Identification of epistasis loci underlying rice flowering time by controlling population stratification and polygenic effect

Ahsan, Asif; Monir, Mamun; Meng, Xianwen; Rahaman, Md. Matiur; Chen, Hongjun; Chen, Ming

doi:10.1093/dnares/dsy043

Cited by 20 publications

(22 citation statements)

References 72 publications

(93 reference statements)

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“…Our mapping results (main effect and epistatic) showed that both unique and common loci underlie FLT variation under long and short photoperiod ( Figure 1 ; Figures S4–S8 ). Epistatic loci underlie FLT in both selfing (Komeda, 2004; Juenger et al, 2005; Huang et al, 2013; Chen et al, 2018b; Li et al, 2018a; Mathew et al, 2018) and outcrossing (Buckler et al, 2009; Durand et al, 2012) species. In addition, the effect size of FLT loci differs between selfing and out crossing species as QTL effect sizes are large in the former (Lin et al, 1995; Maurer et al, 2015) and small in the later (Buckler et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

“…The joint effect of alleles at these loci may be lower or higher than the total effects of the loci (Johnson, 2008). In selfing species, epistasis is common due to high level of homozygosity (Volis et al, 2010) and epistatic interactions have been found among loci underlying flowering time in barley (Mathew et al, 2018), rice (Chen et al, 2015; Chen et al, 2018b), and sorghum (Li et al, 2018a). Although, theoretical models and empirical studies involving simulations have suggested the significant role for epistasis in breeding (Melchinger et al, 2007; Volis et al, 2010; Messina et al, 2011; Howard et al, 2014), empirical evidence from practical breeding are limited.…”

Section: Introductionmentioning

confidence: 99%

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Epistasis Detection and Modeling for Genomic Selection in Cowpea (Vigna unguiculata L. Walp.)

Olatoye

Aikpokpodion

2019

Front. Genet.

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Genetic architecture reflects the pattern of effects and interaction of genes underlying phenotypic variation. Most mapping and breeding approaches generally consider the additive part of variation but offer limited knowledge on the benefits of epistasis which explains in part the variation observed in traits. In this study, the cowpea multiparent advanced generation inter-cross (MAGIC) population was used to characterize the epistatic genetic architecture of flowering time, maturity, and seed size. In addition, consideration for epistatic genetic architecture in genomic-enabled breeding (GEB) was investigated using parametric, semi-parametric, and non-parametric genomic selection (GS) models. Our results showed that large and moderate effect–sized two-way epistatic interactions underlie the traits examined. Flowering time QTL colocalized with cowpea putative orthologs of Arabidopsis thaliana and Glycine max genes like PHYTOCLOCK1 ( PCL1 [Vigun11g157600]) and PHYTOCHROME A ( PHY A [Vigun01g205500]). Flowering time adaptation to long and short photoperiod was found to be controlled by distinct and common main and epistatic loci. Parametric and semi-parametric GS models outperformed non-parametric GS model, while using known quantitative trait nucleotide(s) (QTNs) as fixed effects improved prediction accuracy when traits were controlled by large effect loci. In general, our study demonstrated that prior understanding of the genetic architecture of a trait can help make informed decisions in GEB.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Epistasis Detection and Modeling for Genomic Selection in Cowpea (Vigna unguiculata L. Walp.)

Olatoye

Aikpokpodion

2019

Front. Genet.

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“…For example, three candidate genes including Nsn1, Fpa, and Zmm22 were identified in 942 maize samples (Zea mays) [21]; two candidate genes, CO1 and BFL, were identified in using 218 barley samples (Hordeum vulgare) [22]; eight candidate genes including Hd1 were confirmed in 950 rice samples (Oryza sativa) [23]; and ten candidate genes including SOC1, AGL6, and ELF8 were reported in 309 soybean samples (Glycine max) [13]. These findings have presented valuable information to various breeding programs focused on DTF, but have a limitation to further improve DTF, because they are not sufficient for explaining all of the phenotypic variations in DTF such as interaction effects between markers [24].…”

Section: Introductionmentioning

confidence: 96%

“…Epistasis is defined as the interaction between genes or SNP markers that influences a trait [25]. Each SNP marker above a significant level in GWAS has a strong effect on the determination of a trait, but non-significant markers that interact with each other could also have a large influence on the trait [24]. Therefore, considering epistatic interactions for multi-variant nonadditive effects, enables to discover more markers associated with traits, together with GWAS on single-variant-additive-effects [26].…”

Section: Introductionmentioning

confidence: 99%

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

Kim

Lim

et al. 2020

PLoS ONE

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Genome-wide association studies (GWAS) have enabled the discovery of candidate markers that play significant roles in various complex traits in plants. Recently, with increased interest in the search for candidate markers, studies on epistatic interactions between single nucleotide polymorphism (SNP) markers have also increased, thus enabling the identification of more candidate markers along with GWAS on single-variant-additive-effect. Here, we focused on the identification of candidate markers associated with flowering time in soybean (Glycine max). A large population of 2,662 cultivated soybean accessions was genotyped using the 180k Axiom ® SoyaSNP array, and the genomic architecture of these accessions was investigated to confirm the population structure. Then, GWAS was conducted to evaluate the association between SNP markers and flowering time. A total of 93 significant SNP markers were detected within 59 significant genes, including E1 and E3, which are the main determinants of flowering time. Based on the GWAS results, multilocus epistatic interactions were examined between the significant and non-significant SNP markers. Two significant and 16 non-significant SNP markers were discovered as candidate markers affecting flowering time via interactions with each other. These 18 candidate SNP markers mapped to 18 candidate genes including E1 and E3, and the 18 candidate genes were involved in six major flowering pathways. Although further biological validation is needed, our results provide additional information on the existing flowering time markers and present another option to marker-assisted breeding programs for regulating flowering time of soybean.

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“…In the area of quantitative genetics, it is well known that complex traits are controlled by multiple genes, epistasis, and gene-environment interactions [9][10][11]. Therefore, multiple genetic variants 3 and environmental modulators may control BSA.…”

Section: Introductionmentioning

confidence: 99%

Genome-Wide Conditional Association Study Reveals the Influences of Lifestyle Cofactors on Genetic Regulation of Body Surface Area in MESA Population

Khatun

Monir

et al. 2020

Preprint

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Backgrounds Body surface area (BSA) is an important trait used for many clinical purposes and is associated with a variety of diseases including cardiovascular diseases and cancer. People's BSA may vary due to genetic background, race, and different lifestyle factors (such as walking, exercise, reading, smoking, transportation, etc.). Genome-wide association study of BSA was conducted on 5,336 subjects of four ethnic populations of European-American, African-American, Hispanic-American, and Chinese-American from MESA (The Multi-Ethnic Study of Atherocloris) data using unconditional and conditional full genetic models for analyzing genetic effects of additive, dominance, epistasis, and genetic by ethnicity interactions.Results Conditional association analyses revealed that lifestyle cofactors could affect the genetic effects of genes that regulate BSA. Moreover, impacts of the lifestyle cofactors on BSA could depend on the genotypes of several SNPs, and ethnicity of individuals. In this study, fifteen SNPs were identified with highly significant (Experiment-wise PEW < 1×10–5) genetic effects using unconditional full genetic model, of which thirteen SNPs had individual genetic effects and seven SNPs were involved in four pairs of epistasis interactions. Seven single SNPs and eight pairs of epistasis SNPs were additionally identified using exercise, smoking, and transportation cofactor-conditional models. Estimated heritabily was 72.88% using unconditional model and 74.85 ~ 79.87% using lifestyle cofactor-conditional models. It was revealed that lifestyle cofactors could contribute, suppress, increase or decrease the genetic effects of BSA associated genes. From gene ontology analysis, it was observed that several genes are related to the metabolic pathway of calcium compounds, a main compound in several diseases related to obesity, coronary artery disease, type-2 Diabetes, Alzheimer disease, childhood obesity, sleeping duration, Parkinson disease, and cancer.Conclusions In summary, our study provides novel insights into the genetic mechanism of BSA in MESA population, and influences of different lifestyle cofactors on the genetic effects of BSA associated loci.

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Identification of epistasis loci underlying rice flowering time by controlling population stratification and polygenic effect

Cited by 20 publications

References 72 publications

Epistasis Detection and Modeling for Genomic Selection in Cowpea (Vigna unguiculata L. Walp.)

Epistasis Detection and Modeling for Genomic Selection in Cowpea (Vigna unguiculata L. Walp.)

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

Genome-Wide Conditional Association Study Reveals the Influences of Lifestyle Cofactors on Genetic Regulation of Body Surface Area in MESA Population

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