Epistatic Models Research Articles

Inheritance of Resistance to Downy Mildew in Sweet Basil

Sweet basil (Ocimum basilicum) is one of the most economically important culinary herbs in the world, yet global production has become increasingly challenging due to the destructive disease downy mildew (Peronospora belbahrii). Although multiple sources of resistance have been identified, there are no resistant sweet basil cultivars with a commercially acceptable chemotype and phenotype available. The commercial basil cultivar Mrihani (MRI) was identified as resistant and crossed with a Rutgers University susceptible sweet basil inbred line (SB22) to generate a full-sibling family. To determine the mode of inheritance for resistance to downy mildew in basil, six related generations of the MRI × SB22 family were evaluated using a disease severity index (DSI) at northern and southern New Jersey locations over 2 years. All siblings in the F1 and BC1P2 generations were resistant (0.33 > DSI) providing strong evidence that inheritance of resistance from MRI was conferred by dominant alleles. Segregation ratios in the F2 and backcross to the susceptible parent (BCP1) generations demonstrated chi-square goodness of fit to the two-gene complementary (F2: P = 0.11, BC1P1: P = 0.04) and recessive epistatic (F2: P = 0.03, BC1P1: P = 0.63) models. Further analyses of gene effects using a weighted six-parameter scaling test provided evidence that nonallelic additive × additive and additive × dominant gene effects were highly significant (P < 0.001) and resistance reducing. This is the first report of heritable genetic resistance that can be introduced to sweet basil without the issue of sterility barriers. Plant breeding strategies using the MRI × SB22 family should exploit dominant gene action and remove recessive, resistance-reducing alleles from the population.

Identifying gene-gene interactions that are highly associated with Body Mass Index using Quantitative Multifactor Dimensionality Reduction (QMDR).

BackgroundDespite heritability estimates of 40–70 % for obesity, less than 2 % of its variation is explained by Body Mass Index (BMI) associated loci that have been identified so far. Epistasis, or gene-gene interactions are a plausible source to explain portions of the missing heritability of BMI.MethodsUsing genotypic data from 18,686 individuals across five study cohorts – ARIC, CARDIA, FHS, CHS, MESA – we filtered SNPs (Single Nucleotide Polymorphisms) using two parallel approaches. SNPs were filtered either on the strength of their main effects of association with BMI, or on the number of knowledge sources supporting a specific SNP-SNP interaction in the context of BMI. Filtered SNPs were specifically analyzed for interactions that are highly associated with BMI using QMDR (Quantitative Multifactor Dimensionality Reduction). QMDR is a nonparametric, genetic model-free method that detects non-linear interactions associated with a quantitative trait.ResultsWe identified seven novel, epistatic models with a Bonferroni corrected p-value of association < 0.1. Prior experimental evidence helps explain the plausible biological interactions highlighted within our results and their relationship with obesity. We identified interactions between genes involved in mitochondrial dysfunction (POLG2), cholesterol metabolism (SOAT2), lipid metabolism (CYP11B2), cell adhesion (EZR), cell proliferation (MAP2K5), and insulin resistance (IGF1R). Moreover, we found an 8.8 % increase in the variance in BMI explained by these seven SNP-SNP interactions, beyond what is explained by the main effects of an index FTO SNP and the SNPs within these interactions. We also replicated one of these interactions and 58 proxy SNP-SNP models representing it in an independent dataset from the eMERGE study.ConclusionThis study highlights a novel approach for discovering gene-gene interactions by combining methods such as QMDR with traditional statistics.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-015-0074-0) contains supplementary material, which is available to authorized users.

Genetic effects on panicle traits of crossbred rainfed rice cultivars using generation mean analysis

The nature and magnitude of gene effects involved in expression of panicle traits in rainfed rice cultivars were estimated among a wide range of crosses using generation mean analysis. The parental lines comprised of two low-land and six upland rain-fed rice. The lowland parents were used as pollen parents and the upland genotypes were maintained as the seed parents. Crosses were made between them to obtain the F1 hybrids. Backcrosses were produced by crossing the F1 hybrids to their pollen parent to obtain BC1.1 and seed parents to produce BC1.2. The result revealed significant differences (P ≤ 0.05) among the genotypes for all the characters studied. Except for Max x CT7127-49 where P2 and F2 plants of WITA 4 x NERICA 1 that produced long panicles (29.28 and 26.13 cm) that differed significantly (P ≤ 0.05) from other generations, F1 plants produced the longest panicles in the other crosses followed by the F2 plants. For most traits, F1 generation means were higher than the mid-parent values. Significant differences observed between the F1 and F2 generation means in majority of the cases for percentage fertile spikelet and spikelet number per panicle is thought to be due to the diversity in these traits among the parental lines. The means of BC₁ and BC2 tended to be located close to those of their respective recurrent parents. Digenic epistatic model was adequate to explain variation in generation means for all the panicle traits for the pooled analysis. Most of the crosses manifested non-allelic interactions for number of spikelet per panicles and fertile spikelet per panicle and is an indication that epistasis is determined to some extent by the genotypes used for the study. Key words: Generation mean, dominance, additive, epistasis, F1, F2, parental line and backcross.

Association mapping in Populus reveals the interaction between Pto-miR530a and its target Pto-KNAT1.

We used transcript profiling and multi-SNP association to investigate the genetic regulatory relationship between miRNA Pto-miR530a and its target Pto-KNAT1, identifying additive, dominant, and epistatic effects. MicroRNAs (miRNAs) play crucial roles in the post-transcriptional regulation of plant growth and development; indeed, many studies have described the importance of miRNA-target interactions in herbaceous species. However, elucidation of the miRNA-target interactions in trees may require novel strategies. In the present study, we describe a strategy combining expression profiling by reverse transcription quantitative PCR (RT-qPCR) and association mapping with multiple single nucleotide polymorphisms (SNPs) to evaluate the interaction between Pto-miR530a and its target Pto-KNAT1 in Populus tomentosa. RT-qPCR analysis showed a negative correlation (r = -0.62, P < 0.05) between expression levels of Pto-miR530a and Pto-KNAT1 in eight tissues. We used a Bayesian hierarchical model to identify allelic variants of Pto-miR530a and Pto-KNAT1 that associated with eight traits related to growth and wood properties, in a population of 460 unrelated individuals of P. tomentosa. This analysis identified 27 associations, with the proportions of phenotypic variance (R (2)) contributed by each SNP ranging of 0.82-15.81 %, the additive effects of each SNP ranging of 0.16-18.09, and the dominant effects ranging from -14.09 to 19.00. Epistatic interaction models showed a strong interaction among SNPs in the miRNA target with R (2) of 0.1-3.56 %, and information gain of significant SNP pairs of -3.09 to 0.93 %, representing the regulatory interactions between the miRNA and the mRNA. Thus, we used a new strategy that combines association genetics and expression profiling based on SNPs to study the regulatory relationship between this miRNA and its target mRNA, thereby providing novel advances in our understanding of the genetic architecture of important traits.

Identification of additive, dominant, and epistatic variation conferred by key genes in cellulose biosynthesis pathway in Populus tomentosa†.

Economically important traits in many species generally show polygenic, quantitative inheritance. The components of genetic variation (additive, dominant and epistatic effects) of these traits conferred by multiple genes in shared biological pathways remain to be defined. Here, we investigated 11 full-length genes in cellulose biosynthesis, on 10 growth and wood-property traits, within a population of 460 unrelated Populus tomentosa individuals, via multi-gene association. To validate positive associations, we conducted single-marker analysis in a linkage population of 1,200 individuals. We identified 118, 121, and 43 associations (P< 0.01) corresponding to additive, dominant, and epistatic effects, respectively, with low to moderate proportions of phenotypic variance (R2). Epistatic interaction models uncovered a combination of three non-synonymous sites from three unique genes, representing a significant epistasis for diameter at breast height and stem volume. Single-marker analysis validated 61 associations (false discovery rate, Q ≤ 0.10), representing 38 SNPs from nine genes, and its average effect (R2 = 3.8%) nearly 2-fold higher than that identified with multi-gene association, suggesting that multi-gene association can capture smaller individual variants. Moreover, a structural gene–gene network based on tissue-specific transcript abundances provides a better understanding of the multi-gene pathway affecting tree growth and lignocellulose biosynthesis. Our study highlights the importance of pathway-based multiple gene associations to uncover the nature of genetic variance for quantitative traits and may drive novel progress in molecular breeding.

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.

The impact of macroscopic epistasis on long-term evolutionary dynamics.

Genetic interactions can strongly influence the fitness effects of individual mutations, yet the impact of these epistatic interactions on evolutionary dynamics remains poorly understood. Here we investigate the evolutionary role of epistasis over 50,000 generations in a well-studied laboratory evolution experiment in Escherichia coli. The extensive duration of this experiment provides a unique window into the effects of epistasis during long-term adaptation to a constant environment. Guided by analytical results in the weak-mutation limit, we develop a computational framework to assess the compatibility of a given epistatic model with the observed patterns of fitness gain and mutation accumulation through time. We find that a decelerating fitness trajectory alone provides little power to distinguish between competing models, including those that lack any direct epistatic interactions between mutations. However, when combined with the mutation trajectory, these observables place strong constraints on the set of possible models of epistasis, ruling out many existing explanations of the data. Instead, we find that the data are consistent with a "two-epoch" model of adaptation, in which an initial burst of diminishing-returns epistasis is followed by a steady accumulation of mutations under a constant distribution of fitness effects. Our results highlight the need for additional DNA sequencing of these populations, as well as for more sophisticated models of epistasis that are compatible with all of the experimental data.

Gene action for quantitative traits through Generation means analysis in sesame (Sesamum indicum)

Understanding the nature of gene action in the breeding material is helpful for breeders in formulating breeder strategy. In order to understand the type of gene action operating in the breeding materials six generation means (P1, P2, F1, F2, BC1 and BC2) from five crosses were used to estimate the genetic effects of yield and some quantitative traits in sesame (Sesamum indicum L.) The analysis showed the presence of additive, dominance and epistatic gene interactions. The additive dominance model was adequate for capsule length in the KMR 108 × JCS 507 and KKS 98049 × IS 562 B crosses. An epistatic digenic model was assumed for the remaining crosses. Duplicate- type epistasis played a greater role than complementary epistasis. The study deciphered that simple additive dominance model exhibited lack of good fit for all the traits in five crosses studied, indicating the role of non-allelic interactions. Dominance and epistatic interactions played a major role in the inheritance of yield and yield contributing characters in sesame. It can be categorically stated that reciprocal recurrent selection or diallel selective mating system are the need of the hour to modify the genetic architecture of sesame for attaining higher yields with desirable oil content.

EQTL epistasis: detecting epistatic effects and inferring hierarchical relationships of genes in biological pathways

Epistasis is the interactions among multiple genetic variants. It has emerged to explain the 'missing heritability' that a marginal genetic effect does not account for by genome-wide association studies, and also to understand the hierarchical relationships between genes in the genetic pathways. The Fisher's geometric model is common in detecting the epistatic effects. However, despite the substantial successes of many studies with the model, it often fails to discover the functional dependence between genes in an epistasis study, which is an important role in inferring hierarchical relationships of genes in the biological pathway. We justify the imperfectness of Fisher's model in the simulation study and its application to the biological data. Then, we propose a novel generic epistasis model that provides a flexible solution for various biological putative epistatic models in practice. The proposed method enables one to efficiently characterize the functional dependence between genes. Moreover, we suggest a statistical strategy for determining a recessive or dominant link among epistatic expression quantitative trait locus to enable the ability to infer the hierarchical relationships. The proposed method is assessed by simulation experiments of various settings and is applied to human brain data regarding schizophrenia. The MATLAB source codes are publicly available at: http://biomecis.uta.edu/epistasis.

Epistasis between SNPs in genes involved in lipoprotein metabolism influences high- and low-density lipoprotein cholesterol levels

Although genome-wide association (GWA) studies have provided valuable insights into the genetic architecture of human disease, they have elucidated relatively little of the heritability of complex traits. A significant part of the missing heritability might be explained by rare combinations of common SNPs. We hypothesized that epistasis among 15 genes (148 SNPs) involved in lipoprotein metabolism would influence HDL-cholesterol (HDL-C) and LDL-cholesterol (LDL-C) levels. Using SNPwinter software with the various epistatic models, we identified 58 association signals with HDL-C levels for SNPs in eleven genes and 118 associations with LDL-C for SNPs in fourteen genes. These associations were discovered in the urban Ansan cohort (n = 4,102) and replicated in a rural cohort (n = 3,434), the Ansung. We found replicated associations with new genes (SOAT1, APOB, HMGCR, and FDFT1 for HDL-C, and SOAT1, FDFT1, LPL, SQLE, ABCA1, LRP1, SCARB1, and PLTP for LDL-C), in addition to those (CETP, LIPC, LPL, ABCA1, PLTP, SCARB1, and LRP1 for HDL-C, and CETP, LIPC, LDLR, APOB, CYP7A1, and HMGCR for LDL-C) identified by GWA studies, through investigating pairwise interactions between candidate genes of biological and clinical importance. Interestingly, we found that some genes were more likely to be involved in epistatic interactions (ABCA1 and LIPC for HDL-C, and ABCA1, SCARB1, and LIPC for LDL-C).

Gene × Environment interactions in autism spectrum disorders: role of epigenetic mechanisms.

Several studies support currently the hypothesis that autism etiology is based on a polygenic and epistatic model. However, despite advances in epidemiological, molecular and clinical genetics, the genetic risk factors remain difficult to identify, with the exception of a few chromosomal disorders and several single gene disorders associated with an increased risk for autism. Furthermore, several studies suggest a role of environmental factors in autism spectrum disorders (ASD). First, arguments for a genetic contribution to autism, based on updated family and twin studies, are examined. Second, a review of possible prenatal, perinatal, and postnatal environmental risk factors for ASD are presented. Then, the hypotheses are discussed concerning the underlying mechanisms related to a role of environmental factors in the development of ASD in association with genetic factors. In particular, epigenetics as a candidate biological mechanism for gene × environment interactions is considered and the possible role of epigenetic mechanisms reported in genetic disorders associated with ASD is discussed. Furthermore, the example of in utero exposure to valproate provides a good illustration of epigenetic mechanisms involved in ASD and innovative therapeutic strategies. Epigenetic remodeling by environmental factors opens new perspectives for a better understanding, prevention, and early therapeutic intervention of ASD.

Gene interactions and genetics of blast resistance and yield attributes in rice (Oryza sativa L.).

Blast disease caused by the pathogen Pyricularia oryzae is a serious threat to rice production. Six generations viz., P1, P2, F1, F2, B1 and B2 of a cross between blast susceptible high-yielding rice cultivar ADT 43 and resistant near isogenic line (NIL) CT13432-3R, carrying four blast resistance genes Pi1, Pi2, Pi33 and Pi54 in combination were used to study the nature and magnitude of gene action for disease resistance and yield attributes. The epistatic interaction model was found adequate to explain the gene action in most of the traits. The interaction was complementary for number of productive tillers, economic yield, lesion number, infected leaf area and potential disease incidence but duplicate epistasis was observed for the remaining traits. Among the genotypes tested under epiphytotic conditions, gene pyramided lines were highly resistant to blast compared to individuals with single genes indicating that the nonallelic genes have a complementary effect when present together. The information on genetics of various contributing traits of resistance will further aid plant breeders in choosing appropriate breeding strategy for blast resistance and yield enhancement in rice.

Epi2Loc: an R package to investigate two-locus epistatic models.

Epistasis is a growing area of research in genome-wide studies, but the differences between alternative definitions of epistasis remain a source of confusion for many researchers. One problem is that models for epistasis are presented in a number of formats, some of which have difficult-to-interpret parameters. In addition, the relation between the different models is rarely explained. Existing software for testing epistatic interactions between single-nucleotide polymorphisms (SNPs) does not provide the flexibility to compare the available model parameterizations. For that reason we have developed an R package for investigating epistatic and penetrance models, Epi2Loc, to aid users who wish to easily compare, interpret, and utilize models for two-locus epistatic interactions. Epi2Loc facilitates research on SNP-SNP interactions by allowing the R user to easily convert between common parametric forms for two-locus interactions, generate data for simulation studies, and perform power analyses for the selected model with a continuous or dichotomous phenotype. The usefulness of the package for model interpretation and power analysis is illustrated using data on rheumatoid arthritis.

EpiPen: an R package to investigate two-locus epistatic models - retraction.

EpiPen: an R package to investigate two-locus epistatic models - retraction.

A classification and characterization of two-locus, pure, strict, epistatic models for simulation and detection.

BackgroundThe statistical genetics phenomenon of epistasis is widely acknowledged to confound disease etiology. In order to evaluate strategies for detecting these complex multi-locus disease associations, simulation studies are required. The development of the GAMETES software for the generation of complex genetic models, has provided the means to randomly generate an architecturally diverse population of epistatic models that are both pure and strict, i.e. all n loci, but no fewer, are predictive of phenotype. Previous theoretical work characterizing complex genetic models has yet to examine pure, strict, epistasis which should be the most challenging to detect. This study addresses three goals: (1) Classify and characterize pure, strict, two-locus epistatic models, (2) Investigate the effect of model ‘architecture’ on detection difficulty, and (3) Explore how adjusting GAMETES constraints influences diversity in the generated models.ResultsIn this study we utilized a geometric approach to classify pure, strict, two-locus epistatic models by “shape”. In total, 33 unique shape symmetry classes were identified. Using a detection difficulty metric, we found that model shape was consistently a significant predictor of model detection difficulty. Additionally, after categorizing shape classes by the number of edges in their shape projections, we found that this edge number was also significantly predictive of detection difficulty. Analysis of constraints within GAMETES indicated that increasing model population size can expand model class coverage but does little to change the range of observed difficulty metric scores. A variable population prevalence significantly increased the range of observed difficulty metric scores and, for certain constraints, also improved model class coverage.ConclusionsThese analyses further our theoretical understanding of epistatic relationships and uncover guidelines for the effective generation of complex models using GAMETES. Specifically, (1) we have characterized 33 shape classes by edge number, detection difficulty, and observed frequency (2) our results support the claim that model architecture directly influences detection difficulty, and (3) we found that GAMETES will generate a maximally diverse set of models with a variable population prevalence and a larger model population size. However, a model population size as small as 1,000 is likely to be sufficient.

Using Linkage Analysis to Detect Gene-Gene Interaction by Stratifying Family Data on Known Disease, or Disease-Associated, Alleles

Detecting gene-gene interaction in complex diseases is a major challenge for common disease genetics. Most interaction detection approaches use disease-marker associations and such methods have low power and unknown reliability in real data. We developed and tested a powerful linkage-analysis-based gene-gene interaction detection strategy based on conditioning the family data on a known disease-causing allele or disease-associated marker allele. We computer-generated multipoint linkage data for a disease caused by two epistatically interacting loci (A and B). We examined several two-locus epistatic inheritance models: dominant-dominant, dominant-recessive, recessive-dominant, recessive-recessive. At one of the loci (A), there was a known disease-related allele. We stratified the family data on the presence of this allele, eliminating family members who were without it. This elimination step has the effect of raising the “penetrance” at the second locus (B). We then calculated the lod score at the second locus (B) and compared the pre- and post-stratification lod scores at B. A positive difference indicated interaction. We also examined if it was possible to detect interaction with locus B based on a disease-marker association (instead of an identified disease allele) at locus A. We also tested whether the presence of genetic heterogeneity would generate false positive evidence of interaction. The power to detect interaction for a known disease allele was 60–90%. The probability of false positives, based on heterogeneity, was low. Decreasing linkage disequilibrium between the disease and marker at locus A decreased the likelihood of detecting interaction. The allele frequency of the associated marker made little difference to the power.

Evidence for an epistatic effect between TP53 R72P and MDM2 T309G SNPs in HIV infection: a cross-sectional study in women from South Brazil.

ObjectiveTo investigate the associations of TP53 R72P and MDM2 T309G SNPs with HPV infection status, HPV oncogenic risk and HIV infection status.DesignCross-sectional study combining two groups (150 HIV-negative and 100 HIV-positive) of women.MethodsData was collected using a closed questionnaire. DNA was extracted from cervical samples. HPV infection status was determined by nested-PCR, and HPV oncogenic risk group by Sanger sequencing. Both SNPS were genotyped by PCR-RFLP. Crude and adjusted associations involving each exposure (R72P and T309G SNPs, as well as 13 models of epistasis) and each outcome (HPV status, HPV oncogenic risk group and HIV infection) were assessed using logistic regression.ResultsR72P SNP was protectively associated with HPV status (overdominant model), as well as T309G SNP with HPV oncogenic risk (strongest in the overdominant model). No epistatic model was associated with HPV status, but a dominant (R72P over T309G) protective epistatic effect was observed for HPV oncogenic risk. HIV status was strongly associated (risk factor) with different epistatic models, especially in models based on a visual inspection of the results. Moreover, HIV status was evidenced to be an effect mediator of the associations involving HPV oncogenic risk.ConclusionsWe found evidence for a role of R72P and T309G SNPs in HPV status and HPV oncogenic risk (respectively), and strong associations were found for an epistatic effect in HIV status. Prospective studies in larger samples are warranted to validate our findings, which point to a novel role of these SNPs in HIV infection.

EpiPen: An R Package to Investigate Two-Locus Epistatic Models

Epistasis is a growing area of research in genome-wide studies, but the differences between alternative definitions of epistasis remain a source of confusion for many researchers. One problem is that models for epistasis are presented in a number of formats, some of which have difficult-to-interpret parameters. In addition, the relation between the different models is rarely explained. Existing software for testing epistatic interactions between single-nucleotide polymorphisms (SNPs) does not provide the flexibility to compare the available model parameterizations. For that reason we have developed an R package for investigating epistatic and penetrance models, EpiPen, to aid users who wish to easily compare, interpret, and utilize models for two-locus epistatic interactions. EpiPen facilitates research on SNP-SNP interactions by allowing the R user to easily convert between common parametric forms for two-locus interactions, generate data for simulation studies, and perform power analyses for the selected model with a continuous or dichotomous phenotype. The usefulness of the package for model interpretation and power analysis is illustrated using data on rheumatoid arthritis.

Epistatic Clustering: A Model-Based Approach for Identifying Links Between Clusters

Most clustering methods assume that the data can be represented by mutually exclusive clusters, although this assumption may not be the case in practice. For example, in gene expression microarray studies, investigators have often found that a gene can play multiple functions in a cell and may, therefore, belong to more than one cluster simultaneously, and that gene clusters can be linked to each other in certain pathways. This article examines the effect of the above assumption on the likelihood of finding latent clusters using theoretical calculations and simulation studies, for which the epistatic structures were known in advance, and on real data analyses. To explore potential links between clusters, we introduce an epistatic mixture model which extends the Gaussian mixture by including epistatic terms. A generalized expectation-maximization (EM) algorithm is developed to compute the related maximum likelihood estimators. The Bayesian information criterion is then used to determine the order of the proposed model. A bootstrap test is proposed for testing whether the epistatic mixture model is a significantly better fit to the data than a standard mixture model in which each data point belongs to one cluster. The asymptotic properties of the proposed estimators are also investigated when the number of analysis units is large. The results demonstrate that the epistatic links between clusters do have a serious effect on the accuracy of clustering and that our epistatic approach can substantially reduce such an effect and improve the fit.

Epistatic models and pre-selection of markers improve prediction of performance in corn

In our previous papers, we demonstrated that the inclusion of epistatic interactions in marker models improved prediction for corn (Zea mays L.) grain quality traits. The utility of pre-selecting markers for epistatic models was not reported. In papers by other researchers, including epistatic effects in a model did not improve prediction efficacy for whole genome selection. The objectives of this study were therefore to evaluate the value of: (1) pre-selecting markers and interactions at different type 1 error levels to predict performance; (2) adding epistatic interactions to models including all markers, and (3) using marker-based models to predict performance of kernel weight (KWT), flowering date (FDT), and plant height (PHT). Data for KWT, FDT, PHT, and oil and protein concentrations were obtained for 500 S2 lines and their testcrosses from the crosses of Illinois high oil × Illinois low oil and Illinois high protein × Illinois low protein corn strains. Pre-selection using an epistatic model including both single-locus and two-locus interaction effects significant at the P = 0.05 level significantly increased prediction efficacy over selection including all markers and epistatic interactions. Adding all epistatic interactions to a model including all markers did not improve prediction. For most traits, prediction based on the P = 0.05 epistatic pre-selection model was nearly as effective as prediction based on phenotype, suggesting subsequent marker-based selection would be effective.