this study is to investigate the association of the MTHFR 677 C>T and 1298A>C, ACE insertion-deletion (I/D) and ApoE genetic polymorphisms with hypertension and to further explore the epistasis interactions that are involved in these mechanisms. A total of 594 subjects, including 348 normotensive and 246 hypertensive ischemic stroke subjects were recruited. The MTHFR 677 C>T and 1298A>C, ACE I/D and ApoEpolymorphisms were genotyped and the epistasis interaction were analyzed. The MTHFR 677 C>T and ApoE polymorphisms demonstrated significant associations with susceptibility to hypertension in multiple logistic regression models, multifactor dimensionality reduction and a classification and regression tree. In addition, the logistic regression model demonstrated that significant interactions between the ApoE E3E3, E2E4, E2E2 and MTHFR 677 C>T polymorphisms existed. In conclusion, the results of this epistasis study indicated significant association between the ApoE and MTHFR polymorphisms and hypertension.

Phosphorylation of a WRKY transcription factor by MAPKs is required for pollen development and function in Arabidopsis.

Plant male gametogenesis involves complex and dynamic changes in gene expression. At present, little is known about the transcription factors involved in this process and how their activities are regulated. Here, we show that a pollen-specific transcription factor, WRKY34, and its close homolog, WRKY2, are required for male gametogenesis in Arabidopsis thaliana. When overexpressed using LAT52, a strong pollen-specific promoter, epitope-tagged WRKY34 is temporally phosphorylated by MPK3 and MPK6, two mitogen-activated protein kinases (MAPKs, or MPKs), at early stages in pollen development. During pollen maturation, WRKY34 is dephosphorylated and degraded. Native promoter-driven WRKY34-YFP fusion also follows the same expression pattern at the protein level. WRKY34 functions redundantly with WRKY2 in pollen development, germination, and pollen tube growth. Loss of MPK3/MPK6 phosphorylation sites in WRKY34 compromises the function of WRKY34 in vivo. Epistasis interaction analysis confirmed that MPK6 belongs to the same genetic pathway of WRKY34 and WRKY2. Our study demonstrates the importance of temporal post-translational regulation of WRKY transcription factors in the control of developmental phase transitions in plants.

Association mapping for epistasis and environmental interaction of yield traits in 323 cotton cultivars under 9 different environments.

Improving yield is a major objective for cotton breeding schemes, and lint yield and its three component traits (boll number, boll weight and lint percentage) are complex traits controlled by multiple genes and various environments. Association mapping was performed to detect markers associated with these four traits using 651 simple sequence repeats (SSRs). A mixed linear model including epistasis and environmental interaction was used to screen the loci associated with these four yield traits by 323 accessions of Gossypium hirsutum L. evaluated in nine different environments. 251 significant loci were detected to be associated with lint yield and its three components, including 69 loci with individual effects and all involved in epistasis interactions. These significant loci explain ∼ 62.05% of the phenotypic variance (ranging from 49.06% ∼ 72.29% for these four traits). It was indicated by high contribution of environmental interaction to the phenotypic variance for lint yield and boll numbers, that genetic effects of SSR loci were susceptible to environment factors. Shared loci were also observed among these four traits, which may be used for simultaneous improvement in cotton breeding for yield traits. Furthermore, consistent and elite loci were screened with −Log10 (P-value) >8.0 based on predicted effects of loci detected in different environments. There was one locus and 6 pairs of epistasis for lint yield, 4 loci and 10 epistasis for boll number, 15 loci and 2 epistasis for boll weight, and 2 loci and 5 epistasis for lint percentage, respectively. These results provided insights into the genetic basis of lint yield and its components and may be useful for marker-assisted breeding to improve cotton production.

Identification of quantitative trait loci (QTLs) for seed protein concentration in soybean and analysis for additive effects and epistatic effects of QTLs under multiple environments

AbstractSoybean protein concentration is a key trait driver of successful soybean quality. A recombination inbred lines derived from a cross between ‘Charleston’ and ‘Dongnong594’, were planted in three environments across four years in China. Then, the genetic effects were partitioned into additive main effects, epistatic main effects and their environment interaction effects by using composite interval mapping, multiple interval mapping and composite interval mapping in a mixed linear model. Forty‐three quantitative trait loci QTLs were identified on 17 of 20 soybean chromosomes excluding Ch 7, Ch 8 and Ch 17. Two QTLs showed a good stability across multiple environments, qPRO20‐1 was detected under four environments, which explained 4.4–9.95% phenotypic variances and the allele was from ‘Charleston’ among four environments. qPRO7‐5 was detected under three environments, which explained 7.2–14.5% phenotypic variances and the allele was from ‘Dongnong 594’, three pathway genes of protein biosynthesis were detected in the interval of qPRO7‐5. The additive main‐effect QTLs contributed more phenotypic variation than the epistasis and environmental interaction. This indicated that it is feasible by marker‐assisted selection to improve soybean protein concentration.

Analysis of genetic association and epistasis interactions between circadian clock genes and symptom dimensions of bipolar affective disorder

Bipolar affective disorder (BD) is a severe psychiatric disorder characterized by periodic changes in mood from depression to mania. Disruptions of biological rhythms increase risk of mood disorders. Because clinical representation of disease is heterogeneous, homogenous sets of patients are suggested to use in the association analyses. In our study, we aimed to apply previously computed structure of bipolar disorder symptom dimension for analyses of genetic association. We based quantitative trait on: main depression, sleep disturbances, appetite disturbances, excitement and psychotic dimensions consisted of OPCRIT checklist items. We genotyped 42 polymorphisms from circadian clock genes: PER3, ARNTL, CLOCK and TIMELSSS from 511 patients BD (n = 292 women and n = 219 men). As quantitative trait we used clinical dimensions, described above. Genetic associations between alleles and quantitative trait were performed using applied regression models applied in PLINK. In addition, we used the Kruskal–Wallis test to look for associations between genotypes and quantitative trait. During second stage of our analyses, we used multidimensional scaling (multifactor dimensionality reduction) for quantitative trait to compute pairwise epistatic interactions between circadian gene variants. We found association between ARNTL variant rs11022778 main depression (p = 0.00047) and appetite disturbances (p = 0.004). In epistatic interaction analyses, we observed two locus interactions between sleep disturbances (p = 0.007; rs11824092 of ARNTL and rs11932595 of CLOCK) as well as interactions of subdimension in main depression and ARNTL variants (p = 0.0011; rs3789327, rs10766075) and appetite disturbances in depression and ARNTL polymorphism (p = 7 × 10−4; rs11022778, rs156243).

QTL analysis of soybean oil content under 17 environments

Qi, Z., Han, X., Hou, M., Xin, D., Wang, Z., Zhu, R., Hu, Z., Jiang, H., Li, C., Liu, C., Hu, G. and Chen, Q. 2014. QTL analysis of soybean oil content under 17 environments. Can. J. Plant Sci. 94: 245–261. Soybean oil content is a key trait driver of successful soybean quality. Due to its complex nature, less stable quantitative trait loci (QTL) are known. The goal of this study was to identify important and stable QTL affecting soybean oil content using recombination inbred lines (RILs) derived from a cross between Charleston and Dongnong594. The plant materials were planted in three environments across 9 yr in China. The genetic effects were then partitioned into additive main effects (A), epistatic main effects (AA) and their environment interaction effects (AE and AAE) by using composite interval mapping, multiple interval mapping and composite interval mapping in a mixed linear model. Fifty-six QTL were identified on 15 of 20 soybean chromosomes excluding LG C1, D2, E, M and O by composite interval mapping and multiple interval mapping methods. Seven oil content QTL detected on LG A1, 1 on LG A2, 5 on LG B1, 4 on LG B2, 8 on LG C2, 11 on LG D1a, 2 on LG D1b, 4 on LG F, 5 on LG G, 2 on LG H, 1 on LG I, 1 on LG J, 1 on LG K, 2 on LG L and 2 on LG N. Eight QTL showed a good stability across 17 environments. The additive main-effect QTL contributed more phenotypic variation than the epistasis and environmental interaction. This indicated that it is feasible to improve soybean oil content by marker-assisted selection.

Estimation of epistasis in doubled haploid barley populations considering interactions between all possible marker pairs

AbstractEpistasis, is the interaction between alleles from two or more loci determining complex traits, and thus plays an important role in the development of quantitative traits of crops. In mapping studies of inbreeding species epistasis is usually defined as the interactions between quantitative trait loci with significant additive gene effects. Indeed, in many studies, genes with small effects do not come into the final model and thus the total epistasis interaction effect is biased. Many loci may not have a significant direct effect on the trait under consideration, but they may still affect trait expression by interacting with other loci. In this paper the benefits of using all loci, not only the loci with significant main effects, for estimation of the epistatic effects are presented. The particular examples are with doubled haploids lines and so are restricted to homozygotes and thus additive genetic effects and additive × additive interactions. Numerical analyses were carried out on three populations of doubled haploid lines of barley (Hordeum vulgare L.): 120 doubled haploid lines from the Clipper × Sahara 3771 cross, 145 doubled haploid lines from the Harrington × TR306 cross and 150 doubled haploid lines from the Steptoe × Morex cross. In total, 157 sets of observations were analyzed and altogether 728 pairs of loci were observed for the three datasets.

Using Information Interaction to Discover Epistatic Effects in Complex Diseases

It is widely agreed that complex diseases are typically caused by the joint effects of multiple instead of a single genetic variation. These genetic variations may show stronger effects when considered together than when considered individually, a phenomenon known as epistasis or multilocus interaction. In this work, we explore the applicability of information interaction to discover pairwise epistatic effects related with complex diseases. We start by showing that traditional approaches such as classification methods or greedy feature selection methods (such as the Fleuret method) do not perform well on this problem. We then compare our information interaction method with BEAM and SNPHarvester in artificial datasets simulating epistatic interactions and show that our method is more powerful to detect pairwise epistatic interactions than its competitors. We show results of the application of information interaction method to the WTCCC breast cancer dataset. Our results are validated using permutation tests. We were able to find 89 statistically significant pairwise interactions with a p-value lower than . Even though many recent algorithms have been designed to find epistasis with low marginals, we observed that all (except one) of the SNPs involved in statistically significant interactions have moderate or high marginals. We also report that the interactions found in this work were not present in gene-gene interaction network STRING.