Mixed-model-based Composite Interval Mapping Research Articles

Genetic Dissection of Extreme Seed-Flooding Tolerance in a Wild Soybean PI342618B by Linkage Mapping and Candidate Gene Analysis.

Seed-flooding stress is one of major abiotic constraints that adversely affects soybean production worldwide. Identifying tolerant germplasms and revealing the genetic basis of seed-flooding tolerance are imperative goals for soybean breeding. In the present study, high-density linkage maps of two inter-specific recombinant inbred line (RIL) populations, named NJIRNP and NJIR4P, were utilized to identify major quantitative trait loci (QTLs) for seed-flooding tolerance using three parameters viz., germination rate (GR), normal seedling rate (NSR), and electrical conductivity (EC). A total of 25 and 18 QTLs were detected by composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM), respectively, and 12 common QTLs were identified through both methods. All favorable alleles for the tolerance are notably from the wild soybean parent. Moreover, four digenic epistatic QTL pairs were identified, and three of them showed no main effects. In addition, the pigmented soybean genotypes exhibited high seed-flooding tolerance compared with yellow seed coat genotypes in both populations. Moreover, out of five identified QTLs, one major region containing multiple QTLs associated with all three traits was identified on Chromosome 8, and most of the QTLs within this hotspot were major loci (R2 > 10) and detectable in both populations and multiple environments. Based on the gene expression and functional annotation information, 10 candidate genes from QTL "hotspot 8-2" were screened for further analysis. Furthermore, the results of qRT-PCR and sequence analysis revealed that only one gene, GmDREB2 (Glyma.08G137600), was significantly induced under flooding stress and displayed a TTC tribasic insertion mutation of the nucleotide sequence in the tolerant wild parent (PI342618B). GmDREB2 encodes an ERF transcription factor, and the subcellular localization analysis using green fluorescent protein (GFP) revealed that GmDREB2 protein was localized in the nucleus and plasma membrane. Furthermore, overexpression of GmDREB2 significantly promoted the growth of soybean hairy roots, which might indicate its critical role in seed-flooding stress. Thus, GmDREB2 was considered as the most possible candidate gene for seed-flooding tolerance.

Identification and Validation of Major QTLs, Epistatic Interactions, and Candidate Genes for Soybean Seed Shape and Weight Using Two Related RIL Populations.

Understanding the genetic mechanism underlying seed size, shape, and weight is essential for enhancing soybean cultivars. High-density genetic maps of two recombinant inbred line (RIL) populations, LM6 and ZM6, were evaluated across multiple environments to identify and validate M-QTLs as well as identify candidate genes behind major and stable quantitative trait loci (QTLs). A total of 239 and 43 M-QTLs were mapped by composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM) approaches, from which 180 and 18, respectively, are novel QTLs. Twenty-two QTLs including four novel major QTLs were validated in the two RIL populations across multiple environments. Moreover, 18 QTLs showed significant AE effects, and 40 pairwise of the identified QTLs exhibited digenic epistatic effects. Thirty-four QTLs associated with seed flatness index (FI) were identified and reported here for the first time. Seven QTL clusters comprising several QTLs for seed size, shape, and weight on genomic regions of chromosomes 3, 4, 5, 7, 9, 17, and 19 were identified. Gene annotations, gene ontology (GO) enrichment, and RNA-seq analyses of the genomic regions of those seven QTL clusters identified 47 candidate genes for seed-related traits. These genes are highly expressed in seed-related tissues and nodules, which might be deemed as potential candidate genes regulating the seed size, weight, and shape traits in soybean. This study provides detailed information on the genetic basis of the studied traits and candidate genes that could be efficiently implemented by soybean breeders for fine mapping and gene cloning, and for marker-assisted selection (MAS) targeted at improving these traits individually or concurrently.

Mapping chromosomal regions associated with anther indehiscence with exerted stigmas in CRI-48 and Jasmine 85 cross of rice (Oryza sativa L)

Anther indehiscence in certain wide crosses combines male sterility with stigma exertion, a phenomenon that is desirable for hybrid rice seed production. This study sought to identify chromosomal region(s) that combine anther indehiscence with exerted stigmas. A mapping population consisting of 189 BC1F1 plants was derived from a cross between CRI-48 and Jasmine 85 and backcrossing the resulting F1 to Jasmine 85. Contrary to the three complementary genes mode of inheritance reported earlier, a single locus (AI6-1) was mapped on chromosome 6 at 27.4 cM for anther indehiscence with exerted stigmas through a mixed model-based composite interval mapping (MCIM). This locus was flanked by two single nucleotide polymorphism (SNP) markers, K_ID6002884 and K_ID6003341 within a range of 23.1–28.9 cM. The allele at the locus was contributed by the CRI-48 parent which has Oryza glaberrima ancestry. This locus is suggested to control anther indehiscence and stigma exertion through pleiotropic gene action or cluster of genes.

Genetic dissection of spike-related traits in wheat (Triticum aestivum L.) under aluminum stress

Wheat (Triticum aestivum L.) is one of the most important cereals and is a major staple food in developing countries. However, in acid soils, its productivity is limited by aluminum (Al) toxicity. Wheat yield can be enhanced by modifying the spike-related traits. In order to identify quantitative trait loci (QTL) associated with Al tolerance, 167 recombinant inbred lines (RILs) derived from a cross between SeriM82 and Babax were evaluated under normal and Al stress conditions for two consecutive crop seasons. Using Composite Interval Mapping (CIM) method, 64 QTLs including 13 putative and 51 suggestive QTLs were detected for all traits. These QTLs explained 3.58–14.39% of the total variations during both the crop seasons. Using mixed model-based composite interval mapping (MCIM) method, 58 QTLs were determined to have significant additive effects, of which 21 also showed epistatic effects. Twenty-three additive QTLs had significant interaction with environment, and 35 QTLs with no additive × environment interactions (AEI) were suggested as stable. Seven pairs of 12 pairs epistatic QTLs exhibited significant interactions with environment. 16.00% of additive QTL × environment interactions (QEIs) and 71.43% of epistatic QEIs were related to Al stress with significant genetic effects increasing phenotypic values. In both method, nine QTL clusters on six LGs 2A-d, 2D, 3B, 4B, 6A-a, and 7D-a, indicated pleiotropy or gene linkage in the inheritance of spike-related traits. These results will be of great value for marker-assisted selection and QTL pyramiding in wheat breeding programs for Al tolerance, following validation in different environments and genetic backgrounds and will accelerate the understanding of the genetic relationships among spike-related traits at the molecular level.

High-Resolution Mapping in Two RIL Populations Refines Major "QTL Hotspot" Regions for Seed Size and Shape in Soybean (Glycine max L.).

Seed size and shape are important traits determining yield and quality in soybean. However, the genetic mechanism and genes underlying these traits remain largely unexplored. In this regard, this study used two related recombinant inbred line (RIL) populations (ZY and K3N) evaluated in multiple environments to identify main and epistatic-effect quantitative trait loci (QTLs) for six seed size and shape traits in soybean. A total of 88 and 48 QTLs were detected through composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM), respectively, and 15 QTLs were common among both methods; two of them were major (R2 > 10%) and novel QTLs (viz., qSW-1-1ZN and qSLT-20-1K3N). Additionally, 51 and 27 QTLs were identified for the first time through CIM and MCIM methods, respectively. Colocalization of QTLs occurred in four major QTL hotspots/clusters, viz., “QTL Hotspot A”, “QTL Hotspot B”, “QTL Hotspot C”, and “QTL Hotspot D” located on Chr06, Chr10, Chr13, and Chr20, respectively. Based on gene annotation, gene ontology (GO) enrichment, and RNA-Seq analysis, 23 genes within four “QTL Hotspots” were predicted as possible candidates, regulating soybean seed size and shape. Network analyses demonstrated that 15 QTLs showed significant additive x environment (AE) effects, and 16 pairs of QTLs showing epistatic effects were also detected. However, except three epistatic QTLs, viz., qSL-13-3ZY, qSL-13-4ZY, and qSW-13-4ZY, all the remaining QTLs depicted no main effects. Hence, the present study is a detailed and comprehensive investigation uncovering the genetic basis of seed size and shape in soybeans. The use of a high-density map identified new genomic regions providing valuable information and could be the primary target for further fine mapping, candidate gene identification, and marker-assisted breeding (MAB).

Genetic control of some plant growth characteristics of bread wheat (Triticum aestivum L.) under aluminum stress.

Biomass yield is an important trait for wheat breeding programs. Enhancing the yield of the aerial components of wheat cultivars will be an integral part of future wheat improvement. Aluminum (Al) toxicity is one of the main factors limiting wheat growth and production in acid soils, which occur on up to 50% of the arable lands of the world especially in tropical and subtropical regions. Our objective was to identify quantitative trait loci (QTL) of plant growth characteristics and yield in wheat. A recombinant inbred line (RIL) population consisting of 167 lines, derived from a cross between SeriM82 and Babax were evaluated under two Al treatments (+ Al, 800 µM of Al; -Al, 0 µM of Al) in the field based on an alpha lattice design with two replications for two consecutive crop seasons. A total of 40 QTLs including nine putative and 31 suggestive QTLs were found for all traits using the composite interval mapping (CIM) method. By mixed model-based composite interval mapping (MCIM) method, 42 additive QTLs and nine pairs of epistatic effects were detected for studied traits, of which 20 additive and six pairs of epistatic QTLs showed significant QTL × environment interactions. Most of the detected QTLs across environments were stable, and the highest number of stable QTLs was related to A genome. Co-localization of QTL was found on linkage groups (LGs) 2B, 4B, 6A-a, and 7A (CIM method) and 2A-d, and 6A-a (MCIM method). These results have implications for selection strategies in biomassyield and for increasing the yield of the aerial part of wheatfollowing further evaluations in various genetic backgrounds and environments.

Deciphering the Genetic Architecture of Plant Height in Soybean Using Two RIL Populations Sharing a Common M8206 Parent.

Plant height (PH) is an important agronomic trait that is closely related to soybean yield and quality. However, it is a complex quantitative trait governed by multiple genes and is influenced by environment. Unraveling the genetic mechanism involved in PH, and developing soybean cultivars with desirable PH is an imperative goal for soybean breeding. In this regard, the present study used high-density linkage maps of two related recombinant inbred line (RIL) populations viz., MT and ZM evaluated in three different environments to detect additive and epistatic effect quantitative trait loci (QTLs) as well as their interaction with environments for PH in Chinese summer planting soybean. A total of eight and 12 QTLs were detected by combining the composite interval mapping (CIM) and mixed-model based composite interval mapping (MCIM) methods in MT and ZM populations, respectively. Among these QTLs, nine QTLs viz., QPH-2, qPH-6-2MT, QPH-6, qPH-9-1ZM, qPH-10-1ZM, qPH-13-1ZM, qPH-16-1MT, QPH-17 and QPH-19 were consistently identified in multiple environments or populations, hence were regarded as stable QTLs. Furthermore, Out of these QTLs, three QTLs viz., qPH-4-2ZM, qPH-15-1MT and QPH-17 were novel. In particular, QPH-17 could detect in both populations, which was also considered as a stable and major QTL in Chinese summer planting soybean. Moreover, eleven QTLs revealed significant additive effects in both populations, and out of them only six showed additive by environment interaction effects, and the environment-independent QTLs showed higher additive effects. Finally, six digenic epistatic QTLs pairs were identified and only four additive effect QTLs viz., qPH-6-2MT, qPH-19-1MT/QPH-19, qPH-5-1ZM and qPH-17-1ZM showed epistatic effects. These results indicate that environment and epistatic interaction effects have significant influence in determining genetic basis of PH in soybean. These results would not only increase our understanding of the genetic control of plant height in summer planting soybean but also provide support for implementing marker assisted selection (MAS) in developing cultivars with ideal plant height as well as gene cloning to elucidate the mechanisms of plant height.

Mapping QTLs for physiological and biochemical traits related to grain yield under control and terminal heat stress conditions in bread wheat (Triticum aestivum L.).

In order to detect genomic regions with different effects for some of the physiological and biochemical traits of wheat, four experiments were conducted at Research Farm of Agricultural and Natural Resources Research Center of Zabol in 2015-2016 and 2016-2017 growing seasons. The experiments were carried out using four alpha lattice designs with two replications under non-stress and terminal heat stress conditions. Plant materials used in this study included 167 recombinant inbred lines and their parents ('SeriM82' and 'Babax'). Six traits including grain yield (GY), proline content (PRO), water soluble carbohydrates (WSC), maximum efficiency of photosystem II (Fv/Fm), cytoplasmic membrane stability (CMS) and chlorophyll content (CHL) were evaluated. Genetic linkage map consisted of 211 AFLP marker, 120 SSR marker and 144 DArT markers with 1864cm length and 4.4cm mean distance. QTL analysis was carried out using a mixed-model-based composite interval mapping (MCIM) method. By the combined analysis of normal phenotypic values, 27 additive QTLs and five pairs of epistatic effects were identified for studied traits, among which two additive and one epistatic QTL showed significant QTL × environment interactions. By the combined analysis of stress phenotypic values, a total of 26 QTLs with additive effects and 5 epistatic QTLs were detected, among which one additive and one epistatic QTL showed QTL × environment interactions. Six QTLs with major effects (QGY-2B, QGY-2D, QPro-5B, QWSC-4A, QFv/Fm-6A and QCMS-4B), which were common between two conditions could be useful for marker-assisted selection (MAS) in order to develop heat tolerant and high-performance wheat varieties.

Main and epistatic QTL analyses for Sclerotinia Head Rot resistance in sunflower.

Sclerotinia Head Rot (SHR), a disease caused by Sclerotinia sclerotiorum, is one of the most limiting factors in sunflower production. In this study, we identified genomic loci associated with resistance to SHR to support the development of assisted breeding strategies. We genotyped 114 Recombinant Inbred Lines (RILs) along with their parental lines (PAC2 –partially resistant–and RHA266 –susceptible–) by using a 384 single nucleotide polymorphism (SNP) Illumina Oligo Pool Assay to saturate a sunflower genetic map. Subsequently, we tested these lines for SHR resistance using assisted inoculations with S. sclerotiorum ascospores. We also conducted a randomized complete-block assays with three replicates to visually score disease incidence (DI), disease severity (DS), disease intensity (DInt) and incubation period (IP) through four field trials (2010–2014). We finally assessed main effect quantitative trait loci (M-QTLs) and epistatic QTLs (E-QTLs) by composite interval mapping (CIM) and mixed-model-based composite interval mapping (MCIM), respectively. As a result of this study, the improved map incorporates 61 new SNPs over candidate genes. We detected a broad range of narrow sense heritability (h2) values (1.86–59.9%) as well as 36 M-QTLs and 13 E-QTLs along 14 linkage groups (LGs). On LG1, LG10, and LG15, we repeatedly detected QTLs across field trials; which emphasizes their putative effectiveness against SHR. In all selected variables, most of the identified QTLs showed high determination coefficients, associated with moderate to high heritability values. Using markers shared with previous Sclerotinia resistance studies, we compared the QTL locations in LG1, LG2, LG8, LG10, LG11, LG15 and LG16. This study constitutes the largest report of QTLs for SHR resistance in sunflower. Further studies focusing on the regions in LG1, LG10, and LG15 harboring the detected QTLs are necessary to identify causal alleles and contribute to unraveling the complex genetic basis governing the resistance.

Advanced Backcross QTL Analysis of Fiber Strength and Fineness in a Cross between Gossypium hirsutum and G. mustelinum.

The molecular genetic basis of cotton fiber strength and fineness in crosses between Gossypium mustelinum and Gossypium hirsutum (Upland cotton) was dissected using 21 BC3F2 and 12 corresponding BC3F2:3 and BC3F2:4 families. The BC3F2 families were genotyped with simple sequence repeat markers from a G. hirsutum by G. mustelinum linkage map, and the three generations of BC3-derived families were phenotyped for fiber strength (STR) and fineness (Micronaire, MIC). A total of 42 quantitative trait loci (QTLs) were identified through one-way analysis of variance, including 15 QTLs for STR and 27 for MIC, with the percentage of variance explained by individual loci averaging 13.86 and 14.06%, respectively. Eighteen of the 42 QTLs were detected at least twice near the same markers in different generations/families or near linked markers in the same family, and 28 of the 42 QTLs were identified in both mixed model-based composite interval mapping and one-way variance analyses. Alleles from G. mustelinum increased STR for eight of 15 and reduced MIC for 15 of 27 QTLs. Significant among-family genotypic effects (P < 0.001) were detected in 13 and 10 loci for STR and MIC respectively, and five loci showed significant (P < 0.001) genotype × family interaction for MIC. These results support the hypothesis that fiber quality improvement for Upland cotton could be realized by introgressing G. mustelinum alleles although complexities due to the different effects of genetic background on introgressed chromatin might be faced. Building on prior work with G. barbadense, G. tomentosum, and G. darwinii, QTL mapping involving introgression of G. mustelinum alleles offers new allelic variation to Upland cotton germplasm.

A genome-wide SNP-based genetic map and QTL mapping for agronomic traits in Chinese cabbage

The aim of this work was to construct a high-resolution genetic map for the dissection of complex morphological and agronomic traits in Chinese cabbage (Brassica rapa L. syn. B. campestris). Chinese cabbage, an economically important vegetable, is a good model plant for studies on the evolution of morphologic variation. Herein, two high-generation inbred Chinese cabbage lines, ‘Huangxiaoza’ and ‘Bqq094-11’, were crossed. Then restriction-site-associated DNA sequencing (RAD-seq) was performed on the parents and 120 F2 individuals. A genetic map containing 711 bins representing 3985 single nucleotide polymorphism (SNP) markers was constructed. By using WinQTL with composite interval mapping (CIM) and mixed-model based composite interval mapping (MCIM) analysis via QTLNetwork, quantitative trait loci (QTL) linked to 16 genetic traits related to plant size, color and leaf characteristics were mapped to 10 linkage groups. The high density genetic map and QTL identified for morphological and agronomic traits lay the groundwork for functional gene mapping, map-based cloning and marker-assisted selection (MAS) in Chinese cabbage.

QTL analysis of cotton fiber length in advanced backcross populations derived from a cross between Gossypium hirsutum and G. mustelinum

QTLs for fiber length mapped in three generations of advanced backcross populations derived from crossing Gossypium hirsutum and Gossypium mustelinum showed opportunities to improve elite cottons by introgression from wild relatives. The molecular basis of cotton fiber length in crosses between Gossypium hirsutum and Gossypium mustelinum was dissected using 21 BC3F2 and 12 corresponding BC3F2:3 and BC3F2:4 families. Sixty-five quantitative trait loci (QTLs) were detected by one-way analysis of variance. The QTL numbers detected for upper-half mean length (UHM), fiber uniformity index (UI), and short fiber content (SFC) were 19, 20, and 26 respectively. Twenty-three of the 65 QTLs could be detected at least twice near adjacent markers in the same family or near the same markers across different families/generations, and 32 QTLs were detected in both one-way variance analyses and mixed model-based composite interval mapping. G. mustelinum alleles increased UHM and UI and decreased SFC for five, one, and one QTLs, respectively. In addition to the main-effect QTLs, 17 epistatic QTLs were detected which helped to elucidate the genetic basis of cotton fiber length. Significant among-family genotypic effects were detected at 18, 16, and 16 loci for UHM, UI, and SFC, respectively. Six, two, and two loci showed genotype × family interaction for UHM, UI and SFC, respectively, illustrating complexities that might be faced in introgression of exotic germplasm into cultivated cotton. Co-location of many QTLs for UHM, UI, and SFC accounted for correlations among these traits, and selection of these QTLs may improve the three traits simultaneously. The simple sequence repeat (SSR) markers associated with G. mustelinum QTLs will assist breeders in transferring and maintaining valuable traits from this exotic source during cultivar development.

Mapping and Epistatic Interactions of QTLs for Pericarp Thickness in Sweet Corn

Pericarp thickness is of great importance to the sensory quality of sweet corn. Mining the gene for pericarp thickness and understanding its genetic mechanism can provide a base for instructing breeding. Quantitative trait locus(QTL) for pericarp thickness was detected based on two genetic models using a population comprising 190 BC1F2 families derived from the cross of Richao-1(thin pericarp, 56.57 μm) ×1021(thick pericarp, 100.23 μm) in the present study. Three QTLs for pericarp thickness were identified on bin 3.01, 6.01, and 8.05 using the Composite interval mapping(CIM) method, explained 8.6%, 16.0%, and 7.2% of phenotypic variation, respectively. Based on the MCIM(mixed-model based CIM) method, we identified five QTLs for pericarp thickness, comprising one additive QTL and two pairs of epistatic QTLs. The additive QTL was located on bin 8.05. Additive × additive epistatic effects for pericarp thickness were showed between QTL in 2.01 and QTL in 6.05 with estimated 6.63% of the phenotypic variation and between QTL in 5.06 and QTL in 6.01 with the estimated phenotypic variation of 12.48%. The results indicated that epistasis and additive effects play an important role in the genetic basis of pericarp thickness. The MCIM model with the ability to detect epistatic QTLs is more suitable for pericarp thickness QTL mapping. In addition, candidate genes encoding proteins that play important role for pigment biosynthesis and cell transformation in endosperm were contained in four QTL regions of all, suggesting the likely relations between the expressions of these candidate genes and pericarp thickness variation.

QTL Mapping of Yield and Yield Components under Normal and Salt-stress Conditions in Bread Wheat (Triticum aestivum L.)

A population of 186 recombinant inbred lines of bread wheat (Superhead#2/Roshan) was evaluated to identify quantitative trait loci (QTL) for yield and yield components under normal (2 ds m–1) and salt-stress (10–12 ds m–1) conditions. A genetic map was constructed with 451 markers, including, 23 simple sequences repeats (SSRs) and 428 diversity arrays technology markers (DArTs). The main-effect QTL were identified by composite interval mapping (CIM) analysis using QTL Cartographer v2.5 and Qgene v4.3.2 and a mixed-model-based composite interval mapping (MCIM) method using QTLNetwork v2.1. A total of 98 significant QTL were detected at two testing locations on 20 chromosomes. Of these, only 40 QTL were detected by at least two of these software programs. A total of 24 QTL on ten chromosomes were identified for grain yield, most of which had a minor effect, contributing less than 10 % of the total phenotypic variation. Two grain-yield QTL intervals were detected on 1A1 and 3B, which contributed 11.02 % and 10.3 % to the total phenotypic variation, respectively. Roshan alleles were associated with an increase in grain yield under stress conditions on 1A1, 2B3, 3B, 6B1, 1D, 2D1. Among the 20 chromosomes, chromosome 3B with 27 QTL and two distinctive cluster regions was the most important. SSR markers gwm282, gwm247, gwm566, and gwm33 were tightly linked to QTL for the same or different traits under normal, stress or both conditions, and accounted for about 17 %, 43 %, 43 % and 20 % of the total phenotypic variation, respectively. These markers are suitable for marker-assisted selection to improve grain yield under normal and salt-stress conditions.

Detection of QTLs with main, epistatic and QTL × environment interaction effects for rice grain appearance quality traits using two populations of backcross inbred lines (BILs)

Quantitative trait loci (QTLs) of rice (Oryza sativa L.) grain appearance traits with additive, epistatic and QTL×environment (QE) interaction effects were studied using a mixed-model-based composite interval mapping (MCIM) method and two populations of back cross inbred lines (BILs) with a common maternal parent. In each of the populations, two or three main effects of QTLs were detected for grain length, width, length to width ratio, length of cooked rice, elongation ratio, elongation index and water absorption traits. The main effects of QTLs explained 12.4–43.9% and 20.4–50.4% of the variation of measured traits in the two populations, respectively. Some identified QTL clusters suggested that co-located QTL or maybe genes with pleiotropic effects could control these traits. When the QTLs detected for cooked versus milled rice were compared, it was found that the main QTLs with large effects for appearance in milled rice were on chromosomes 3 and 5, but those for appearance of cooked rice traits were on chromosome 6 close to the Wx gene. The phenotypic variations which were explained by the effects of the additive×additive×environment interaction (AAE) and additive×additive epistasis (AA) QTLs were less significant than those of the main QTLs. We conclude that although the major genes are frequent determinants of the rice grain appearance traits, the interactions between genes of minor effects may culminate in sizable effects on rice grain appearance traits. The information obtained in this study could be useful for manipulating the QTLs of these traits by molecular marker-assisted selection.

QTL Analysis on Yield and Its Components in Recombinant Inbred Lines of Upland Cotton

To facilitate the application of simple sequence repeat (SSR) markers in molecular marker-assisted selection (MAS) of upland cotton ( Gossypium hirsutum L.), a linkage map harboring 192 SSRs was constructed from the CRI-G6 population (Acala 1517 × Dezhou 047) using mixed-model-based composite interval mapping (MCIM) method. This map was used to detect quantitative trait loci (QTLs) associated with cotton yield traits in 3 environments. In the separate analysis, 24 main-effect QTLs were identified including each one stable QTL for seed yield, lint yield, lint percentage, seed index, and boll number per plant. In the joint analysis, 14 main-effect QTLs and 20 pairs of additive–additive epistatic QTLs were detected including 6 main-effect and 7 pairs of epistatic QTLs with environmental interactions. Eight main-effect QTLs were mapped in the same regions using both methods. Some main-effect QTLs with stability in different growing environments had phenotypic contributions larger than 10%, and these QTLs are candidate genes/loci in MAS of upland cotton.

Mapping QTL Associated with Photoperiod Sensitivity and Assessing the Importance of QTL×Environment Interaction for Flowering Time in Maize

BackgroundAn understanding of the genetic determinism of photoperiod response of flowering is a prerequisite for the successful exchange of germplasm across different latitudes. In order to contribute to resolve the genetic basis of photoperiod sensitivity in maize, a set of 201 recombinant inbred lines (RIL), derived from a temperate and tropical inbred line cross were evaluated in 5 field trials spread in short- and long-day environments.Methodology/Principal FindingsFirstly, QTL analyses for flowering time and photoperiod sensitivity in maize were conducted in individual photoperiod environments separately, and then, the total genetic effect was partitioned into additive effect (A) and additive-by-environment interaction effect (AE) by using a mixed-model-based composite interval mapping (MCIM) method.Conclusions/SignificanceSeven putative QTL were found associated with DPS thermal time based on the data estimated in individual environments. Nine putative QTL were found associated with DPS thermal time across environments and six of them showed significant QTL×enviroment (QE) interactions. Three QTL for photoperiod sensitivity were identified on chromosome 4, 9 and 10, which had the similar position to QTL for DPS thermal time in the two long-day environment. The major photoperiod sensitive loci qDPS10 responded to both short and long-day photoperiod environments and had opposite effects in different photoperiod environment. The QTL qDPS3, which had the greatest additive effect exclusively in the short-day environment, were photoperiod independent and should be classified in autonomous promotion pathway.

Influence of Epistasis and QTL × Environment Interaction on Heading Date of Rice ( Oryza sativa L.)

QTLs for heading date of rice ( Oryza sativa L.) with additive, epistatic, and QTL × environment ( QE) interaction effects were studied using a mixed-model-based composite interval mapping (MCIM) method and a double haploid (DH) population derived from IR64/Azucena in two crop seasons. Fourteen QTLs conferring heading date in rice, which were distributed on ten chromosomes except for chromosomes 5 and 9, were detected. Among these QTLs, eight had single-locus effects, five pairs had double-locus interaction effects, and two single-loci and one pair of double-loci showed QTL × environment interaction effects. All predicted values of QTL effects varied from 1.179 days to 2.549 days, with corresponding contribution ratios of 1.04%?4.84%. On the basis of the effects of the QTLs, the total genetic effects on rice heading date for the two parents and the two superior lines were predicted, and the putative reasons for discrepancies between predicted values and observed values, and the genetic potentiality in the DH population for improvement of heading date were discussed. These results are in agreement with previous results for heading date in rice, and the results provide further information, which indicate that both epistasis and QE interaction are important genetic basis for determining heading date in rice.

MIXED LINEAR MODEL APPROACHES FOR ANALYZING GENETIC MODELS OF COMPLEX QUANTITATIVE TRAITS

New approaches based on general mixed linear models were presented for analyzing complex quantitative traits in animal models, seed models and QTL (quantitative trait locus) mapping models. Variances and covariances can be appropriately estimated by MINQUE (minimum norm quadratic unbiased estimation) approaches. Random genetic effects can be predicted without bias by LUP (linear unbiased prediction) or AUP (adjusted unbiased prediction) methods. Mixed-model based composite interval mapping (MCIM) methods are suitable for efficiently searching QTLs along the whole genome. Bayesian methods and Markov Chain Monte Carlo (MCMC) methods can be applied in analyzing parameters of random effects as well as their variances.