Epistatic Models Research Articles

Emergent time scales of epistasis in protein evolution

We introduce a data-driven epistatic model of protein evolution, capable of generating evolutionary trajectories spanning very different time scales reaching from individual mutations to diverged homologs. Our in silico evolution encompasses random nucleotide mutations, insertions and deletions, and models selection using a fitness landscape, which is inferred via a generative probabilistic model for protein families. We show that the proposed framework accurately reproduces the sequence statistics of both short-time (experimental) and long-time (natural) protein evolution, suggesting applicability also to relatively data-poor intermediate evolutionary time scales, which are currently inaccessible to evolution experiments. Our model uncovers a highly collective nature of epistasis, gradually changing the fitness effect of mutations in a diverging sequence context, rather than acting via strong interactions between individual mutations. This collective nature triggers the emergence of a long evolutionary time scale, separating fast mutational processes inside a given sequence context, from the slow evolution of the context itself. The model quantitatively reproduces epistatic phenomena such as contingency and entrenchment, as well as the loss of predictability in protein evolution observed in deep mutational scanning experiments of distant homologs. It thereby deepens our understanding of the interplay between mutation and selection in shaping protein diversity and functions, allows one to statistically forecast evolution, and challenges the prevailing independent-site models of protein evolution, which are unable to capture the fundamental importance of epistasis.

Predicting the evolution of bacterial populations with an epistatic selection-mutation model

Predicting the evolution of bacterial populations with an epistatic selection-mutation model

Functional effects of mutations in proteins can be predicted and interpreted by guided selection of sequence covariation information

Predicting the effects of one or more mutations to the in vivo or in vitro properties of a wild-type protein is a major computational challenge, due to the presence of epistasis, that is, of interactions between amino acids in the sequence. We introduce a computationally efficient procedure to build minimal epistatic models to predict mutational effects by combining evolutionary (homologous sequence) and few mutational-scan data. Mutagenesis measurements guide the selection of links in a sparse graphical model, while the parameters on the nodes and the edges are inferred from sequence data. We show, on 10 mutational scans, that our pipeline exhibits performances comparable to state-of-the-art deep networks trained on many more data, while requiring much less parameters and being hence more interpretable. In particular, the identified interactions adapt to the wild-type protein and to the fitness or biochemical property experimentally measured, mostly focus on key functional sites, and are not necessarily related to structural contacts. Therefore, our method is able to extract information relevant for one mutational experiment from homologous sequence data reflecting the multitude of structural and functional constraints acting on proteins throughout evolution.

Genome-wide analysis with additive and epistasis effects reveals growth-related loci and candidate genes in hybrid Argopecten scallops

The first filial (F1) generation from the successful hybridization of bay scallops (Argopecten irradians) and Peruvian scallops (A. purpuratus) exhibits significant heterosis, playing a crucial role in enhancing the germplasm and increasing yields of the scallops. However, there is significant variation in the growth traits of the F1 hybrid population, and there is little information concerning its hereditary basis. To unravel the heterosis mechanism and identify key loci and genes contributing to superhybrids, genome-wide association study (GWAS) with additive and epistasis models were conducted for six growth-related traits in the F1 hybrid scallop population in this study. In total, 18,789,547 single nucleotide polymorphisms (SNPs) were identified after filtering from 198 hybrid individuals, comprising 63.5% of maternal loci and 36.5% of paternal loci. The additive model revealed 2768 associated SNPs and 14 candidate genes, with most of the loci associated with the body mass traits. The additive-additive epistatic model identified 133,094 SNP interaction pairs and 20 candidate gene pairs with exon region SNPs, which were predominantly associated with shell morphology traits. Almost all the SNP pairs associated with body mass traits were maternal-maternal interactions, but the shell morphology traits had the most maternal-paternal and maternal-maternal interactions, suggesting that body mass traits might be affected mainly by maternal effects and that parental interaction effects mainly contributed to the shell size of the hybrid scallops. Both in the shell morphology and the body mass traits, multiple loci participate in epistasis, interacting with more than one locus and forming high-order epistatic interactions, which could be critical factors contributing to the heterosis observed in hybrid scallops. The identified associated loci and candidate genes not only constitute valuable resources for the molecular breeding of hybrid scallops, contributing to the improvement of growth-related traits, but also revealed mechanisms underlying heterosis in aquatic species, providing insights into the advantages of heterosis and paving the way for future research on hybridization in aquatic organisms.

An epistatic model of sequence evolution generates evolved sequences with in vivo antibiotic functionality

An epistatic model of sequence evolution generates evolved sequences with in vivo antibiotic functionality

In vivo functional phenotypes from a computational epistatic model of evolution

Computational models of evolution are valuable for understanding the dynamics of sequence variation, to infer phylogenetic relationships or potential evolutionary pathways and for biomedical and industrial applications. Despite these benefits, few have validated their propensities to generate outputs with in vivo functionality, which would enhance their value as accurate and interpretable evolutionary algorithms. We demonstrate the power of epistasis inferred from natural protein families to evolve sequence variants in an algorithm we developed called sequence evolution with epistatic contributions (SEEC). Utilizing the Hamiltonian of the joint probability of sequences in the family as fitness metric, we sampled and experimentally tested for in vivo [Formula: see text]-lactamase activity in Escherichia coli TEM-1 variants. These evolved proteins can have dozens of mutations dispersed across the structure while preserving sites essential for both catalysis and interactions. Remarkably, these variants retain family-like functionality while being more active than their wild-type predecessor. We found that depending on the inference method used to generate the epistatic constraints, different parameters simulate diverse selection strengths. Under weaker selection, local Hamiltonian fluctuations reliably predict relative changes to variant fitness, recapitulating neutral evolution. SEEC has the potential to explore the dynamics of neofunctionalization, characterize viral fitness landscapes, and facilitate vaccine development.

Genetic analysis of pericarp pigmentation variation in Corn Belt dent maize.

The US standard for maize commercially grown for grain specifies that yellow corn can contain at maximum 5% corn of other colors. Inbred parents of commercial hybrids typically have clear pericarp, but transgressive segregants in breeding populations can display variation in pericarp pigmentation. We identified 10 doubled haploid biparental populations segregating for pigmented pericarp and evaluated qualitative genetic models using chi-square tests of observed and expected frequencies. Pigmentation ranged from light to dark brown color, and pigmentation intensity was quantitatively measured across 1,327 inbred lines using hue calculated from RGB pixel values. Genetic mapping was used to identify loci associated with pigmentation intensity. For 9 populations, pigmentation inheritance best fit a hypothesis of a 2- or 3-gene epistatic model. Significant differences in pigment intensity were observed across populations. W606S-derived inbred lines with the darkest pericarp often had clear glumes, suggesting the presence of a novel P1-rw allele, a hypothesis supported by a significant quantitative trait locus peak at P1. A separate quantitative trait locus region on chromosome 2 between 221.64 and 226.66 Mbp was identified in LH82-derived populations, and the peak near p1 was absent. A genome-wide association study using 416 inbred lines from the Wisconsin Diversity panel with full genome resequencing revealed 4 significant associations including the region near P1. This study supports that pericarp pigmentation among dent maize inbreds can arise by transgressive segregation when pigmentation in the parental generation is absent and is partially explained by functional allelic variation at the P1 locus.

Correction: Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models

Correction: Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models

The R2R3-MYB transcription factor GaPC controls petal coloration in cotton

Although a few cases of genetic epistasis in plants have been reported, the combined analysis of genetically phenotypic segregation and the related molecular mechanism remains rarely studied. Here, we have identified a gene (named GaPC) controlling petal coloration in Gossypium arboreum and following a heritable recessive epistatic genetic model. Petal coloration is controlled by a single dominant gene, GaPC. A loss-of-function mutation of GaPC leads to a recessive gene Gapc that masks the phenotype of other color genes and shows recessive epistatic interactions. Map-based cloning showed that GaPC encodes an R2R3-MYB transcription factor. A 4814-bp long terminal repeat retrotransposon insertion at the second exon led to GaPC loss of function and disabled petal coloration. GaPC controlled petal coloration by regulating the anthocyanin and flavone biosynthesis pathways. Expression of core genes in the phenylpropanoid and anthocyanin pathways was higher in colored than in white petals. Petal color was conferred by flavonoids and anthocyanins, with red and yellow petals rich in anthocyanin and flavonol glycosides, respectively. This study provides new insight on molecular mechanism of recessive epistasis, also has potential breeding value by engineering GaPC to develop colored petals or fibers for multi-functional utilization of cotton.

Mutational Paths with Sequence-Based Models of Proteins: From Sampling to Mean-Field Characterization.

Identifying and characterizing mutational paths is an important issue in evolutionary biology, with potential applications to bioengineering. We here propose an algorithm to sample mutational paths, which we benchmark on exactly solvable models of proteins in silico, and apply to data-driven models of natural proteins learned from sequence data with restricted Boltzmann machines. We then use mean-field theory to characterize paths for different mutational dynamics of interest, and to extend Kimura's estimate of evolutionary distances to sequence-based epistatic models of selection.

Genomic prediction using information across years with epistatic models and dimension reduction via haplotype blocks.

The importance of accurate genomic prediction of phenotypes in plant breeding is undeniable, as higher prediction accuracy can increase selection responses. In this regard, epistasis models have shown to be capable of increasing the prediction accuracy while their high computational load is challenging. In this study, we investigated the predictive ability obtained in additive and epistasis models when utilizing haplotype blocks versus pruned sets of SNPs by including phenotypic information from the last growing season. This was done by considering a single biological trait in two growing seasons (2017 and 2018) as separate traits in a multi-trait model. Thus, bivariate variants of the Genomic Best Linear Unbiased Prediction (GBLUP) as an additive model, Epistatic Random Regression BLUP (ERRBLUP) and selective Epistatic Random Regression BLUP (sERRBLUP) as epistasis models were compared with respect to their prediction accuracies for the second year. The prediction accuracies of bivariate GBLUP, ERRBLUP and sERRBLUP were assessed with eight phenotypic traits for 471/402 doubled haploid lines in the European maize landrace Kemater Landmais Gelb/Petkuser Ferdinand Rot. The results indicate that the obtained prediction accuracies are similar when utilizing a pruned set of SNPs or haplotype blocks, while utilizing haplotype blocks reduces the computational load significantly compared to the pruned sets of SNPs. The number of interactions considered in the model was reduced from 323.5/456.4 million for the pruned SNP panel to 4.4/5.5 million in the haplotype block dataset for Kemater and Petkuser landraces, respectively. Since the computational load scales linearly with the number of parameters in the model, this leads to a reduction in computational time of 98.9% from 13.5 hours for the pruned set of markers to 9 minutes for the haplotype block dataset. We further investigated the impact of genomic correlation, phenotypic correlation and trait heritability as factors affecting the bivariate models' prediction accuracy, identifying the genomic correlation between years as the most influential one. As computational load is substantially reduced, while the accuracy of genomic prediction is unchanged, the here proposed framework to use haplotype blocks in sERRBLUP provided a solution for the practical implementation of sERRBLUP in real breeding programs. Furthermore, our results indicate that sERRBLUP is not only suitable for prediction across different locations, but also for the prediction across growing seasons.

The role of non-additive gene action on gene expression variation in plant domestication

BackgroundPlant domestication is a remarkable example of rapid phenotypic transformation of polygenic traits, such as organ size. Evidence from a handful of study cases suggests this transformation is due to gene regulatory changes that result in non-additive phenotypes. Employing data from published genetic crosses, we estimated the role of non-additive gene action in the modulation of transcriptional landscapes in three domesticated plants: maize, sunflower, and chili pepper. Using A. thaliana, we assessed the correlation between gene regulatory network (GRN) connectivity properties, transcript abundance variation, and gene action. Finally, we investigated the propagation of non-additive gene action in GRNs.ResultsWe compared crosses between domesticated plants and their wild relatives to a set of control crosses that included a pair of subspecies evolving under natural selection and a set of inbred lines evolving under domestication. We found abundance differences on a higher portion of transcripts in crosses between domesticated-wild plants relative to the control crosses. These transcripts showed non-additive gene action more often in crosses of domesticated-wild plants than in our control crosses. This pattern was strong for genes associated with cell cycle and cell fate determination, which control organ size. We found weak but significant negative correlations between the number of targets of trans-acting genes (Out-degree) and both the magnitude of transcript abundance difference a well as the absolute degree of dominance. Likewise, we found that the number of regulators that control a gene’s expression (In-degree) is weakly but negatively correlated with the magnitude of transcript abundance differences. We observed that dominant-recessive gene action is highly propagable through GRNs. Finally, we found that transgressive gene action is driven by trans-acting regulators showing additive gene action.ConclusionsOur study highlights the role of non-additive gene action on modulating domestication-related traits, such as organ size via regulatory divergence. We propose that GRNs are shaped by regulatory changes at genes with modest connectivity, which reduces the effects of antagonistic pleiotropy. Finally, we provide empirical evidence of the propagation of non-additive gene action in GRNs, which suggests a transcriptional epistatic model for the control of polygenic traits, such as organ size.

EpiReSIM: A Resampling Method of Epistatic Model without Marginal Effects Using Under-Determined System of Equations.

Simulation experiments are essential to evaluate epistasis detection methods, which is the main way to prove their effectiveness and move toward practical applications. However, due to the lack of effective simulators, especially for simulating models without marginal effects (eNME models), epistasis detection methods can hardly verify their effectiveness through simulation experiments. In this study, we propose a resampling simulation method (EpiReSIM) for generating the eNME model. First, EpiReSIM provides two strategies for solving eNME models. One is to calculate eNME models using prevalence constraints, and another is by joint constraints of prevalence and heritability. We transform the computation of the model into the problem of solving the under-determined system of equations. Introducing the complete orthogonal decomposition method and Newton's method, EpiReSIM calculates the solution of the underdetermined system of equations to obtain the eNME model, especially the solution of the high-order model, which is the highlight of EpiReSIM. Second, based on the computed eNME model, EpiReSIM generates simulation data by a resampling method. Experimental results show that EpiReSIM has advantages in preserving the biological properties of minor allele frequencies and calculating high-order models, and it is a convenient and effective alternative method for current simulation software.

Whole genome sequencing reveals epistasis effects within RET for Hirschsprung disease

Common variants in RET and NRG1 have been associated with Hirschsprung disease (HSCR), a congenital disorder characterised by incomplete innervation of distal gut, in East Asian (EA) populations. However, the allelic effects so far identified do not fully explain its heritability, suggesting the presence of epistasis, where effect of one genetic variant differs depending on other (modifier) variants. Few instances of epistasis have been documented in complex diseases due to modelling complexity and data challenges. We proposed four epistasis models to comprehensively capture epistasis for HSCR between and within RET and NRG1 loci using whole genome sequencing (WGS) data in EA samples. 65 variants within the Topologically Associating Domain (TAD) of RET demonstrated significant epistasis with the lead enhancer variant (RET+3; rs2435357). These epistatic variants formed two linkage disequilibrium (LD) clusters represented by rs2506026 and rs2506028 that differed in minor allele frequency and the best-supported epistatic model. Intriguingly, rs2506028 is in high LD with one cis-regulatory variant (rs2506030) highlighted previously, suggesting that detected epistasis might be mediated through synergistic effects on transcription regulation of RET. Our findings demonstrated the advantages of WGS data for detecting epistasis, and support the presence of interactive effects of regulatory variants in RET for HSCR.

Hermann Hoffmann's 1921 Monograph: "The Offspring of Endogenous Psychoses: Genealogical-Characterological Examinations".

Five years after the publication of Rüdin's major sibling study, Hermann Hoffmann, working with Rüdin, performed the first systematic study of the risk for dementia praecox (DP) in offspring of DP probands. Field work was limited to 3 months. Hoffmann ascertained families with at least one parent with certain DP, after Kraepelin, with children the youngest of whom were at least 30 years old. These families contained 103 offspring 30 years or older of whom 7 had definite DP and two possible DP for an estimated risk of 6.8%-8.7%. Hoffmann assessed schizoidia in these children, reporting the quite high risk figure of 47.6%. Hoffmann explored a wide range of two and three locus recessive models in his modest sample. He finds Rüdin's two locus recessive model at the boundary of his results and then reviews three additional more complex models. The simplest is a three-locus recessive model which fits his data better. He also explores an oligogenic three locus model with risk classes of individuals with 1 to 6 risk alleles and an epistatic model where two loci form a di-recessive model for schizoidia, and the third locus is a dominant required for the expression of psychosis. Hoffman questioned whether DP was a "unit-character" appropriate for Mendelian analysis and advocated for a much larger study of offspring. His work should be appreciated in light of his enthusiastic endorsement of Nazi eugenic goals.

Polymorphisms of hypertension susceptibility genes as a risk factors of preeclampsia in the Caucasian population of central Russia

IntroductionThe study was designed to assess the effects of hypertension (HT) susceptibility genes polymorphisms in the development of preeclampsia (PE) in Caucasians from Central Russia. MethodsPE patients (n = 452) and women control group (n = 498) were genotyped for 10 polymorphisms of HT/blood pressure (BP) susceptibility genes (according to the previously published GWAS in Caucasian populations) including AC026703.1 (rs1173771), HFE (rs1799945), BAG6 (rs805303), PLCE1 (rs932764), OBFC1 (rs4387287), ARHGAP42 (rs633185), CERS5 (rs7302981), ATP2B1 (rs2681472), TBX2 (rs8068318) and RGL3 (rs167479). A logistic regression method was applied to search for associations between SNPs and PE. The relationship between SNP-SNP interactions and PE risk was analyzed by performing MB-MDR. ResultsThe rs1799945 gene in HFE significantly independently increased the risk of developing PE (OR = 2.24) and rs805303 in BAG6 was associated with a reduced risk in the occurrence of PE (OR = 0.55–0.78). Among the 10 SNPs examined, nine SNPs were associated with PEs within the 10 most significant SNP-SNP interaction models. Loci rs7302981 CERS5, rs805303 BAG6 and rs932764 PLCE1 contributed to the largest number of epistatic models (50% or more). DiscussionThe present study is the first to report an association between polymorphisms of HT/BP susceptibility genes important for GWAS and the risk of PE in Caucasians from Central Russia. Our pathway-based functional annotation of the PE risk variants highlights the potential regulatory function (epigenetic/eQTL/sQTL/non-synonymous) that nine genetic risk markers and their 115 highly correlated variants exert on 155 genes. The study shows that these genes may function cooperatively in key signaling pathways in PE biology.

Inheritance of androgenesis response in pepper.

Anther culture has become an important part of pepper breeding. Response to haploidy via androgenesis is highly genotype-specific. However, studies on the inheritance of response to anther culture are lacking. Therefore, the study aimed to determine the inheritance of androgenesis. The plant material included crosses involving Capsicum annuum L. (253A, and Inan3363) X C. chinense PI 159,236 populations. To estimate the heritability of the trait, two pepper lines with very high androgenesis responses were crossed with PI 159,236, a non-responsive accession. The androgenesis response was phenotyped using the parents, F1, F2, BC1P1, and BC1P2/P3 created through reciprocal crosses. Using variance components, the number of genes controlling the trait, broad (H2 = 0.97), and narrow sense heritabilities (h2 = 0.20), genetic variance (VG=113.8), the additive (VA=23.9), and the dominance gene variances (VD = 89.9), as well as the environmental variance (VE = 3.6) were calculated. Additive and dominant gene effects were 21% and 79%, respectively. Results derived from two different populations showed that the number of genes controlling the trait was between 1.96 and 2.46, and H2 = 0.96-0.97, h2 = 0.20-0.65, VG=91.8-113.8, VA, = 23.9-62.7, VD = 29.1-89.9, and VE=3.5-3.6 were calculated. The X2 analysis indicated that the most suitable one is the 9: 3: 4 epistatic genetic model (X2 = 2.13, P = 0.343, N = 155). Results obtained from two different populations indicate the existence of a few major genes for response to androgenesis in pepper. Elucidating the inheritance of androgenesis is expected to pave the way for tagging the gene(s) in the pepper genome.

MTHSA-DHEI: multitasking harmony search algorithm for detecting high-order SNP epistatic interactions

Genome-wide association studies have succeeded in identifying genetic variants associated with complex diseases, but the findings have not been well interpreted biologically. Although it is widely accepted that epistatic interactions of high-order single nucleotide polymorphisms (SNPs) [(1) Single nucleotide polymorphisms (SNP) are mainly deoxyribonucleic acid (DNA) sequence polymorphisms caused by variants at a single nucleotide at the genome level. They are the most common type of heritable variation in humans.] are important causes of complex diseases, the combinatorial explosion of millions of SNPs and multiple tests impose a large computational burden. Moreover, it is extremely challenging to correctly distinguish high-order SNP epistatic interactions from other high-order SNP combinations due to small sample sizes. In this study, a multitasking harmony search algorithm (MTHSA-DHEI) is proposed for detecting high-order epistatic interactions [(2) In classical genetics, if genes X1 and X2 are mutated and each mutation by itself produces a unique disease status (phenotype) but the mutations together cause the same disease status as the gene X1 mutation, gene X1 is epistatic and gene X2 is hypostatic, and gene X1 has an epistatic effect (main effect) on disease status. In this work, a high-order epistatic interaction occurs when two or more SNP loci have a joint influence on disease status.], with the goal of simultaneously detecting multiple types of high-order (k1-order, k2-order, …, kn-order) SNP epistatic interactions. Unified coding is adopted for multiple tasks, and four complementary association evaluation functions are employed to improve the capability of discriminating the high-order SNP epistatic interactions. We compare the proposed MTHSA-DHEI method with four excellent methods for detecting high-order SNP interactions for 8 high-orderepistatic interaction models with no marginal effect (EINMEs) and 12 epistatic interaction models with marginal effects (EIMEs) (*) and implement the MTHSA-DHEI algorithm with a real dataset: age-related macular degeneration (AMD). The experimental results indicate that MTHSA-DHEI has power and an F1-score exceeding 90% for all EIMEs and five EINMEs and reduces the computational time by more than 90%. It can efficiently perform multiple high-order detection tasks for high-order epistatic interactions and improve the discrimination ability for diverse epistasis models.

DETECTING EPISTATIC EFFECTS UNDER A QUALITATIVE CONTEXT: IMPORTANCE AND QUANTIFICATION

The objectives of this work were to simulate and quantify epistatic effects on oligogenic traits and to verify the efficiency of Artificial Neural Networks (ANN) and Ridge Regression Best Linear Unbiased Predictor (RRBLUP) in the prediction of genetic values of oligogenic traits controlled by epistatic genes. We simulated 10 F2 populations in Hardy-Weinberg equilibrium with 800 individuals, each. The individuals were genotyped with 105 codominant markers equidistantly distributed along 10 chromosomes. Genotypic values were simulated for each individual considering five oligogenic traits controlled by two biallelic loci, according to five different epistatic models: duplicate recessive genes, dominant and recessive interaction, duplicate dominant genes, recessive epistasis, and dominant epistasis. RRBLUP, as well as an ANN, were used to perform genomic selection. The coefficient of determination of the regression model revealed a mean epistatic effect ranging from 13.3% in the duplicate recessive genes to 62.5% in the duplicate dominant genes. The identification of epistatic genes was superior in the ANN model compared with the RRBLUP approach. Our result reinforces the potential of ANN in predicting genetic values in situations where other than linear or quadratic relationships are present. The results presented in this work offer important insights about the exploration of epistasis in the qualitative context. In situations where epistatic effects are completely ignored, they can play a role on genetic values, as seen for the duplicative dominant genes.

Generation Mean Analysis for Yield, its Components and MYMV Disease Resistance in Greengram [Vigna radiata (L.) Wilczek

Background: Greengram or mungbean is a diploid (2n=22) autogamous leguminous crop. The average global yield is low and stagnant over years in the entire tropical and subtropical Asia. Any improvement in the yield is hard, predominantly because of pest infestation and diseases. Viral diseases result in significant financial losses due to drop in seed yield and quality in many important crops including greengram. Mungbean yellow mosaic disease (MYMD) can lead to yield loss up to 85% in greengram in epidemic conditions. Methods: The present study was conducted at Department of Genetics and Plant Breeding, Faculty of Agriculture, Annamalai University during the period August-October 2020 in three crosses of greengram viz., PLM 506 x IC 76417, PLM 506 x IC 76381 and IC 398746 x IC 76417. The mean data of five basic generations viz., P1, P2, F1, F2 and F3 of three crosses were evaluated. Populations were screened for MYMV disease resistance by employing infector row method. Scaling test was done using the mean values of various generations for the characters. Significance of scale C suggested dominance x dominance (l) type of gene interaction, whereas the significance of scale D showed additive x additive (i) type of gene interaction. The significance of any of the scaling tests proposes inefficacy of simple additive-dominance model. Five genetic effects viz., mean effect (m), additive (d), dominance (h), additive x additive (i) and dominance x dominance (l) was estimated. Result: Hundred-seed weight showed additive-dominance model in the cross PLM 506 x IC 76417. The additive as well as additive x additive type of gene action were in control of number(s) of clusters per plant, percentage of disease infection and most of yield components in the cross PLM 506 x IC 76417. Hence, selection at a later generation is effective to improve these traits. Pod length was controlled by additive gene action in the cross PLM 506 x IC 76381, whereas percentage of disease infection showed additive-dominance model. The additive as well as additive x additive type of gene action were in control of plant height and most of yield components in the cross PLM 506 x IC 76381. In the case of other traits, epistatic model was evidenced. Hence, selection needs to be postponed to a later generation. Based on gene action, these crosses may be employed to evolve high yielding plants with MYMV disease resistance.