Genome-wide association and epistatic interactions of flowering time in soybean cultivar

Genome-wide exploration of soybean domestication traits: integrating association mapping and SNP × SNP interaction analyses

Soybean domestication has been essential for crop evolution, adaptation, and modern breeding. Despite advancements in understanding soybean genetics, the genetic basis of DRTs has yet to be fully explored, particularly in the context of genome-wide association studies (GWASs) and gene interaction analyses (epistasis). This study evaluated 198 diverse soybean accessions using 23,574 high-quality SNPs obtained via ddRAD-seq. Nine key DRTs-including those related to seed size (length, width, and thickness), seed coat color, cotyledon color, hypocotyl color, stem growth habit, flower color, pod color, pubescence, and pod-shattering-were phenotyped in two environments. A GWASs conducted via the FarmCPU and BLINK models identified 78 significant SNPs, 14 consistently detected across both environments and models, demonstrating stability. Notably, the SNP rs.Gm16.29778273 linked to pod-shattering resistance. The functional annotation linked three known quantitative trait loci /genes and revealed 11 novel candidate genes associated with DRTs, providing insights into their roles via Gene Ontology (GO) terms. The main effect SNP × SNP interaction analysis revealed that the significant SNP rs.Gm13.16695800 exhibits a pleiotropic effect, controlling both hypocotyl and flower color. Furthermore, 324 epistatic interactions were identified, influencing the expression of DRTs, thereby highlighting the complex genetic architecture underlying these traits. These findings offer valuable insights into domestication and the traits linked to higher yield. They provide a solid foundation for developing marker-assisted selection (MAS) strategies and functional studies to improve soybean breeding for resilient, high-yielding varieties.

Genome-wide association study revealed some new candidate genes associated with flowering and maturity time of soybean in Central and West Siberian regions of Russia

The duration of flowering and maturity is an important agricultural trait determining the suitability of a variety for cultivation in the target region. In the present study, we used genome-wide association analysis (GWAS) to search for loci associated with soybean flowering and maturity in the Central and West Siberian regions of Russia. A field experiment was conducted in 2021/2022 at two locations (Orel and Novosibirsk). A germplasm collection of 180 accessions was genotyped using SoySNP50K Illumina Infinium Bead-Chip. From the initial collection, we selected 129 unrelated accessions and conducted GWAS on this dataset using two multi-locus models: FarmCPU and BLINK. As a result, we identified 13 loci previously reported to be associated with duration of soybean development, and 17 new loci. 33 candidate genes were detected in these loci using analysis of co-expression, gene ontology, and literature data, with the best candidates being Glyma.03G177500, Glyma.13G177400, and Glyma.06G213100. These candidate genes code the Arabidopis orthologs TOE1 (TARGET OF EAT 1), SPL3 (SQUAMOSA PROMOTER BINDING PROTEIN LIKE 3), the DELLA protein, respectively. In these three genes, we found haplotypes which may be associated with the length of soybean flowering and maturity, providing soybean adaptation to a northern latitudes.

Epistasis and pleiotropy-induced variation for plant breeding

Epistasis refers to nonallelic interaction between genes that cause bias in estimates of genetic parameters for a phenotype with interactions of two or more genes affecting the same trait. Partitioning of epistatic effects allows true estimation of the genetic parameters affecting phenotypes. Multigenic variation plays a central role in the evolution of complex characteristics, among which pleiotropy, where a single gene affects several phenotypic characters, has a large influence. While pleiotropic interactions provide functional specificity, they increase the challenge of gene discovery and functional analysis. Overcoming pleiotropy-based phenotypic trade-offs offers potential for assisting breeding for complex traits. Modelling higher order nonallelic epistatic interaction, pleiotropy and non-pleiotropy-induced variation, and genotype × environment interaction in genomic selection may provide new paths to increase the productivity and stress tolerance for next generation of crop cultivars. Advances in statistical models, software and algorithm developments, and genomic research have facilitated dissecting the nature and extent of pleiotropy and epistasis. We overview emerging approaches to exploit positive (and avoid negative) epistatic and pleiotropic interactions in a plant breeding context, including developing avenues of artificial intelligence, novel exploitation of large-scale genomics and phenomics data, and involvement of genes with minor effects to analyse epistatic interactions and pleiotropic quantitative trait loci, including missing heritability.

Genome-wide association study reveals GmFulb as candidate gene for maturity time and reproductive length in soybeans (Glycine max)

The ability of soybean [Glycine max (L.) Merr.] to adapt to different latitudes is attributed to genetic variation in major E genes and quantitative trait loci (QTLs) determining flowering time (R1), maturity (R8), and reproductive length (RL). Fully revealing the genetic basis of R1, R8, and RL in soybeans is necessary to enhance genetic gains in soybean yield improvement. Here, we performed a genome-wide association analysis (GWA) with 31,689 single nucleotide polymorphisms (SNPs) to detect novel loci for R1, R8, and RL using a soybean panel of 329 accessions with the same genotype for three major E genes (e1-as/E2/E3). The studied accessions were grown in nine environments and observed for R1, R8 and RL in all environments. This study identified two stable peaks on Chr 4, simultaneously controlling R8 and RL. In addition, we identified a third peak on Chr 10 controlling R1. Association peaks overlap with previously reported QTLs for R1, R8, and RL. Considering the alternative alleles, significant SNPs caused RL to be two days shorter, R1 two days later and R8 two days earlier, respectively. We identified association peaks acting independently over R1 and R8, suggesting that trait-specific minor effect loci are also involved in controlling R1 and R8. From the 111 genes highly associated with the three peaks detected in this study, we selected six candidate genes as the most likely cause of R1, R8, and RL variation. High correspondence was observed between a modifying variant SNP at position 04:39294836 in GmFulb and an association peak on Chr 4. Further studies using map-based cloning and fine mapping are necessary to elucidate the role of the candidates we identified for soybean maturity and adaptation to different latitudes and to be effectively used in the marker-assisted breeding of cultivars with optimal yield-related traits.

Genome-wide association study as a powerful tool for dissecting competitive traits in legumes

Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.

Molecular breeding for improvement of photothermal adaptability in soybean

Soybean (Glycine max (L.) Merr.) is a typical short-day and temperate crop that is sensitive to photoperiod and temperature. Responses of soybean to photothermal conditions determine plant growth and development, which affect its architecture, yield formation, and capacity for geographic adaptation. Flowering time, maturity, and other traits associated with photothermal adaptability are controlled by multiple major-effect and minor-effect genes and genotype-by-environment interactions. Genetic studies have identified at least 11 loci (E1-E4, E6-E11, and J) that participate in photoperiodic regulation of flowering time and maturity in soybean. Molecular cloning and characterization of major-effect flowering genes have clarified the photoperiod-dependent flowering pathway, in which the photoreceptor gene phytochrome A, circadian evening complex (EC) components, central flowering repressor E1, and FLOWERING LOCUS T family genes play key roles in regulation of flowering time, maturity, and adaptability to photothermal conditions. Here, we provide an overview of recent progress in genetic and molecular analysis of traits associated with photothermal adaptability, summarizing advances in molecular breeding practices and tools for improving these traits. Furthermore, we discuss methods for breeding soybean varieties with better adaptability to specific ecological regions, with emphasis on a novel strategy, the Potalaization model, which allows breeding of widely adapted soybean varieties through the use of multiple molecular tools in existing elite widely adapted varieties.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-023-01406-z.

Genome-Wide Association Studies in Sunflower: Towards Sclerotinia sclerotiorum and Diaporthe/Phomopsis Resistance Breeding

Diseases caused by necrotrophic fungi, such as the cosmopolitan Sclerotinia sclerotiorum and the Diaporthe/Phomopsis complex, are among the most destructive diseases of sunflower worldwide. The lack of complete resistance combined with the inefficiency of chemical control makes assisted breeding the best strategy for disease control. In this work, we present an integrated genome-wide association (GWA) study investigating the response of a diverse panel of sunflower inbred lines to both pathogens. Phenotypic data for Sclerotinia head rot (SHR) consisted of five disease descriptors (disease incidence, DI; disease severity, DS; area under the disease progress curve for DI, AUDPCI, and DS, AUDPCS; and incubation period, IP). Two disease descriptors (DI and DS) were evaluated for two manifestations of Diaporthe/Phomopsis: Phomopsis stem canker (PSC) and Phomopsis head rot (PHR). In addition, a principal component (PC) analysis was used to derive transformed phenotypes as inputs to a univariate GWA (PC-GWA). Genotypic data comprised a panel of 4269 single nucleotide polymorphisms (SNP), generated via genotyping-by-sequencing. The GWA analysis revealed 24 unique marker-trait associations for SHR, 19 unique marker-trait associations for Diaporthe/Phomopsis diseases, and 7 markers associated with PC1 and PC2. No common markers were found for the response to the two pathogens. Nevertheless, epistatic interactions were identified between markers significantly associated with the response to S. sclerotiorum and Diaporthe/Phomopsis. This suggests that, while the main determinants of resistance may differ for the two pathogens, there could be an underlying common genetic basis. The exploration of regions physically close to the associated markers yielded 364 genes, of which 19 were predicted as putative disease resistance genes. This work presents the first simultaneous evaluation of two manifestations of Diaporthe/Phomopsis in sunflower, and undertakes a comprehensive GWA study by integrating PSC, PHR, and SHR data. The multiple regions identified, and their exploration to identify candidate genes, contribute not only to the understanding of the genetic basis of resistance, but also to the development of tools for assisted breeding.

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction

The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support.

Core circadian clock and light signaling genes brought into genetic linkage across the green lineage

The circadian clock is conserved at both the level of transcriptional networks as well as core genes in plants, ensuring that biological processes are phased to the correct time of day. In the model plant Arabidopsis (Arabidopsis thaliana), the core circadian SHAQKYF-type-MYB (sMYB) genes CIRCADIAN CLOCK ASSOCIATED 1 (CCA1) and REVEILLE (RVE4) show genetic linkage with PSEUDO-RESPONSE REGULATOR 9 (PRR9) and PRR7, respectively. Leveraging chromosome-resolved plant genomes and syntenic ortholog analysis enabled tracing this genetic linkage back to Amborella trichopoda, a sister lineage to the angiosperm, and identifying an additional evolutionarily conserved genetic linkage in light signaling genes. The LHY/CCA1-PRR5/9, RVE4/8-PRR3/7, and PIF3-PHYA genetic linkages emerged in the bryophyte lineage and progressively moved within several genes of each other across an array of angiosperm families representing distinct whole-genome duplication and fractionation events. Soybean (Glycine max) maintained all but two genetic linkages, and expression analysis revealed the PIF3-PHYA linkage overlapping with the E4 maturity group locus was the only pair to robustly cycle with an evening phase, in contrast to the sMYB-PRR morning and midday phase. While most monocots maintain the genetic linkages, they have been lost in the economically important grasses (Poaceae), such as maize (Zea mays), where the genes have been fractionated to separate chromosomes and presence/absence variation results in the segregation of PRR7 paralogs across heterotic groups. The environmental robustness model is put forward, suggesting that evolutionarily conserved genetic linkages ensure superior microhabitat pollinator synchrony, while wide-hybrids or unlinking the genes, as seen in the grasses, result in heterosis, adaptation, and colonization of new ecological niches.

A Genome-Wide Association Study Coupled With a Transcriptomic Analysis Reveals the Genetic Loci and Candidate Genes Governing the Flowering Time in Alfalfa (Medicago sativa L.)

The transition to flowering at the right time is very important for adapting to local conditions and maximizing alfalfa yield. However, the understanding of the genetic basis of the alfalfa flowering time remains limited. There are few reliable genes or markers for selection, which hinders progress in genetic research and molecular breeding of this trait in alfalfa. We sequenced 220 alfalfa cultivars and conducted a genome-wide association study (GWAS) involving 875,023 single-nucleotide polymorphisms (SNPs). The phenotypic analysis showed that the breeding status and geographical origin strongly influenced the alfalfa flowering time. Our GWAS revealed 63 loci significantly related to the flowering time. Ninety-five candidate genes were detected at these SNP loci within 40 kb (20 kb up- and downstream). Thirty-six percent of the candidate genes are involved in development and pollen tube growth, indicating that these genes are key genetic mechanisms of alfalfa growth and development. The transcriptomic analysis showed that 1,924, 2,405, and 3,779 differentially expressed genes (DEGs) were upregulated across the three growth stages, while 1,651, 2,613, and 4,730 DEGs were downregulated across the stages. Combining the results of our GWAS and transcriptome analysis, in total, 38 candidate genes (7 differentially expressed during the bud stage, 13 differentially expressed during the initial flowering stage, and 18 differentially expressed during the full flowering stage) were identified. Two SNPs located in the upstream region of the Msa0888690 gene (which is involved in isop renoids) were significantly related to flowering. The two significant SNPs within the upstream region of Msa0888690 existed as four different haplotypes in this panel. The genes identified in this study represent a series of candidate targets for further research investigating the alfalfa flowering time and could be used for alfalfa molecular breeding.

A genome-wide scan of wastewater E. coli for genes under positive selection: focusing on mechanisms of antibiotic resistance

Antibiotic resistance is a global health threat and consequently, there is a need to understand the mechanisms driving its emergence. Here, we hypothesize that genes and mutations under positive selection may contribute to antibiotic resistance. We explored wastewater E. coli, whose genomes are highly diverse. We subjected 92 genomes to a statistical analysis for positively selected genes. We obtained 75 genes under positive selection and explored their potential for antibiotic resistance. We found that eight genes have functions relating to antibiotic resistance, such as biofilm formation, membrane permeability, and bacterial persistence. Finally, we correlated the presence/absence of non-synonymous mutations in positively selected sites of the genes with a function in resistance against 20 most prescribed antibiotics. We identified mutations associated with antibiotic resistance in two genes: the porin ompC and the bacterial persistence gene hipA. These mutations are located at the surface of the proteins and may hence have a direct effect on structure and function. For hipA, we hypothesize that the mutations influence its interaction with hipB and that they enhance the capacity for dormancy as a strategy to evade antibiotics. Overall, genomic data and positive selection analyses uncover novel insights into mechanisms driving antibiotic resistance.

A compressed variance component mixed model for detecting QTNs and QTN-by-environment and QTN-by-QTN interactions in genome-wide association studies

Although genome-wide association studies are widely used to mine genes for quantitative traits, the effects to be estimated are confounded, and the methodologies for detecting interactions are imperfect. To address these issues, the mixed model proposed here first estimates the genotypic effects for AA, Aa, and aa, and the genotypic polygenic background replaces additive and dominance polygenic backgrounds. Then, the estimated genotypic effects are partitioned into additive and dominance effects using a one-way analysis of variance model. This strategy was further expanded to cover QTN-by-environment interactions (QEIs) and QTN-by-QTN interactions (QQIs) using the same mixed-model framework. Thus, a three-variance-component mixed model was integrated with our multi-locus random-SNP-effect mixed linear model (mrMLM) method to establish a new methodological framework, 3VmrMLM, that detects all types of loci and estimates their effects. In Monte Carlo studies, 3VmrMLM correctly detected all types of loci and almost unbiasedly estimated their effects, with high powers and accuracies and a low false positive rate. In re-analyses of 10 traits in 1439 rice hybrids, detection of 269 known genes, 45 known gene-by-environment interactions, and 20 known gene-by-gene interactions strongly validated 3VmrMLM. Further analyses of known genes showed more small (67.49%), minor-allele-frequency (35.52%), and pleiotropic (30.54%) genes, with higher repeatability across datasets (54.36%) and more dominance loci. In addition, a heteroscedasticity mixed model in multiple environments and dimension reduction methods in quite a number of environments were developed to detect QEIs, and variable selection under a polygenic background was proposed for QQI detection. This study provides a new approach for revealing the genetic architecture of quantitative traits.

Identification of Chilling Accumulation-Associated Genes for Litchi Flowering by Transcriptome-Based Genome-Wide Association Studies

Litchi is an important Sapindaceae fruit tree. Flowering in litchi is triggered by low temperatures in autumn and winter. It can be divided into early-, medium-, and late-flowering phenotypes according to the time for floral induction. Early-flowering varieties need low chilling accumulation level for floral induction, whereas the late-flowering varieties require high chilling accumulation level. In the present study, RNA-Seq of 87 accessions was performed and transcriptome-based genome-wide association studies (GWAS) was used to identify candidate genes involved in chilling accumulation underlying the time for floral induction. A total of 98,155 high-quality single-nucleotide polymorphism (SNP) sites were obtained. A total of 1,411 significantly associated SNPs and 1,115 associated genes (AGs) were identified, of which 31 were flowering-related, 23 were hormone synthesis-related, and 27 were hormone signal transduction-related. Association analysis between the gene expression of the AGs and the flowering phenotypic data was carried out, and differentially expressed genes (DEGs) in a temperature-controlled experiment were obtained. As a result, 15 flowering-related candidate AGs (CAGs), 13 hormone synthesis-related CAGs, and 11 hormone signal transduction-related CAGs were further screened. The expression levels of the CAGs in the early-flowering accessions were different from those in the late-flowering ones, and also between the flowering trees and non-flowering trees. In a gradient chilling treatment, flowering rates of the trees and the CAGs expression were affected by the treatment. Our present work for the first time provided candidate genes for genetic regulation of flowering in litchi using transcriptome-based GWAS.

Doing Genetic and Genomic Biology Using the Legume Information System and Associated Resources

In this chapter, we introduce the main components of the Legume Information System ( https://legumeinfo.org ) and several associated resources. Additionally, we provide an example of their use by exploring a biological question: is there a common molecular basis, across legume species, that underlies the photoperiod-mediated transition from vegetative to reproductive development, that is, days to flowering? The Legume Information System (LIS) holds genetic and genomic data for a large number of crop and model legumes and provides a set of online bioinformatic tools designed to help biologists address questions and tasks related to legume biology. Such tasks include identifying the molecular basis of agronomic traits; identifying orthologs/syntelogs for known genes; determining gene expression patterns; accessing genomic datasets; identifying markers for breeding work; and identifying genetic similarities and differences among selected accessions. LIS integrates with other legume-focused informatics resources such as SoyBase ( https://soybase.org ), PeanutBase ( https://peanutbase.org ), and projects of the Legume Federation ( https://legumefederation.org ).

Identification of Flowering Regulatory Networks and Hub Genes Expressed in the Leaves of Elymus sibiricus L. Using Comparative Transcriptome Analysis

Flowering is a significant stage from vegetative growth to reproductive growth in higher plants, which impacts the biomass and seed yield. To reveal the flowering time variations and identify the flowering regulatory networks and hub genes in Elymus sibiricus, we measured the booting, heading, and flowering times of 66 E. sibiricus accessions. The booting, heading, and flowering times varied from 136 to 188, 142 to 194, and 148 to 201 days, respectively. The difference in flowering time between the earliest- and the last-flowering accessions was 53 days. Furthermore, transcriptome analyses were performed at the three developmental stages of six accessions with contrasting flowering times. A total of 3,526 differentially expressed genes (DEGs) were predicted and 72 candidate genes were identified, including transcription factors, known flowering genes, and plant hormone-related genes. Among them, four candidate genes (LATE, GA2OX6, FAR3, and MFT1) were significantly upregulated in late-flowering accessions. LIMYB, PEX19, GWD3, BOR7, PMEI28, LRR, and AIRP2 were identified as hub genes in the turquoise and blue modules which were related to the development time of flowering by weighted gene co-expression network analysis (WGCNA). A single-nucleotide polymorphism (SNP) of LIMYB found by multiple sequence alignment may cause late flowering. The expression pattern of flowering candidate genes was verified in eight flowering promoters (CRY, COL, FPF1, Hd3, GID1, FLK, VIN3, and FPA) and four flowering suppressors (CCA1, ELF3, Ghd7, and COL4) under drought and salt stress by qRT-PCR. The results suggested that drought and salt stress activated the flowering regulation pathways to some extent. The findings of the present study lay a foundation for the functional verification of flowering genes and breeding of new varieties of early- and late-flowering E. sibiricus.

Genotyping Platforms for Genome-Wide Association Studies: Options and Practical Considerations

Genome-wide association studies (GWAS) in crops requires genotyping platforms that are capable of producing accurate high density genotyping data on hundreds of plants in a cost-effective manner. Currently there are multiple commercial platforms available that are being effectively used across crops. These platforms include genotyping arrays such as the Illumina Infinium arrays and the Applied Biosystems Axiom Arrays along with a variety of resequencing methods. These methods are being used to genotype tens of thousands of markers up to millions of markers on GWAS panels. They are being used on crops with simple genomes to crops with very complex, large, polyploid genomes. Depending on the crop and the goal of the GWAS, there are several options and practical considerations to take into account when selecting a genotyping technology to ensure that the right coverage, accuracy, and cost for the study is achieved.

MACHINE LEARNING FOR DETECTING EPISTASIS INTERACTIONS AND ITS RELEVANCE TO PERSONALIZED MEDICINE IN ALZHEIMER’S DISEASE: SYSTEMATIC REVIEW

Alzheimer’s disease (AD) is a progressive disease that attacks the brain’s neurons and causes problems in memory, thinking, and reasoning skills. Personalized Medicine (PM) needs a better and more accurate understanding of the relationship between human genetic data and complex diseases like AD. The goal of PM is to tailor the treatment of a case person to his individual properties. PM requires the prediction of a person’s disease from genetic data, and its success depends on the accurate detection of genetic biomarkers. Single Nucleotide polymorphisms (SNPs) are considered the most prevalent type of variation in the human genome. Epistasis has a biological relevance to complex diseases and has an important impact on PM. Detection of the most significant epistasis interactions associated with complex diseases is a big challenge. This paper reviews several machine learning techniques and algorithms to detect the most significant epistasis interactions in Alzheimer’s disease. We discuss many machine learning techniques that can be used for detecting SNPs’ combinations like Random Forests, Support Vector Machines, Multifactor Dimensionality Reduction, Neural Network, and Deep Learning. This review paper highlights the pros and cons of these techniques and explains how they can be applied in an efficient framework to apply knowledge discovery and data mining in AD disease.

MIDESP: Mutual Information-Based Detection of Epistatic SNP Pairs for Qualitative and Quantitative Phenotypes

Simple Summary

The interactions between SNPs, which are known as epistasis, can strongly influence the phenotype. Their detection is still a challenge, which is made even more difficult through the existence of background associations that can hide correct epistatic interactions. To address the limitations of existing methods, we present in this study our novel method MIDESP for the detection of epistatic SNP pairs. It is the first mutual information-based method that can be applied to both qualitative and quantitative phenotypes and which explicitly accounts for background associations in the dataset.

Abstract

The interactions between SNPs result in a complex interplay with the phenotype, known as epistasis. The knowledge of epistasis is a crucial part of understanding genetic causes of complex traits. However, due to the enormous number of SNP pairs and their complex relationship to the phenotype, identification still remains a challenging problem. Many approaches for the detection of epistasis have been developed using mutual information (MI) as an association measure. However, these methods have mainly been restricted to case–control phenotypes and are therefore of limited applicability for quantitative traits. To overcome this limitation of MI-based methods, here, we present an MI-based novel algorithm, MIDESP, to detect epistasis between SNPs for qualitative as well as quantitative phenotypes. Moreover, by incorporating a dataset-dependent correction technique, we deal with the effect of background associations in a genotypic dataset to separate correct epistatic interaction signals from those of false positive interactions resulting from the effect of single SNP×phenotype associations. To demonstrate the effectiveness of MIDESP, we apply it on two real datasets with qualitative and quantitative phenotypes, respectively. Our results suggest that by eliminating the background associations, MIDESP can identify important genes, which play essential roles for bovine tuberculosis or the egg weight of chickens.

Identification of Key Metabolic Pathways and Biomarkers Underlying Flowering Time of Guar (Cyamopsis tetragonoloba (L.) Taub.) via Integrated Transcriptome-Metabolome Analysis

Guar (Cyamopsis tetragonoloba (L.) Taub.) is an annual legume crop native to India and Pakistan. Seeds of the plant serve as a source of galactomannan polysaccharide (guar gum) used in the food industry as a stabilizer (E412) and as a gelling agent in oil and gas fracturing fluids. There were several attempts to introduce this crop to countries of more northern latitudes. However, guar is a plant of a short photoperiod, therefore, its introduction, for example, to Russia is complicated by a long day length during the growing season. Breeding of new guar varieties insensitive to photoperiod slowed down due to the lack of information on functional molecular markers, which, in turn, requires information on guar genome. Modern breeding strategies, e.g., genomic predictions, benefit from integration of multi-omics approaches such as transcriptome, proteome and metabolome assays. Here we present an attempt to use transcriptome-metabolome integration to understand the genetic determination of flowering time variation among guar plants that differ in their photoperiod sensitivity. This study was performed on nine early- and six delayed-flowering guar varieties with the goal to find a connection between 63 metabolites and 1,067 differentially expressed transcripts using Shiny GAM approach. For the key biomarker of flowering in guar myo-inositol we also evaluated the KEGG biochemical pathway maps available for Arabidopsis thaliana. We found that the phosphatidylinositol signaling pathway is initiated in guar plants that are ready for flowering through the activation of the phospholipase C (PLC) gene, resulting in an exponential increase in the amount of myo-inositol in its free form observed on GC-MS chromatograms. The signaling pathway is performed by suppression of myo-inositol phosphate kinases (phosphorylation) and alternative overexpression of phosphatases (dephosphorylation). Our study suggests that metabolome and transcriptome information taken together, provide valuable information about biomarkers that can be used as a tool for marker-assisted breeding, metabolomics and functional genomics of this important legume crop.

Genome-wide association coupled gene to gene interaction studies unveil novel epistatic targets among major effect loci impacting rice grain chalkiness

Rice varieties whose quality is graded as excellent have a lower percent grain chalkiness (PGC) of two per cent and below with higher whole grain yields upon milling, leading to higher economic returns for farmers. We have conducted a genome-wide association study (GWAS) using a combined population panel of indica and japonica rice varieties, and identified a total of 746 single nucleotide polymorphisms (SNPs) that were strongly associated with the chalk phenotype, covered 78 Quantitative Trait Loci (QTL) regions. Among them, 21 were high-value QTLs, as they explained at least 10 % of the phenotypic variance for PGC. A combined epistasis and GWAS was applied to dissect the genetics of the complex chalkiness trait, and its regulatory cascades were validated using gene regulatory networks. Promising novel epistatic interactions were found between the loci of chromosomes 6 (PGC6.1) and 7 (PGC7.8) that contributed to lower PGC. Based on haplotype mining only a few modern rice varieties confounded with a lower chalkiness, and they possess several PGC QTLs. The importance of PGC6.1 was validated through multi-parent advanced generation intercrosses and several low-chalk lines possessing superior haplotypes were identified. The results of this investigation have deciphered the underlying genetic networks that can reduce PGC to 2%, and will thus support future breeding programs to improve the grain quality of elite genetic material with high-yielding potentials.

QTL Mapping and Candidate Gene Analysis for Pod Shattering Tolerance in Soybean (Glycine max)

Pod shattering is an important reproductive process in many wild species. However, pod shattering at the maturing stage can result in severe yield loss. The objectives of this study were to discover quantitative trait loci (QTLs) for pod shattering using two recombinant inbred line (RIL) populations derived from an elite cultivar having pod shattering tolerance, namely "Daewonkong", and to predict novel candidate QTL/genes involved in pod shattering based on their allele patterns. We found several QTLs with more than 10% phenotypic variance explained (PVE) on seven different chromosomes and found a novel candidate QTL on chromosome 16 (qPS-DS16-1) from the allele patterns in the QTL region. Out of the 41 annotated genes in the QTL region, six were found to contain SNP (single-nucleotide polymorphism)/indel variations in the coding sequence of the parents compared to the soybean reference genome. Among the six potential candidate genes, Glyma.16g076600, one of the genes with known function, showed a highly differential expression levels between the tolerant and susceptible parents in the growth stages R3 to R6. Further, Glyma.16g076600 is a homolog of AT4G19230 in Arabidopsis, whose function is related to abscisic acid catabolism. The results provide useful information to understand the genetic mechanism of pod shattering and could be used for improving the efficiency of marker-assisted selection for developing varieties of soybeans tolerant to pod shattering.