Epistatic Effects Research Articles

The long-term effects of genomic selection: 2. Changes in allele frequencies of causal loci and new mutations.

Genetic selection has been applied for many generations in animal, plant, and experimental populations. Selection changes the allelic architecture of traits to create genetic gain. It remains unknown whether the changes in allelic architecture are different for the recently introduced technique of genomic selection compared to traditional selection methods and whether they depend on the genetic architectures of traits. Here, we investigate the allele frequency changes of old and new causal loci under 50 generations of phenotypic, pedigree, and genomic selection, for a trait controlled by either additive, additive and dominance, or additive, dominance, and epistatic effects. Genomic selection resulted in slightly larger and faster changes in allele frequencies of causal loci than pedigree selection. For each locus, allele frequency change per generation was not only influenced by its statistical additive effect but also to a large extent by the linkage phase with other loci and its allele frequency. Selection fixed a large number of loci, and 5 times more unfavorable alleles became fixed with genomic and pedigree selection than with phenotypic selection. For pedigree selection, this was mainly a result of increased genetic drift, while genetic hitchhiking had a larger effect on genomic selection. When epistasis was present, the average allele frequency change was smaller (∼15% lower), and a lower number of loci became fixed for all selection methods. We conclude that for long-term genetic improvement using genomic selection, it is important to consider hitchhiking and to limit the loss of favorable alleles.

Genetic Effects Analysis of QTLs for Rice Grain Size Based on CSSL-Z403 and Its Dissected Single and Dual-Segment Substitution Lines.

Rice chromosomal segment substitution lines (CSSLs) are ideal materials for studying quantitative traits such as grain size. Here, a rice large-grain CSSL-Z403 was identified among progeny of the recipient Xihui18 and the donor Jinhui35 based on molecular marker-assisted selection. Z403 carried 10 substitution segments with average length of 3.01 Mb. Then, a secondary F2 population derived from a cross between Xihui18 and Z403 was used to map quantitative trait loci (QTL) for grain size. Six QTLs distributed on chromosomes 5, 6, 7, 9 and 12 were detected. Finally four single-segment substitution lines (SSSLs) and two dual-segment substitution lines (DSSLs) carrying these target QTLs were constructed, and 10 novel QTLs were identified by four SSSLs. The large grain of Z403 was controlled at least by qGWT5, qGWT7, qGWT9 and qGWT12, and its grain weight was influenced through grain length QTL such as qGL5, qGL6, qGL9 and qGL12, as well as grain width QTL such as qGW5, qGW7, qGW9 and qGW12. Among 16 QTLs, four QTLs including qGL6, etc., might be novel compared with the reported documents. Again, positive or less negative epistatic effects between two non-allelic QTLs (additive effect > 0) may assist screening the genotype with larger grain size in further selection.

Epistasis-aware genome-wide association studies provide insights into the efficient breeding of high-yield and high-quality rice

Marker-assisted selection (MAS) and genomic selection (GS) breeding have greatly improved the efficiency of rice breeding. Due to the influences of epistasis and gene pleiotropy, ensuring the actual breeding effect of MAS and GS is still a difficult challenge to overcome. In this study, 113 indica rice varieties (V) and their 565 testcross hybrids (TC) were used as the materials to investigate the genetic basis of 12 quality traits and nine agronomic traits. The original traits and general combining ability of the parents, as well as the original traits and mid-parent heterosis of TC, were subjected to genome-wide association analysis. In total, 381 primary significantly associated loci (SAL) and 1,759 secondary SALs that had epistatic interactions with these primary SALs were detected. Among these loci, 322 candidate genes located within or nearby the SALs were screened, 204 of which were cloned genes. A total of 39 MAS molecular modules that are beneficial for trait improvement were identified by pyramiding the superior haplotypes of candidate genes and desirable epistatic alleles of the secondary SALs. All the SALs were used to construct genetic networks, in which 91 pleiotropic loci were investigated. Additionally, we estimated the accuracy of genomic prediction in the parent V and TC by incorporating either no SALs, primary SALs, secondary SALs or epistatic effect SALs as covariates. Although the prediction accuracies of the four models were generally not significantly different in the TC dataset, the incorporation of primary SALs, secondary SALs, and epistatic effect SALs significantly improved the prediction accuracies of 5 (26%), 3 (16%), and 11 (58%) traits in the V dataset, respectively. These results suggested that SALs and epistatic effect SALs identified based on an additive genotype can provide considerable predictive power for the parental lines. They also provide insights into the genetic basis of complex traits and valuable information for molecular breeding in rice.

Inheritance of important metric traits of wheat (Triticum aestivum)

Development of salinity tolerant genotypes is important for sustaining wheat productivity in suppressive soils. The generation mean analysis of three bread wheat crosses viz., Lok 1 x Raj 3880, Job 666 x Kharchia 65 and Raj 1972 x Kharchia 65 under normal and saline-sodic environment revealed presence of both additive and non-additive gene effects in the inheritance of grain yield per plant and other contributing characters under both the environments. Among the digenic interactions, all three types of epistatic effects were involved in the inheritance of characters studied. Only duplicate gene interaction was present, wherever available. Hence, intermating in early generations and intense selection in later generations could be successfully adopted for breeding wheat varieties having appreciable salinity tolerance level.

Evolution of a minimal cell

Possessing only essential genes, a minimal cell can reveal mechanisms and processes that are critical for the persistence and stability of life1,2. Here we report on how an engineered minimal cell3,4 contends with the forces of evolution compared with the Mycoplasma mycoides non-minimal cell from which it was synthetically derived. Mutation rates were the highest among all reported bacteria, but were not affected by genome minimization. Genome streamlining was costly, leading to a decrease in fitness of greater than 50%, but this deficit was regained during 2,000 generations of evolution. Despite selection acting on distinct genetic targets, increases in the maximum growth rate of the synthetic cells were comparable. Moreover, when performance was assessed by relative fitness, the minimal cell evolved 39% faster than the non-minimal cell. The only apparent constraint involved the evolution of cell size. The size of the non-minimal cell increased by 80%, whereas the minimal cell remained the same. This pattern reflected epistatic effects of mutations in ftsZ, which encodes a tubulin-homologue protein that regulates cell division and morphology5,6. Our findings demonstrate that natural selection can rapidly increase the fitness of one of the simplest autonomously growing organisms. Understanding how species with small genomes overcome evolutionary challenges provides critical insights into the persistence of host-associated endosymbionts, the stability of streamlined chassis for biotechnology and the targeted refinement of synthetically engineered cells2,7–9.

Integrating GWAS summary statistics, individual-level genotypic and omic data to enhance the performance for large-scale trait imputation.

Recently, a non-parametric method has been proposed to impute the genetic component of a trait for a large set of genotyped individuals based on a separate genome-wide association study (GWAS) summary dataset of the same trait (from the same population). The imputed trait may contain linear, non-linear and epistatic effects of genetic variants, thus can be used for downstream linear or non-linear association analyses and machine learning tasks. Here, we propose an extension of the method to impute both genetic and environmental components of a trait using both single nucleotide polymorphism (SNP)-trait and omics-trait association summary data. We illustrate an application to a UK Biobank subset of individuals (n ≈ 80K) with both body mass index (BMI) GWAS data and metabolomic data. We divided the whole dataset into two equally sized and non-overlapping training and test datasets; we used the training data to build SNP- and metabolite-BMI association summary data and impute BMI on the test data. We compared the performance of the original and new imputation methods. As by the original method, the imputed BMI values by the new method largely retained SNP-BMI association information; however, the latter retained more information about BMI-environment associations and were more highly correlated with the original observed BMI values.

Genome-wide association studies for epistatic genetic effects on fertility and reproduction traits in Holstein cattle.

Non-additive genetic effects are well known to play an important role in the phenotypic expression of complex traits, such as fertility and reproduction. In this study, a genome scan was performed using 41,640 single nucleotide polymorphism (SNP) markers to identify genomic regions associated with epistatic (additive-by-additive) effects in fertility and reproduction traits in Holstein cattle. Nine fertility and reproduction traits were analysed on 5825 and 6090 Holstein heifers and cows with phenotypes and genotypes, respectively. The Marginal Epistasis Test (MAPIT) was used to identify SNPs with significant marginal epistatic effects at a chromosome-wise 5% and 10% false discovery rate (FDR) level. The -log10 (p) values were adjusted by the genomic inflation factor (λ) to correct for the potential bias on the p-values and minimize the possible effects of population stratification. After adjustments, MAPIT enabled the identification of genomic regions with significant marginal epistatic effects for heifers on BTA5 for age at first insemination, BTA3 and BTA24 for non-return rate (NRR); BTA16 and BTA28 for gestation length (GL); BTA1, BTA4 and BTA17 for stillbirth (SB). For the cow traits, MAPIT enabled the identification of regions on BTA11 for GL, BTA11 and BTA16 for SB and BTA19 for calf size (CZ). An additional approach for mapping epistasis in a genome-wide association study was also proposed, in which the genome scan was performed using estimates of epistatic values as the input pseudo-phenotypes, computed using single-trait animal models. Significant SNPs were identified at the chromosome-wise 5% and 10% FDR levels for all traits. For the heifer traits, significant regions were found on BTA7 for AFS; BTA12 for NRR; BTA14 and BTA19 for GL; BTA19 for calving ease (CE); BTA5, BTA24, BTA25 and in the X chromosome for SB; BTA23 and in the X chromosome for CZ and in the X chromosome for the number of services (NS). For the cow traits, significant regions were found on BTA29 and in the X chromosome for NRR, BTA11, BTA16 and in the X chromosome for SB, BTA2 for GL, BTA28 for CZ, BTA19 for calving to first insemination, and in the X chromosome for NS and first insemination to conception. The results suggest that the epistatic genetic effects are likely due to many loci with a small effect rather than few loci with a large effect and/or a single SNP marker alone do not capture the epistatic effects well. The genomic architecture of fertility and reproduction traits is complex, and these results should be validated in independent dairy cattle populations and using alternative statistical models.

The association of immune-related genes and the potential role of IL10 with biliary atresia.

Biliary atresia (BA) is a severe immune-related disease that is characterized by biliary obstruction and cholestasis. The etiology of BA is unclear, our aim was to explore the relationship between biliary tract inflammation and immune-related genes. We selected 14 SNPs in 13 immune-related genes and investigated their associations with BA by using a large case‒control cohort with a total of 503 cases and 1473 controls from southern China. SNP rs1518111 in interleukin10 (IL10) was identified as associated with BA (P = 5.79E-03; OR: 0.80; 95% CI: 0.68-0.94). The epistatic effects of the following pairwise interactions among these SNPs were associated with BA: signal transducer and activator of transcription 4 (STAT4) and chemokine (C-X-C motif) ligand 3 (CXCL3); STAT4 and damage-regulated autophagy modulator1 (DRAM1); CXCL3 and RAD51 paralog B (RAD51B); and interferon gamma (IFNG) and interleukin26 (IL26). Furthermore, we explored the potential role of IL-10 in the pathogenesis of the neonatal mouse model of BA. IL-10 effectively prevented biliary epithelial cell injury and biliary obstruction in murine BA as well as inhibit the activation of BA-related immune cells. In conclusion, this study provided strong evidence implicating IL10 as a susceptibility gene for BA in the southern Chinese population. This study provided strong evidence implicating IL10 as a susceptibility gene for BA in the southern Chinese population. This study could infer that IL-10 may play a protective role in BA mouse model. We found that four SNPs (rs7574865, rs352038, rs4622329, and rs4902562) have genetic interactions.

Sparse Phenotyping and Haplotype-Based Models for Genomic Prediction in Rice

The multi-environment genomic selection enables plant breeders to select varieties resilient to diverse environments or particularly adapted to specific environments, which holds a great potential to be used in rice breeding. To realize the multi-environment genomic selection, a robust training set with multi-environment phenotypic data is of necessity. Considering the huge potential of genomic prediction enhanced sparse phenotyping on the cost saving of multi-environment trials (MET), the establishment of a multi-environment training set could also benefit from it. Optimizing the genomic prediction methods is also crucial to enhance the multi-environment genomic selection. Using haplotype-based genomic prediction models is able to capture local epistatic effects which could be conserved and accumulated across generations much like additive effects thereby benefitting breeding. However, previous studies often used fixed length haplotypes composed by a few adjacent molecular markers disregarding the linkage disequilibrium (LD) which is of essential role in determining the haplotype length. In our study, based on three rice populations with different sizes and compositions, we investigated the usefulness and effectiveness of multi-environment training sets with varying phenotyping intensities and different haplotype-based genomic prediction models based on LD-derived haplotype blocks for two agronomic traits, i.e., days to heading (DTH) and plant height (PH). Results showed that phenotyping merely 30% records in multi-environment training set is able to provide a comparable prediction accuracy to high phenotyping intensities; the local epistatic effects are much likely existent in DTH; dividing the LD-derived haplotype blocks into small segments with two or three single nucleotide polymorphisms (SNPs) helps to maintain the predictive ability of haplotype-based models in large populations; modelling the covariances between environments improves genomic prediction accuracy. Our study provides means to improve the efficiency of multi-environment genomic selection in rice.

Identification of SNP interactions in chromosome 19 for Alzheimer’s Disease through Capsule Network

AbstractBackgroundAlzheimer’s disease (AD) is highly heritable. Genome‐wide association studies and whole genome sequencing have uncovered many AD‐risk variants but are still falling short from providing an explanation of the full heritability. To address this issue, we devised a novel deep learning approach that can quantify the polygenic risk score (PRS) of genetic variants and their interactive effects at the individual level.MethodWe applied a novel deep learning model to the 266,161 SNP chromosome 19 data of 313 AD and 457 cognitively unimpaired (CU) ADNI patients. Through occlusion maps, we quantified the predictive value of 70,643 possible AD‐determining SNPs and identified the top 0.05%, i.e., 35 SNPs, without imposing any additional pre‐assumptions. Epistatic effects increasing or decreasing the risk of AD were further investigated by using the co‐occlusion method. The top 35 AD‐determining SNPs were validated in an external dataset.ResultThe model achieved 68.18% in predicting AD vs. CU. The top 35 AD SNPs showed dominant impact in AD prediction as single as well as an interactive factors. 574 interactions that increased AD risk were found for AD‐predicted patients while 358 interactions predictive of cognitively unimpaired performance were found for CU‐predicted patients. Rs56131196 (APOC1) was recognized as the most significant AD‐determining SNP. It showed interactions with at least 8 other SNPs that further influenced the risk of AD. Rs2229918 (EERC1) was recognized as the most powerful SNP influencing CU diagnostic accuracy.ConclusionOur deep learning model provides a novel approach for assessing the genetic risk for AD at the individual chromosomal level. The results indicate that APOC1 and ERCC1 are strong genetic risk factors for the development of AD. These findings are consistent to the previous medical research. Our model’s capability in deciphering the impact of SNPs on AD development could prove critical for early presymptomatic risk assessment and personalized therapeutic strategy approaches.

Genomic prediction of fertility and calving traits in Holstein cattle based on models including epistatic genetic effects.

The goal of this study was to investigate whether the inclusion of genomic information and epistatic (additive by additive) genetic effects would increase the accuracy of predicting phenotypes adjusted for known environmental effects, reduce prediction bias and minimize the confounding between additive and additive by additive epistatic effects on fertility and calving traits in Holstein cattle. Phenotypic and genotypic records were available for 6090 cows. Eight cow traits were assessed including 56-day nonreturn rate (NRR), number of services (NS), calving to first insemination (CTFS), first insemination to conception (FSTC), gestation length (GL), calving ease (CE), stillbirth (SB) and calf size (CZ). Four scenarios were assessed for their ability to predict adjusted phenotypes, which included: (1) traditional pedigree-based Best Linear Unbiased Prediction (P-BLUP) for additive genetic effects (PA); (2) P-BLUP for additive and epistatic (additive by additive) genetic effects (PAE); (3) genomic BLUP (G-BLUP) for additive genetic effects (GA); and (4) G-BLUP for additive and epistatic genetic effects (GAEn, where n = 1-3 depending on the alternative ways to construct the epistatic genomic matrix used). Constructing epistatic relationship matrix as the Hadamard product of the additive genomic relationship matrix (GAE1), which is the usual method and implicitly assumes a model that fits all pairwise interactions between markers twice and includes the interactions of the markers with themselves (dominance). Two additional constructions of the epistatic genomic relationship matrix were compared to test whether removing the double counting of interactions and the interaction of the markers with themselves (GAE2), and removing double counting of interactions between markers, but including the interaction of the markers with themselves (GAE3) would had an impact on the prediction and estimation error correlation (i.e. confounding) between additive and epistatic genetic effects. Fitting epistatic genetic effects explained up to 5.7% of the variance for NRR (GAE3), 7.7% for NS (GAE1), 11.9% for CTFS (GAE3), 11.1% for FSTC (GAE2), 25.7% for GL (GAE1), 2.3% for CE (GAE1), 14.3% for SB (GAE3) and 15.2% for CZ (GAE1). Despite a substantial proportion of variance being explained by epistatic effects for some traits, the prediction accuracies were similar or lower for GAE models compared with pedigree models and genomic models without epistatic effects. Although the prediction accuracy of direct genomic values did not change significantly between the three variations of the epistatic genetic relationship matrix used, removing the interaction of the markers with themselves reduced the confounding between additive and additive by additive epistatic effects. These results suggest that epistatic genetic effects contribute to the variance of some fertility and calving traits in Holstein cattle. However, the inclusion of epistatic genetic effects in the genomic prediction of these traits is complex and warrant further investigation.

Leveraging the genetic correlation between traits improves the detection of epistasis in genome-wide association studies.

Epistasis, commonly defined as the interaction between genetic loci, is known to play an important role in the phenotypic variation of complex traits. As a result, many statistical methods have been developed to identify genetic variants that are involved in epistasis, and nearly all of these approaches carry out this task by focusing on analyzing one trait at a time. Previous studies have shown that jointly modeling multiple phenotypes can often dramatically increase statistical power for association mapping. In this study, we present the "multivariate MArginal ePIstasis Test" (mvMAPIT)-a multioutcome generalization of a recently proposed epistatic detection method which seeks to detect marginal epistasis or the combined pairwise interaction effects between a given variant and all other variants. By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact-thus, potentially alleviating much of the statistical and computational burden associated with conventional explicit search-based methods. Our proposed mvMAPIT builds upon this strategy by taking advantage of correlation structure between traits to improve the identification of variants involved in epistasis. We formulate mvMAPIT as a multivariate linear mixed model and develop a multitrait variance component estimation algorithm for efficient parameter inference and P-value computation. Together with reasonable model approximations, our proposed approach is scalable to moderately sized genome-wide association studies. With simulations, we illustrate the benefits of mvMAPIT over univariate (or single-trait) epistatic mapping strategies. We also apply mvMAPIT framework to protein sequence data from two broadly neutralizing anti-influenza antibodies and approximately 2,000 heterogeneous stock of mice from the Wellcome Trust Centre for Human Genetics. The mvMAPIT R package can be downloaded at https://github.com/lcrawlab/mvMAPIT.

Balancing Functional Tradeoffs between Protein Stability and ACE2 Binding in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB Lineages: Dynamics-Based Network Models Reveal Epistatic Effects Modulating Compensatory Dynamic and Energetic Changes.

Evolutionary and functional studies suggested that the emergence of the Omicron variants can be determined by multiple fitness trade-offs including the immune escape, binding affinity for ACE2, conformational plasticity, protein stability and allosteric modulation. In this study, we systematically characterize conformational dynamics, structural stability and binding affinities of the SARS-CoV-2 Spike Omicron complexes with the host receptor ACE2 for BA.2, BA.2.75, XBB.1 and XBB.1.5 variants. We combined multiscale molecular simulations and dynamic analysis of allosteric interactions together with the ensemble-based mutational scanning of the protein residues and network modeling of epistatic interactions. This multifaceted computational study characterized molecular mechanisms and identified energetic hotspots that can mediate the predicted increased stability and the enhanced binding affinity of the BA.2.75 and XBB.1.5 complexes. The results suggested a mechanism driven by the stability hotspots and a spatially localized group of the Omicron binding affinity centers, while allowing for functionally beneficial neutral Omicron mutations in other binding interface positions. A network-based community model for the analysis of epistatic contributions in the Omicron complexes is proposed revealing the key role of the binding hotspots R498 and Y501 in mediating community-based epistatic couplings with other Omicron sites and allowing for compensatory dynamics and binding energetic changes. The results also showed that mutations in the convergent evolutionary hotspot F486 can modulate not only local interactions but also rewire the global network of local communities in this region allowing the F486P mutation to restore both the stability and binding affinity of the XBB.1.5 variant which may explain the growth advantages over the XBB.1 variant. The results of this study are consistent with a broad range of functional studies rationalizing functional roles of the Omicron mutation sites that form a coordinated network of hotspots enabling a balance of multiple fitness tradeoffs and shaping up a complex functional landscape of virus transmissibility.

Genomic prediction with haplotype blocks in wheat.

Haplotype blocks might carry additional information compared to single SNPs and have therefore been suggested for use as independent variables in genomic prediction. Studies in different species resulted in more accurate predictions than with single SNPs in some traits but not in others. In addition, it remains unclear how the blocks should be built to obtain the greatest prediction accuracies. Our objective was to compare the results of genomic prediction with different types of haplotype blocks to prediction with single SNPs in 11 traits in winter wheat. We built haplotype blocks from marker data from 361 winter wheat lines based on linkage disequilibrium, fixed SNP numbers, fixed lengths in cM and with the R package HaploBlocker. We used these blocks together with data from single-year field trials in a cross-validation study for predictions with RR-BLUP, an alternative method (RMLA) that allows for heterogeneous marker variances, and GBLUP performed with the software GVCHAP. The greatest prediction accuracies for resistance scores for B. graminis, P. triticina, and F. graminearum were obtained with LD-based haplotype blocks while blocks with fixed marker numbers and fixed lengths in cM resulted in the greatest prediction accuracies for plant height. Prediction accuracies of haplotype blocks built with HaploBlocker were greater than those of the other methods for protein concentration and resistances scores for S. tritici, B. graminis, and P. striiformis. We hypothesize that the trait-dependence is caused by properties of the haplotype blocks that have overlapping and contrasting effects on the prediction accuracy. While they might be able to capture local epistatic effects and to detect ancestral relationships better than single SNPs, prediction accuracy might be reduced by unfavorable characteristics of the design matrices in the models that are due to their multi-allelic nature.

Efficiency of mapping epistatic quantitative trait loci.

Most theoretical studies on epistatic QTL mapping have shown that this procedure is powerful, efficient to control the false positive rate (FPR), and precise to localize QTLs. The objective of this simulation-based study was to show that mapping epistatic QTLs is not an almost-perfect process. We simulated 50 samples of 400 F2 plants/recombinant inbred lines, genotyped for 975 SNPs distributed in 10 chromosomes of 100 cM. The plants were phenotyped for grain yield, assuming 10 epistatic QTLs and 90 minor genes. Adopting basic procedures of r/qtl package, we maximized the power of detection for QTLs (56-74%, on average) but associated with a very high FPR (65%) and a low detection power for the epistatic pairs (7%). Increasing the average detection power for epistatic pairs (14%) highly increased the related FPR. Adopting a procedure to find the best balance between power and FPR, there was a significant decrease in the power of QTL detection (17-31%, on average), associated with a low average detection power for epistatic pairs (8%) and an average FPR of 31% for QTLs and 16% for epistatic pairs. The main reasons for these negative results are a simplified specification of the coefficients of epistatic effects, as theoretically proved, and the effects of minor genes since 2/3 of the FPR for QTLs were due to them. We hope that this study, including the partial derivation of the coefficients of epistatic effects, motivates investigations on how to increase the power of detection for epistatic pairs, effectively controlling the FPR.

Impact of haemoglobinopathies on asymptomatic Plasmodium falciparum infection and naturally acquired immunity among children in Northern Ghana

BackgroundThe protective effect of certain haemoglobinopathies, such as HbS, HbC, and α-thalassaemia, against severe malaria has long been established; however, there is only limited and equivocal evidence regarding their impact on asymptomatic parasitaemia. Here, we investigated the effect of HbS, HbC, and α-thalassaemia on asymptomatic P. falciparum parasitaemia and acquired immunity among children in Northern Ghana.Materials and methodsA cross-sectional study was conducted among 1,017 healthy children (1-17 years) in 13 malaria-endemic communities in Northern Ghana. The children were screened for structural Hb phenotypes using SickleSCAN, for P. falciparum infection using anti-HRP2 malaria RDT and subsequently confirmed by capillary electrophoresis and PCR, respectively. α-thalassaemia genotyping was done using PCR. Levels of IgG specific for six recombinant malaria antigens (PfCSP, GLURP, MSP3, Pfs230, HB3VAR06, and IT4VAR60) and crude asexual blood-stage antigens were evaluated by ELISA.Results266 out of the 1,017 participants had either HbAC (18%) or HbAS (8.4%), whereas 35% had α‐thalassaemia. Twenty-five percent and 6% HbAC individuals co-inherited heterozygous and homozygous α-thalassaemia respectively. Similarly, 25% and 10.5% of HbAS co-inherited heterozygous and homozygous α-thalassaemia. Asymptomatic parasitaemia rates were 23%, 24%, and 19% in those with HbAA, HbAC and HbAS, respectively. The overall parasite carriage rates in heterozygous (21%) and homozygous α-thalassaemia (25%) individuals were similar to that of individuals without α-thalassaemia (23%). P. falciparum parasite carriage risk was about three times higher among homozygous α-thalassaemia individuals with HbAC (OR = 2.97; 95% CI 0.83-10.62) and heterozygous carriers with HbAS variants (OR = 2.86; 95% CI 0.85-9.60) compared to the wildtype. In HbAS individuals, IgG levels to IT4VAR60 and HB3VAR06 were significantly lower, whereas anti-CSP levels were higher than in HbAA and HbAC.ConclusionsCo-inheritance of HbAS and HbAC with α-thalassaemia increased the risk of asymptomatic parasitaemia, an indication of a negative epistatic effect between these Hb variants. Antibody levels against non-PfEMP1 antigens were slightly higher among HbAS children, but quite similar in all study groups, indicating differences in parasite exposure.

The impact of epistasis in the heterosis and combining ability analyses.

The current theoretical knowledge concerning the influence of epistasis on heterosis is based on a simplified multiplicative model. The objective of this study was to assess how epistasis affects the heterosis and combining ability analyses, assuming additive model, hundreds of genes, linkage disequilibrium (LD), dominance, and seven types of digenic epistasis. We developed the quantitative genetics theory for supporting the simulation of the individual genotypic values in nine populations, the selfed populations, the 36 interpopulation crosses, 180 doubled haploids (DHs), and their 16,110 crosses, assuming 400 genes on 10 chromosomes of 200 cM. Epistasis only affects population heterosis if there is LD. Only additive × additive and dominance × dominance epistasis can affect the components of the heterosis and combining ability analyses of populations. Epistasis can have a negative impact on the heterosis and combining ability analysis of populations, leading to wrong inferences regarding the identification of superior and most divergent populations. However, this depends on the type of epistasis, percentage of epistatic genes, and magnitude of their effects. Except for duplicate genes with cumulative effects and non-epistatic genic interaction, there was a decrease in the average heterosis by increasing the percentage of epistatic genes and the magnitude of their effects. The same results are generally true for the combining ability analysis of DHs. The combining ability analyses of subsets of 20 DHs showed no significant average impact of epistasis on the identification of the most divergent ones, regardless of the number of epistatic genes and magnitude of their effects. However, a negative effect on the assessment of the superior DHs can occur assuming 100% of epistatic genes, but depending on the epistasis type and the epistatic effect magnitude.

PyAGH: a python package to fast construct kinship matrices based on different levels of omic data

BackgroundConstruction of kinship matrices among individuals is an important step for both association studies and prediction studies based on different levels of omic data. Methods for constructing kinship matrices are becoming diverse and different methods have their specific appropriate scenes. However, software that can comprehensively calculate kinship matrices for a variety of scenarios is still in an urgent demand.ResultsIn this study, we developed an efficient and user-friendly python module, PyAGH, that can accomplish (1) conventional additive kinship matrces construction based on pedigree, genotypes, abundance data from transcriptome or microbiome; (2) genomic kinship matrices construction in combined population; (3) dominant and epistatic effects kinship matrices construction; (4) pedigree selection, tracing, detection and visualization; (5) visualization of cluster, heatmap and PCA analysis based on kinship matrices. The output from PyAGH can be easily integrated in other mainstream software based on users’ purposes. Compared with other softwares, PyAGH integrates multiple methods for calculating the kinship matrix and has advantages in terms of speed and data size compared to other software. PyAGH is developed in python and C + + and can be easily installed by pip tool. Installation instructions and a manual document can be freely available from https://github.com/zhaow-01/PyAGH.ConclusionPyAGH is a fast and user-friendly Python package for calculating kinship matrices using pedigree, genotype, microbiome and transcriptome data as well as processing, analyzing and visualizing data and results. This package makes it easier to perform predictions and association studies processes based on different levels of omic data.

Deep Mutational Scanning of an Oxygen-Independent Fluorescent Protein CreiLOV for Comprehensive Profiling of Mutational and Epistatic Effects.

Oxygen-independent, flavin mononucleotide-based fluorescent proteins (FbFPs) are promising alternatives to green fluorescent protein in anaerobic contexts. Deep mutational scanning performs systematic profiling of protein sequence-function relationships but has not been applied to FbFPs. Focusing on CreiLOV from Chlamydomonas reinhardtii, we created and analyzed two comprehensive mutant collections: (1) single-residue, site-saturation mutagenesis libraries covering all 118 residues; and (2) a full combinatorial metagenesis library among 20 mutations at 15 residues, where mutation and residue selection was based on single-site mutagenesis results. Notably, the second type of library is indispensable to study higher-order epistasis but underrepresented in the literature. Using optimized FACS-seq assays, 2,185 (>92.5%) out of 2,360 possible single-site mutants and 165,428 (>89.7%) out of 184,320 possible combinatorial mutants were reliably assigned with fitness values. We constructed statistical and machine-learning models to analyze the CreiLOV data set, enabling accurate fitness prediction of higher-order mutants using lower-order mutagenesis data. In addition, we successfully isolated CreiLOV variants with improved fluorescence quantum yield and thermostability. This work provides new empirical data and design rules to engineer combinatorial protein variants.

Epistatic interaction between CsCEN and CsSHBY in regulating indeterminate/determinate growth of lateral branch in cucumber

Two genetic loci, det-ma (CsCEN) and det-lb, showed epistatic interaction on indeterminate/determinate growth of LB in cucumber. CsSHBY was identified as the candidate gene for det-lb locus. Plant architecture depends on the spatial regulation of meristems from both main axis (MA) and lateral branches (LBs). Fate (indeterminate or determinate) of these meristems is a crucial source of architectural diversity determining crop productivity and management. CENTRORADIALIS/TERMINAL FLOWER 1/SELF-PRUNING (CETS) gene family have been well known as pivotal regulators for indeterminate/determinate growth of MA. Nevertheless, genes that regulate LB indeterminacy/determinacy remained unclear. Cucumber (Cucumis sativus L.) has typical monopodial growth and multiple lateral branches. Both MA and LBs had indeterminate or determinate growth, and indeterminate/determinate growth of LB was controlled by two distinct loci, det-ma (CsCEN) and det-lb. In our study, based on bulked segregant analysis (BSA) method, the det-lb locus was mapped on a 60.6kb region on chromosome 1 harboring only one gene CsaV3_1G044330, which encoded a putative vacuolar-sorting protein (designated as CsSHBY). Multipoint mutations in CsSHBY were identified in D082 and D226, compared with CCMC, including nonsynonymous SNP mutations and a 6-bp deletion in exons. Further, qPCR showed that CsSHBY was highly expressed in lateral bud of CCMC, suggesting that CsSHBY might play an active role in regulating indeterminate/determinate growth of LB. Genetic analyses showed that det-ma (CsCEN) had an epistatic effect on det-lb (CsSHBY), and CsCEN could activate CsSHBY promoter by Dual luciferase and GUS activity assays. Meanwhile, Cscen or Csshby was found to influence auxin contents and CsYUCs and CsPINs expression levels. These findings provided new insights into precisely optimizing plant architecture for yield improvements.