Mapping QTLs with epistatic effects and QTL×environment interactions for plant height using a doubled haploid population in cultivated wheat

Quantitative trait loci （QTLs） for plant height in wheat （Triticum aestivum L.） were studied using a set of 168 doubled haploid （DH） lines, which were derived from the cross Huapei 3/Yumai 57. A genetic linkage map was constructed using 283 SSR and 22 EST-SSR markers. The DH population and the parents were evaluated for wheat plant height in 2005 and 2006 in Tai＇an and 2006 in Suzhou. QTL analyses were performed using the software of QTLNetwork version 2.0 based on the mixed linear model. Four additive QTLs and five pairs of epistatic effects were detected, which were distributed on chromosomes 3A, 4B, 4D, 5A, 6A, 7B, and 7D. Among them, three additive QTLs and three pairs of epistatic QTLs showed QTL×environment interactions （QEs）. Two major QTLs, Qph4B and Qph4D, which accounted for 14.51% and 20.22% of the phenotypic variation, were located similar to the reported locations of the dwarfing genes Rhtl and Rht2, respectively. The Qph3A-2 with additive effect was not reported in previous linkage mapping studies. The total QTL effects detected for the plant height explained 85.04% of the phenotypic variation, with additive effects 46.07%, epistatic effects 19.89%, and QEs 19.09%. The results showed that both additive effects and epistatic effects were important genetic bases of wheat plant height, which were subjected to environmental modifications, and caused dramatic changes in phenotypic effects. The information obtained in this study will be useful for manipulating the QTLs for wheat plant height by molecular marker-assisted selection （MAS）.

Genetic association and epistatic interaction of the interleukin-10 signaling pathway in pediatric inflammatory bowel disease

AIM To study the genetic association and epistatic interaction of the interleukin(IL)-10 and IL-10/STAT3 pathways in pediatric inflammatory bowel disease(IBD). METHODS A total of 159 pediatric inflammatory IBD patients(Crohn's disease,n = 136; ulcerative colitis,n = 23) and 129 matched controls were studied for genetic association of selected single nucleotide polymorphisms(SNPs) of the IL-10 gene and the genes IL10 RA,IL10 RB,STAT3,and HO1,from the IL-10/STAT3 signaling pathway. As interactions between SNPs from different loci may significantly affect the associated risk for disease,additive(a) and dominant(d) modeling of SNP interactions was also performed to examine highorder epistasis between combinations of the individual SNPs. RESULTS The results showed that IL-10 rs304496 was associated with pediatric IBD(P = 0.022),but no association was found for two other IL-10 SNPs,rs1800872 and rs2034498,or for SNPs in genes IL10 RA,IL10 RB,STAT3,and HO1. However,analysis of epistatic interaction among these genes showed significant interactions:(1) between two IL-10 SNPs rs1800872 and rs3024496(additive-additive P = 0.00015,Bonferroni P value(Bp) = 0.003);(2) between IL-10 RB rs2834167 and HO1 rs2071746(dominant-additive,P = 0.0018,Bp = 0.039); and(3) among IL-10 rs1800872,IL10 RB rs2834167,and HO1 rs2071746(additivedominant-additive,P = 0.00015,Bp = 0.005),as well as weak interactions among IL-10 rs1800872,IL-10 rs3024496,and IL-10RA(additive-additive-additive,P = 0.003; Bp = 0.099),and among IL10 RA,IL10 RB,and HO1 genes(additive-dominant-additive,P = 0.008,Bp = 0.287).CONCLUSION These results indicate that both the IL-10 gene itself,and through epistatic interaction with genes within the IL-10/STAT3 signaling pathway,contribute to the risk of pediatric IBD.

QTL Mapping for Wheat Flour Color with Additive,Epistatic,and QTL×Environmental Interaction Effects

To investigate genetic factors affecting wheat flour color traits,a linkage map was constructed using a recombinant inbred line （RIL） population derived from Jing 771×Pm 97034.Main,epistatic and QTL×environment （QE） interaction effects of quantitative trait loci （QTLs） controlling wheat flour color were studied by the mixed linear modeling of data collected from wheat RIL plants under three different environmental conditions.13 QTLs with additive effects and 55 pairs of QTLs with epistatic effects were detected for wheat flour color traits.The additive-additive interactions （AA） involved all of the wheat chromosomes except 3D.Epistasis accounted for more of the observed phenotypic variation than did the main effect QTLs （M-QTLs）.Our results suggested that dual-locus interactions are widespread in the wheat genome and play a critical role in determining wheat flour color characteristics.In this study,3 QTLs were identified to have QE interaction effects,one of them showing significant QE interaction in E2 environment.

QTL Mapping for Stalk Related Traits in Maize (Zea mays L.) Under Different Densities

Stalk related traits, comprising plant height （PH）, ear height （EH）, internode number （IN）, average internode length （ALL）, stalk diameter （SD）, and ear height coefficient （EHC）, are significantly correlated with yield, density tolerance, and lodging resistance in maize. To investigate the genetic basis for stalk related traits, a doubled haploid （DH） population derived from a cross between NX531 and NX110 were evauluated under two densities over 2 yr. The additive quantitative trait loci （QTLs）, epistatic QTLs were detected using inclusive composite interval mapping and QTL-by-environment interaction were detected using mixed linear model. Differences between the two densities were significant for the six traits in the DH population. A linkage map that covered 1 721.19 cM with an average interval of 10.50 cM was constructed with 164 simple sequence repeat （SSR）. Two, two, seven, six, two, and eight additive QTLs for PH, IN, AIL, EH, SD, and EHC, respectively. The extend of their contribution to penotypic variation ranged from 10.10 to 31.93%. Seven QTLs were indentified simultaneously under both densities. One pair, two pairs and one pair of epistatic effects were detected for AIL, SD and EHC, respectively. No epistatic effects were detected for PH, EH, and IN. Nineteen QTLs with environment interactions were detected and their contribution to phenotypic variation ranged from 0.43 to 1.89%. Some QTLs were stably detected under different environments or genetic backgrounds comparing with previous studies. These QTLs could be useful for genetic improvement of stalk related traits in maize breeding.

Dissection of the genetic architecture for tassel branch number by QTL analysis in two related populations in maize

Tassel branch number （TBN） is the principal component of maize tassel inflorescence architecture and is a typical quan- titative trait controlled by multiple genes. The main objective of this research was to detect quantitative trait loci （QTLs） for TBN. The maize inbred line SICAU1212 was used as the common parent to develop BC1S1 and recombinant inbred line （RIL） populations with inbred lines 3237 and B73, respectively. The two related populations consisted of 123 and 238 lines, respectively. Each population was grown and phenotyped for TBN in two environments. Eleven QTLs were detected in the BC1S1 population, located on chromosomes 2, 3, 5, and 7, accounted for 4.45-26.58% of the phenotypic variation. Two QTLs （qB11Jtbn2-1, qB12Ctbn2-1, qBJtbn2-1; q11JBtbn5-1, qB12Ctbn5-1, qBJtbn5-1） that accounted for more than 10% of the phenotypic variation were identified. Three QTLs located on chromosomes 2, 3 and 5, exhibited stable expres- sion in the two environments. Ten QTLs were detected in the RIL population, located on chromosomes 2, 3, 5, 8, and 10, accounted for 2.69-13.58% of the TBN variation. One QTL （qR14Dtbn2-2） explained 〉10% of the phenotypic variation. One common QTL （qB12Ctbn2-2, qR14Dtbn2-2, qRJtbn2-2） was detected between the two related populations. Three pairs of epistatic effects were identified between two loci with or without additive effects and accounted for 1.19-4.26% of the phenotypic variance. These results demonstrated that TBN variation was mainly caused by major effects, minor effects and slightly modified by epistatic effects. Thus, identification of QTL for TBN may help elucidate the genetic basis of TBN and also facilitate map-based cloning and marker-assisted selection （MAS） in maize breeding programs.

QTL mapping for leaf area in maize (Zea mays L.) under multienvironments

Leaves are the main organs of photosynthesis in green plants. Leaf area plays a vital role in dry matter accumulation and grain yield in maize （Zea mays L.）. Thus, investigating the genetic basis of leaf area will aid efforts to breed maize with high yield. In this study, a total of 150 F7 recombinant inbred lines （RILs） derived from a cross between the maize lines Xu 178 and K12 were used to evaluate three ear-leaves area （TELA） under multi-environments. Inclusive composite interval map- ping （ICIM） was used to identify quantitative trait loci （QTLs） for TELA under a single environment and estimated breeding value （EBV）. A total of eight QTLs were detected under a single environmental condition, and four QTLs were identified for EBV which also can be detected in single environment. This indicated that the EBV-detected QTLs have high genetic stability. A major QTL （qTELA_2-9） located in chromosome bin 2.04/2.05 could be detected in four environments and has a high phenotypic contribution rate （ranging from 10.79 to 16.51%） that making it a good target for molecular breeding. In addition, joint analysis was used to reveal the genetic basis of leaf area in six environments. In total, six QTLxenvironment interactions and nine epistatic interactions were identified. Our results reveal that the genetic basis of the leaf area is not only mainly determined by additive effects, but also affected by epistatic effects environmental interaction effects.

QTL Analysis of Yield Components in Rice Using a Cheongcheong/Nagdong Doubled Haploid Genetic Map

In this study, only two of 12 quantitative trait loci (QTLs) affecting yield and yield components were identified in a single year, indicating that individual QTLs are probably sensitive to the environment. A rice growth survey of “Cheongcheong” and “Nag dong” in a doubled haploid population in 2012 revealed that yield capacity was influenced by climate change. Analysis of yield and yield components indicated that five average traits are high in “Cheongcheong”. Frequency distribution tables indicated that panicles per plant (PPP), spike lets per panicle (SPP), and 1000-grain weight (TGW) were normally distributed. The strongest relationship was identified between SPP and seed set percentage (SSP) among phenotypic correlations related to yield and yield components found on chromosomes 2, 3, 6, 8 in 2012. SPP and SSP was a very relevant requisite about quantity. Analysis of QTL about quantity was total 9. In the present study, a doubled haploid population was used to analyze the epistatic effects on yield and yield components in rice. Although other epistatic QTLs were not included in any of the main-effect QTLs, they significantly influenced the traits. These results indicated that epistatic interaction plays an important role in controlling the expression of complex traits. Thus, the utilization of marker-assisted selection in rice breeding programs should take epistatic effects into consideration. Hence, the QTLs responsible for major effects are more suitable for marker-assisted selection programs to improve yield and related traits across different environments.

Multi-Environmental Genetic Analysis of Grain Size Traits Based on Chromosome Segment Substitution Line in Rice(Oryza sativa L.)

Grain size traits are critical agronomic traits which directly determine grain yield,but the genetic bases of these traits are still not well understood.In this study,a total of 154 chromosome segment substitution lines(CSSLs)population derived from a cross between a japonica variety Koshihikari and an indica variety Nona Bokra was used to investigate grain length(GL),grain width(GW),length-width ratio(LWR),grain perimeter(GP),grain area(GA),and thousand grain weight(TGW)under four environments.QTL mapping analysis of six grain size traits was performed by QTL IciMapping 4.2 with an inclusive composite interval mapping(ICIM)model.A total of 64 QTLs were identified for these traits,which mapped to chromosomes 1,2,3,4,6,7,8,10,11,and 12 and accounted for 1.6%–27.1%of the total phenotypic variations.Among these QTLs,thirty-six loci were novel and seven QTLs were identified under four environments.One locus containing the known grain size gene,qGL3/GL3.1/OsPPKL1,also have been found.Moreover,five pairs of digenic epistatic interactions were identified except for GL and GP.These findings will facilitate fine mapping of the candidate gene and QTL pyramiding to genetically improve grain yield in rice.

Triple test cross analysis for seed yield and its components in sesame under water stress conditions

Water deficit is a limiting factor in sesame cultivars yield,thus identification of genetic mechanisms of sesame traits under water stress conditions is essential to development of water stress tolerant genotypes.Hence,the triple test cross(TTC)analysis in F_(2) population of the sesame cross(NA_(76)NA_(54))was used.Since,30 TTC families with their 13 respective parents were evaluated during the summer season 2020 under two irrigation treatments.i.e.,normal irrigation with 5952.38 m^(3) hm^(-2) of applied water,and water stress with 2976.19 m^(3) hm^(-2) of applied water.This was implemented at the experimental field of Kafr El Hamam/Sharkia,Agricultural Research Station,Agricultural Research Center(ARC),Egypt.Additive(D)and dominance(H)gene actions as well as epistatic and its two components of additiveadditive,additivedominance plus dominancedominance were involved in the inheritance of all studied traits under both irrigation treatments and their combined analysis.The degree of dominance(H/D)^(0.5) verified the presence of partial dominance in most cases.The correlation coefficient between sums(additive)and differences(dominance)was non-significant,showing that dominant genes between lines were ambidirectional.The moderately water stress tolerant TTC families as the best selected families were found in families 22,25,10,12,and 15.Therefore,postponing selection in these selected families to later segregating generations for all studied traits would be effective to exploit the positive effects of additiveadditive epistasis.

Analysis of High-Resolution QTL Markers Associated with Rice Yields Using Data for Two Consecutive Years in Different Environmental Conditions

Previously we reported the identification of seven quantitative trait loci (QTLs) associated with the rice yield measuring five parameters including panicles per plant (PPP), spikelets per panicle (SPP), seed set percentage (SSP), 1000-grain weight (TGW) and yield in 2012. Here we report the analysis of QTLs using the same trait parameters data of the mapping population in 2013 for detecting highly conserved QTL markers. A total of 6 QTLs were identified from chromosomes 1, 7, 8, 10, 11, and 12, which were contrasted with our previous results (chromosomes 1, 2, 4, 5, 6, 8, and 11). In this comparison, three QTLs from chromosome 1, 8, and 11 were only found to be associated with the components of yield over two consecutive years indicating high sensitivity of QTL markers to the environment. Of those three QTLs, SPP-associated marker RM12285 was found to be dominantly expressed by real-time PCR (qPCR). In addition, compared to our previous report the numbers of mapping population and markers were significantly increased for higher resolution markers from 70 to 120, and from 143 to 217, respectively. We also found that the parameter SPP was dominantly correlated with the rice yield. Furthermore, the double haploid (DH) population facilitated to analyze the epistatic effects for yield and yield components in rice. Taken together, combining multiple mapping population data over years possibly enables narrowing down to the highly conserved QTL markers against diverse environmental fluctuation caused by such as drought and high temperature. Thus, these data would be critically exploited to improve for the crop breeding strategy.

Genome-Wide Interaction-Based Association of Human Diseases—A Survey

Genome-Wide Association Studies（GWASs） aim to identify genetic variants that are associated with disease by assaying and analyzing hundreds of thousands of Single Nucleotide Polymorphisms（SNPs）. Although traditional single-locus statistical approaches have been standardized and led to many interesting findings, a substantial number of recent GWASs indicate that for most disorders, the individual SNPs explain only a small fraction of the genetic causes. Consequently, exploring multi-SNPs interactions in the hope of discovering more significant associations has attracted more attentions. Due to the huge search space for complicated multilocus interactions, many fast and effective methods have recently been proposed for detecting disease-associated epistatic interactions using GWAS data. In this paper, we provide a critical review and comparison of eight popular methods, i.e., BOOST, TEAM, epi Forest, EDCF, SNPHarvester, epi MODE, MECPM, and MIC, which are used for detecting gene-gene interactions among genetic loci. In views of the assumption model on the data and searching strategies, we divide the methods into seven categories. Moreover, the evaluation methodologies,including detecting powers, disease models for simulation, resources of real GWAS data, and the control of false discover rate, are elaborated as references for new approach developers. At the end of the paper, we summarize the methods and discuss the future directions in genome-wide association studies for detecting epistatic interactions.