Integrated multiple population analysis of leaf architecture traits in maize (Zea mays L.)

Leaf morphology in maize is regulated by developmental patterning along three axes: proximodistal, mediolateral, and adaxial-abaxial. Maize contains homologues of many genes identified as regulators of leaf development in other species, but their relationship to the natural variation of leaf shape remains unknown. In this study, quantitative trait loci (QTLs) for leaf angle, leaf orientation value, leaf length, and leaf width were mapped by a total of 256 F(2:3) families evaluated in three environments. Meta-analysis was used to integrate genetic maps and detect QTLs across several independent QTL studies, on the basis of the previously reported experimental results for leaf architecture traits. Candidate gene sequences for leaf architecture were mapped in the integrated consensus genetic map. In total, 21 QTLs and 17 meta-QTLs (mQTLs) were detected. Among these QTLs, qLA1-1 and qLA2 were consistently detected in five and three populations respectively, and six of seven QTLs with contributions (R(2)) >10% were integrated in mQTLs. Six key mQTLs (mQTL1-1, mQTL2-1, mQTL3-3, mQTL5-1, mQTL7-2, and mQTL8-1) with R(2) of some initial QTLs >10% included 4-6 initial QTLs associated with 2-4 traits. Therefore, the chromosome regions for six mQTLs with high QTL co-localization might be hot spots of the important QTLs for the associated traits. Fifteen key candidate genes controlling leaf architecture traits coincided with 11 corresponding mQTLs, namely DWARF4, KAN3, liguleless1, TAC1, ROT3, AS2/liguleless2, PFL2, yabby9/SE/LIC/yabby15, mwp1, CYCD3;2, and CYCB1. In particular, DWARF4, liguleless1, AS2/liguleless2, yabby9/SE/LIC/yabby15, and CYCD3;2 were mapped within the important mQTL1-1, mQTL2-1, mQTL3-3, mQTL5-1, and mQTL7-2 intervals, respectively. Fine mapping or construction of single chromosome segment lines for genetic regions of these five mQTLs is worth further study and could be put to use in marker-assisted breeding. In conclusion, the results provide useful information for further research and help to reveal the molecular mechanisms with regard to leaf architecture traits.     

Genetic analysis of leaf morphology underlying the plant density response by QTL mapping in maize (<Emphasis Type="Italic">Zea mays</Emphasis> L.)

Maize yield increase has been strongly linked to plant population densities over time with changes in plant architecture, but the genetic basis for the plant architecture response to plant density is unknown, as is its stability across environments. To elucidate the genetic basis of the plant architecture response to density in maize, we mapped quantitative trait loci (QTLs) for leaf morphology-related traits in four sets of recombinant inbred line (RIL) populations under two plant density conditions. Forty-five QTLs for six traits were detected in both high and low plant density conditions. Thirty-seven QTLs were only detected when grown under high plant density, and 34 QTLs were only detected when grown under low plant density. Twenty-two meta-QTLs (mQTLs) were identified by meta-analysis, and mQTL1-1, mQTL3-2 and mQTL8 were identified when grown under high and low plant densities, with R ² of some initial QTLs > 10 %, suggesting the mQTLs might be hot spots of the important QTLs for the related traits under planting density stress conditions. The results presented here provide useful information for further research and the marker-assisted selection of varieties targeting increased plant density and will help to reveal the molecular mechanisms related to leaf morphology in response to density.     

QTL mapping of flag leaf-related traits in wheat (<Emphasis Type="Italic">Triticum aestivum</Emphasis> L.)

Key message

QTL controlling flag leaf length, flag leaf width, flag leaf area and flag leaf angle were mapped in wheat.

Abstract

This study aimed to advance our understanding of the genetic mechanisms underlying morphological traits of the flag leaves of wheat (Triticum aestivum L.). A recombinant inbred line (RIL) population derived from ND3331 and the Tibetan semi-wild wheat Zang1817 was used to identify quantitative trait loci (QTLs) controlling flag leaf length (FLL), flag leaf width (FLW), flag leaf area (FLA), and flag leaf angle (FLANG). Using an available simple sequence repeat genetic linkage map, 23 putative QTLs for FLL, FLW, FLA, and FLANG were detected on chromosomes 1B, 2B, 3A, 3D, 4B, 5A, 6B, 7B, and 7D. Individual QTL explained 4.3–68.52% of the phenotypic variance in different environments. Four QTLs for FLL, two for FLW, four for FLA, and five for FLANG were detected in at least two environments. Positive alleles of 17 QTLs for flag leaf-related traits originated from ND3331 and 6 originated from Zang1817. QTLs with pleiotropic effects or multiple linked QTL were also identified on chromosomes 1B, 4B, and 5A; these are potential target regions for fine-mapping and marker-assisted selection in wheat breeding programs.

     

QTL consistency and meta-analysis for grain yield components in three generations in maize

Grain yield is the most important and complex trait in maize. In this study, a total of 258 F₉ recombinant inbred lines (RIL), derived from a cross between dent corn inbred Dan232 and popcorn inbred N04, were evaluated for eight grain yield components under four environments. Quantitative trait loci (QTL) and their epistatic interactions were detected for all traits under each environment and in combined analysis. Meta-analysis was used to integrate genetic maps and detected QTL across three generations (RIL, F_2:3 and BC₂F₂) derived from the same cross. In total, 103 QTL, 42 pairs of epistatic interactions and 16 meta-QTL (mQTL) were detected. Twelve out of 13 QTL with contributions (R ²) over 15% were consistently detected in 3–4 environments (or in combined analysis) and integrated in mQTL. Only q100GW-7-1 was detected in all four environments and in combined analysis. 100qGW-1-1 had the largest R ² (19.3–24.6%) in three environments and in combined analysis. In contrast, 35 QTL for 6 grain yield components were detected in the BC₂F₂ and F_2:3 generations, no common QTL across three generations were located in the same marker intervals. Only 100 grain weight (100GW) QTL on chromosome 5 were located in adjacent marker intervals. Four common QTL were detected across the RIL and F_2:3 generations, and two between the RIL and BC₂F₂ generations. Each of five important mQTL (mQTL7-1, mQTL10-2, mQTL4-1, mQTL5-1 and mQTL1-3) included 7–12 QTL associated with 2–6 traits. In conclusion, we found evidence of strong influence of genetic structure and environment on QTL detection, high consistency of major QTL across environments and generations, and remarkable QTL co-location for grain yield components. Fine mapping for five major QTL (q100GW-1-1, q100GW-7-1, qGWP-4-1, qERN-4-1 and qKR-4-1) and construction of single chromosome segment lines for genetic regions of five mQTL merit further studies and could be put into use in marker-assisted breeding.     

A Single Locus Is Responsible for Salinity Tolerance in a Chinese Landrace Barley (Hordeum vulgare L.)

Introduction

Salinity and waterlogging are two major abiotic stresses severely limiting barley production. The lack of a reliable screening method makes it very hard to improve the tolerance through breeding programs.

Methods

This work used 188 DH lines from a cross between a Chinese landrace variety, TX9425 (waterlogging and salinity tolerant), and a Japanese malting barley, Naso Nijo (waterlogging and salinity sensitive), to identify QTLs associated with the tolerance.

Results

Four QTLs were found for waterlogging tolerance. The salinity tolerance was evaluated with both a hydroponic system and in potting mixture. In the trial with potting mixture, only one major QTL was identified to associate with salinity tolerance. This QTL explained nearly 50% of the phenotypic variation, which makes it possible for further fine mapping and cloning of the gene. This QTL was also identified in the hydroponic experiment for different salt-related traits. The position of this QTL was located at a similar position to one of the major QTLs for waterlogging tolerance, indicating the possibility of similar mechanisms controlling both waterlogging and salinity tolerance.

Conclusion

The markers associated with the QTL provided a unique opportunity in breeding programs for selection of salinity and waterlogging tolerance.     

Detection of multiple quantitative trait loci and their pleiotropic effects in outbred pig populations

Background

Simultaneous detection of multiple QTLs (quantitative trait loci) may allow more accurate estimation of genetic effects. We have analyzed outbred commercial pig populations with different single and multiple models to clarify their genetic properties and in addition, we have investigated pleiotropy among growth and obesity traits based on allelic correlation within a gamete.

Methods

Three closed populations, (A) 427 individuals from a Yorkshire and Large White synthetic breed, (B) 547 Large White individuals and (C) 531 Large White individuals, were analyzed using a variance component method with one-QTL and two-QTL models. Six markers on chromosome 4 and five to seven markers on chromosome 7 were used.

Results

Population A displayed a high test statistic for the fat trait when applying the two-QTL model with two positions on two chromosomes. The estimated heritabilities for polygenic effects and for the first and second QTL were 19%, 17% and 21%, respectively. The high correlation of the estimated allelic effect on the same gamete and QTL test statistics suggested that the two separate QTL which were detected on different chromosomes both have pleiotropic effects on the two fat traits. Analysis of population B using the one-QTL model for three fat traits found a similar peak position on chromosome 7. Allelic effects of three fat traits from the same gamete were highly correlated suggesting the presence of a pleiotropic QTL. In population C, three growth traits also displayed similar peak positions on chromosome 7 and allelic effects from the same gamete were correlated.

Conclusion

Detection of the second QTL in a model reduced the polygenic heritability and should improve accuracy of estimated heritabilities for both QTLs.     

Mapping in the era of sequencing: high density genotyping and its application for mapping TYLCV resistance in Solanum pimpinellifolium

Background

A RIL population between Solanum lycopersicum cv. Moneymaker and S. pimpinellifolium G1.1554 was genotyped with a custom made SNP array. Additionally, a subset of the lines was genotyped by sequencing (GBS).

Results

A total of 1974 polymorphic SNPs were selected to develop a linkage map of 715 unique genetic loci. We generated plots for visualizing the recombination patterns of the population relating physical and genetic positions along the genome.This linkage map was used to identify two QTLs for TYLCV resistance which contained favourable alleles derived from S. pimpinellifolium. Further GBS was used to saturate regions of interest, and the mapping resolution of the two QTLs was improved. The analysis showed highest significance on Chromosome 11 close to the region of 51.3 Mb (qTy-p11) and another on Chromosome 3 near 46.5 Mb (qTy-p3). Furthermore, we explored the population using untargeted metabolic profiling, and the most significant differences between susceptible and resistant plants were mainly associated with sucrose and flavonoid glycosides.

Conclusions

The SNP information obtained from an array allowed a first QTL screening of our RIL population. With additional SNP data of a RILs subset, obtained through GBS, we were able to perform an in silico mapping improvement to further confirm regions associated with our trait of interest. With the combination of different ~ omics platforms we provide valuable insight into the genetics of S. pimpinellifolium-derived TYLCV resistance.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1152) contains supplementary material, which is available to authorized users.     

玉米叶形相关性状的Meta-QTL及候选基因分析

叶长、叶宽、叶面积及叶夹角不仅影响玉米(Zea mays)光合效率, 也是株型的重要构成因素。通过对620个叶形QTL进行整合, 构建不同遗传背景下的叶形QTL整合图谱, 利用元分析发掘出22个叶长、22个叶宽、12个叶面积以及17个叶夹角mQTL; 进一步运用生物信息学手段, 确定44个与叶片发育密切相关的候选基因。分析发现, 仅有NAL7-like、YABBY6- like和GRF2等13个基因位于mQTL区间内, 而玉米中已克隆的KNOTTED1、AN3/GIF1、rgd1/lbl1、mwp1及SRL2-like、HYL1-like和CYCB2;4-like等水稻(Oryza sativa)和拟南芥(Arabidopsis thaliana)叶形同源基因位于未被整合的QTL内; 对44个候选基因在叶片长、宽、厚发育过程中基部-末端、中央-边缘、远轴-近轴的调控机理进行归纳分析, 发现玉米中仅有少数几个候选基因被报道, 揭示了叶形发育的部分分子机理。因此, 对玉米叶形相关mQTL/QTL及基因进行全面深入的分析, 不仅有助于增加对其遗传结构的了解, 发掘更多候选基因, 阐明叶形发育和形成的分子机制, 还可为耐密理想株型的分子标记辅助选择提供依据。     

Dissection of genotype-phenotype associations in rice grains using metabolome quantitative trait loci analysis

A comprehensive and large‐scale metabolome quantitative trait loci (mQTL) analysis was performed to investigate the genetic backgrounds associated with metabolic phenotypes in rice grains. The metabolome dataset consisted of 759 metabolite signals obtained from the grains of 85 lines of rice (Oryza sativa, Sasanishiki × Habataki back‐crossed inbred lines). Metabolome analysis was performed using four mass spectrometry pipelines to enhance detection of different classes of metabolites. This mQTL analysis of a wide range of metabolites highlighted an uneven distribution of 802 mQTLs on the rice genome, as well as different modes of metabolic trait (m‐trait) control among various types of metabolites. The levels of most metabolites within rice grains were highly sensitive to environmental factors, but only weakly associated with mQTLs. Coordinated control was observed for several groups of metabolites, such as amino acids linked to the mQTL hotspot on chromosome 3. For flavonoids, m‐trait variation among the experimental lines was tightly governed by genetic factors that alter the glycosylation of flavones. Many loci affecting levels of metabolites were detected by QTL analysis, and plausible gene candidates were evaluated by in silico analysis. Several mQTLs profoundly influenced metabolite levels, providing insight into the control of rice metabolism. The genomic region and genes potentially responsible for the biosynthesis of apigenin‐6,8‐di‐C‐α‐l‐ arabinoside are presented as an example of a critical mQTL identified by the analysis.     

Genetic Analysis of Ligation-Induced Neointima Formation in an F2 Intercross of C57BL/6 and FVB/N Inbred Mouse Strains

Objective

Proliferation and migration of vascular smooth muscle cells (SMCs) are central for arterial diseases including atherosclerosis and restenosis. We hypothesized that the underlying mechanisms may be modeled by carotid ligation in mice. In FVB/N inbred mice, ligation leads to abundant neointima formation with proliferating media-derived SMCs, whereas in C57BL/6 mice hardly any neointima is formed. In the present study, we aimed to identify the chromosomal location of the causative gene variants in an F2 intercross between these two mouse strains.

Methods and Results

The neointimal cross-sectional area was significantly different between FVB/N, C57BL/6 and F1 female mice 4 weeks after ligation. Carotid artery ligation and a genome scan using 800 informative SNP markers were then performed in 157 female F2 mice. Using quantitative trait loci (QTL) analysis, we identified suggestive, but no genome-wide significant, QTLs on chromosomes 7 and 12 for neointimal cross-sectional area and on chromosome 14 for media area. Further analysis of the cross revealed 4 QTLs for plasma cholesterol, which combined explained 69% of the variation among F2 mice.

Conclusions

We identified suggestive QTLs for neointima and media area after carotid ligation in an intercross of FVB/N and C57BL/6 mice, but none that reached genome-wide significance indicating a complex genetic architecture of the traits. Genome-wide significant QTLs for total cholesterol levels were identified on chromosomes 1, 3, 9, and 12.     

Construction of a genome-anchored,high-density genetic map for melon (<Emphasis Type="Italic">Cucumis melo</Emphasis> L.) and identification of <Emphasis Type="Italic">Fusarium oxysporum</Emphasis> f. sp. <Emphasis Type="Italic">melonis</Emphasis> race 1 resistance QTL

Key message

Four QTLs and an epistatic interaction were associated with disease severity in response to inoculation with Fusarium oxysporum f. sp. melonis race 1 in a recombinant inbred line population of melon.

Abstract

The USDA Cucumis melo inbred line, MR-1, harbors a wealth of alleles associated with resistance to several major diseases of melon, including powdery mildew, downy mildew, Alternaria leaf blight, and Fusarium wilt. MR-1 was crossed to an Israeli cultivar, Ananas Yok’neam, which is susceptible to all of these diseases, to generate a recombinant inbred line (RIL) population of 172 lines. In this study, the RIL population was genotyped to construct an ultra-dense genetic linkage map with 5663 binned SNPs anchored to the C. melo genome and exhibits the overall high quality of the assembly. The utility of the densely genotyped population was demonstrated through QTL mapping of a well-studied trait, resistance to Fusarium wilt caused by Fusarium oxysporum f. sp. melonis (Fom) race 1. A major QTL co-located with the previously validated resistance gene Fom-2. In addition, three minor QTLs and an epistatic interaction contributing to Fom race 1 resistance were identified. The MR-1 × AY RIL population provides a valuable resource for future QTL mapping studies and marker-assisted selection of disease resistance in melon.

     

Detection and integration of quantitative trait loci for grain yield components and oil content in two connected recombinant inbred line populations of high-oil maize

Improvement in grain yield is an important objective in high-oil maize breeding. In this study, one high-oil maize inbred was crossed with two normal maize inbreds to produce two connected recombinant inbred line (RIL) populations with 282 and 263 F_7:8 families, respectively. The field experiments were conducted under four environments, and eight grain yield components and grain oil content were evaluated. Two genetic linkage maps were constructed using 216 and 208 polymorphic SSR markers. Quantitative trait loci (QTL) were detected for all traits under each environment and in combined analysis. Meta-analysis was used to integrate genetic maps and detected QTL in both populations. A total of 199 QTL were detected, 122 in population 1 and 87 in population 2. Seven, 11 and 19 QTL showed consistency across five environments, across two RIL populations and with respective F_2:3 generations, respectively. 183 QTL were integrated in 28 meta-QTL (mQTL). QTL with contributions over 15% were consistently detected in 3–4 cases and integrated in mQTL. Each mQTL included 3–19 QTL related to 1–4 traits, reflecting remarkable QTL co-location for grain yield components and oil content. Further research and marker-assisted selection (MAS) should be concentrated on 37 consistent QTL and four genetic regions of mQTL with more than 10 QTL at bins 3.04–3.05, 7.02, 8.04–8.05 and 9.04–9.05. Near-isogenic lines for 100-grain-weight QTL at bin 7.02–7.03, for ear-length QTL at bin 7.02–7.03 and for rows-per-ear QTL at bin 3.08 are now in construction using MAS. Co-located candidate genes could facilitate the identification of candidate genes for grain yield in maize.     

Seasonal and plant-density dependency for quantitative trait loci affecting flowering time in multiple populations of Arabidopsis thaliana

Multiple environmental cues regulate the transition to flowering. In natural environments, plants perceive seasonal progression by changes in day length and growth temperature, and plant density is monitored by changes in the light quality reflected from neighbouring vegetation. To understand the seasonal and plant-density dependence associated with natural allelic variation in flowering time, we conducted a quantitative trait loci (QTL) mapping study in Ler x Cvi, Bay x Sha and Ler x No-0 recombinant inbred line (RIL) populations of Arabidopsis thaliana. Days and total leaf number to bolting were examined under low and high plant density (200 or 1600 plants m(-2)) in autumn-winter and spring seasons. We found between 4 and 10 QTLs associated with seasonal and density variations in each RIL population. For Ler x Cvi and Bay x Sha RIL populations, a major proportion of QTLs showed seasonal and density interaction (up to 63%) and four QTLs were common to all environments (21%). Only three QTLs showed seasonal or density dependency. By aligning the linkage maps onto a common physical map, we detected at least one QTL at chromosome 2 and two QTLs at chromosome 5 that overlap between the three RIL populations, suggesting that these QTLs play a crucial role in the adaptive control of flowering time.     

Identification of metabolic and biomass QTL in Arabidopsis thaliana in a parallel analysis of RIL and IL populations

Plant growth and development are tightly linked to primary metabolism and are subject to natural variation. In order to obtain an insight into the genetic factors controlling biomass and primary metabolism and to determine their relationships, two Arabidopsis thaliana populations [429 recombinant inbred lines (RIL) and 97 introgression lines (IL), derived from accessions Col-0 and C24] were analyzed with respect to biomass and metabolic composition using a mass spectrometry-based metabolic profiling approach. Six and 157 quantitative trait loci (QTL) were identified for biomass and metabolic content, respectively. Two biomass QTL coincide with significantly more metabolic QTL (mQTL) than statistically expected, supporting the notion that the metabolic profile and biomass accumulation of a plant are linked. On the same basis, three out the six biomass QTL can be simulated purely on the basis of metabolic composition. QTL based on analysis of the introgression lines were in substantial agreement with the RIL-based results: five of six biomass QTL and 55% of the mQTL found in the RIL population were also found in the IL population at a significance level of P  ≤ 0.05, with >80% agreement on the allele effects. Some of the differences could be attributed to epistatic interactions. Depending on the search conditions, metabolic pathway-derived candidate genes were found for 24–67% of all tested mQTL in the database AraCyc 3.5. This dataset thus provides a comprehensive basis for the detection of functionally relevant variation in known genes with metabolic function and for identification of genes with hitherto unknown roles in the control of metabolism.     

Genetic analysis of Verticillium wilt resistance in a backcross inbred line population and a meta-analysis of quantitative trait loci for disease resistance in cotton

Background

Verticillium wilt (VW) and Fusarium wilt (FW), caused by the soil-borne fungi Verticillium dahliae and Fusarium oxysporum f. sp. vasinfectum, respectively, are two most destructive diseases in cotton production worldwide. Root-knot nematodes (Meloidogyne incognita, RKN) and reniform nematodes (Rotylenchulus reniformis, RN) cause the highest yield loss in the U.S. Planting disease resistant cultivars is the most cost effective control method. Numerous studies have reported mapping of quantitative trait loci (QTLs) for disease resistance in cotton; however, very few reliable QTLs were identified for use in genomic research and breeding.

Results

This study first performed a 4-year replicated test of a backcross inbred line (BIL) population for VW resistance, and 10 resistance QTLs were mapped based on a 2895 cM linkage map with 392 SSR markers. The 10 VW QTLs were then placed to a consensus linkage map with other 182 VW QTLs, 75 RKN QTLs, 27 FW QTLs, and 7 RN QTLs reported from 32 publications. A meta-analysis of QTLs identified 28 QTL clusters including 13, 8 and 3 QTL hotspots for resistance to VW, RKN and FW, respectively. The number of QTLs and QTL clusters on chromosomes especially in the A-subgenome was significantly correlated with the number of nucleotide-binding site (NBS) genes, and the distribution of QTLs between homeologous A- and D- subgenome chromosomes was also significantly correlated.

Conclusions

Ten VW resistance QTL identified in a 4-year replicated study have added useful information to the understanding of the genetic basis of VW resistance in cotton. Twenty-eight disease resistance QTL clusters and 24 hotspots identified from a total of 306 QTLs and linked SSR markers provide important information for marker-assisted selection and high resolution mapping of resistance QTLs and genes. The non-overlapping of most resistance QTL hotspots for different diseases indicates that their resistances are controlled by different genes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1682-2) contains supplementary material, which is available to authorized users.     

Genetic dissection and validation of candidate genes for flag leaf size in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

Key message

Two major loci with functional candidate genes were identified and validated affecting flag leaf size, which offer desirable genes to improve leaf architecture and photosynthetic capacity in rice.

Abstract

Leaf size is a major determinant of plant architecture and yield potential in crops. However, the genetic and molecular mechanisms regulating leaf size remain largely elusive. In this study, quantitative trait loci (QTLs) for flag leaf length and flag leaf width in rice were detected with high-density single nucleotide polymorphism genotyping of a chromosomal segment substitution line (CSSL) population, in which each line carries one or a few chromosomal segments from the japonica cultivar Nipponbare in a common background of the indica variety Zhenshan 97. In total, 14 QTLs for flag leaf length and nine QTLs for flag leaf width were identified in the CSSL population. Among them, qFW4-2 for flag leaf width was mapped to a 37-kb interval, with the most likely candidate gene being the previously characterized NAL1. Another major QTL for both flag leaf width and length was delimited by substitution mapping to a small region of 13.5 kb that contains a single gene, Ghd7.1. Mutants of Ghd7.1 generated using CRISPR/CAS9 approach showed reduced leaf size. Allelic variation analyses also validated Ghd7.1 as a functional candidate gene for leaf size, photosynthetic capacity and other yield-related traits. These results provide useful genetic information for the improvement of leaf size and yield in rice breeding programs.

     

QTL mapping of combining ability and heterosis of agronomic traits in rice backcross recombinant inbred lines and hybrid crosses

Background

Combining ability effects are very effective genetic parameters in deciding the next phase of breeding programs. Although some breeding strategies on the basis of evaluating combining ability have been utilized extensively in hybrid breeding, little is known about the genetic basis of combining ability. Combining ability is a complex trait that is controlled by polygenes. With the advent and development of molecular markers, it is feasible to evaluate the genetic bases of combining ability and heterosis of elite rice hybrids through QTL analysis.

Methodology/Principal Findings

In the present study, we first developed a QTL-mapping method for dissecting combining ability and heterosis of agronomic traits. With three testcross populations and a BCRIL population in rice, biometric and QTL analyses were conducted for ten agronomic traits. The significance of general combining ability and special combining ability for most of the traits indicated the importance of both additive and non-additive effects on expression levels. A large number of additive effect QTLs associated with performance per se of BCRIL and general combining ability, and dominant effect QTLs associated with special combining ability and heterosis were identified for the ten traits.

Conclusions/Significance

The combining ability of agronomic traits could be analyzed by the QTL mapping method. The characteristics revealed by the QTLs for combining ability of agronomic traits were similar with those by multitudinous QTLs for agronomic traits with performance per se of BCRIL. Several QTLs (1–6 in this study) were identified for each trait for combining ability. It demonstrated that some of the QTLs were pleiotropic or linked tightly with each other. The identification of QTLs responsible for combining ability and heterosis in the present study provides valuable information for dissecting genetic basis of combining ability.     

Identification of methylation quantitative trait loci (mQTLs) influencing promoter DNA methylation of alcohol dependence risk genes

Interaction of DNA methylation and sequence variants that are methylation quantitative trait loci (mQTLs) may influence susceptibility to diseases such as alcohol dependence (AD). We used genome-wide genotype data from 268 African Americans (AAs: 129 AD cases and 139 controls) and 143 European Americans (EAs: 129 AD cases and 14 controls) to identify mQTLs that were associated with promoter CpGs in 82 AD risk genes. 282 significant mQTL–CpG pairs (9.9 × 10^?100 ≤ P _nominal ≤ 7.7 × 10^?8) in AAs and 313 significant mQTL–CpG pairs (2.7 × 10^?53 ≤ P _nominal ≤ 9.9 × 10^?8) in EAs were identified [i.e., mQTL–CpG associations survived multiple-testing correction, q values (false discovery rate) ≤ 0.05]. The most significant mQTL was rs1800759, which was strongly associated with CpG cg12011299 in both AAs (P _nominal = 9.9 × 10^?100; q = 6.7 × 10^?91) and EAs (P _nominal = 2.7 × 10^?53; q = 1.4 × 10^?44). Rs1800759 (previously known to be associated to AD) and CpG cg12011299 (distance: 37 bp) are both located in alcohol dehydrogenase (ADH) 4 gene (ADH4) promoter region. In general, the strength of association between mQTLs and CpGs was inversely correlated with the distance between them. Association was also influenced by race and AD. Additionally, 48.3 % of the mQTLs identified in AAs and 65.6 % of the mQTLs identified in EAs were predicted to be expression QTLs. Three mQTLs (rs2173201, rs4147542, and rs4147541 in ADH1B-AHD1C gene cluster region) found in AAs were previously identified by our genome-wide association studies as being significantly associated with AD in AAs. Thus, DNA methylation, which can be influenced by sequence variants and is implicated in gene expression regulation, appears to at least partially underlie the association of genetic variation with AD.     

A genome-wide association study in catfish reveals the presence of functional hubs of related genes within QTLs for columnaris disease resistance

Background

Columnaris causes severe mortalities among many different wild and cultured freshwater fish species, but understanding of host resistance is lacking. Catfish, the primary aquaculture species in the United States, serves as a great model for the analysis of host resistance against columnaris disease. Channel catfish in general is highly resistant to the disease while blue catfish is highly susceptible. F2 generation of hybrids can be produced where phenotypes and genotypes are segregating, providing a useful system for QTL analysis. To identify genes associated with columnaris resistance, we performed a genome-wide association study (GWAS) using the catfish 250 K SNP array with 340 backcross progenies derived from crossing female channel catfish (Ictalurus punctatus) with male F1 hybrid catfish (female channel catfish I. punctatus × male blue catfish I. furcatus).

Results

A genomic region on linkage group 7 was found to be significantly associated with columnaris resistance. Within this region, five have known functions in immunity, including pik3r3b, cyld-like, adcyap1r1, adcyap1r1-like, and mast2. In addition, 3 additional suggestively associated QTL regions were identified on linkage groups 7, 12, and 14. The resistant genotypes on the QTLs of linkage groups 7 and 12 were found to be homozygous with both alleles being derived from channel catfish. The paralogs of the candidate genes in the suggestively associated QTL of linkage group 12 were found on the QTLs of linkage group 7. Many candidate genes on the four associated regions are involved in PI3K pathway that is known to be required by many bacteria for efficient entry into the host.

Conclusion

The GWAS revealed four QTLs associated with columnaris resistance in catfish. Strikingly, the candidate genes may be arranged as functional hubs; the candidate genes within the associated QTLs on linkage groups 7 and 12 are not only co-localized, but also functionally related, with many of them being involved in the PI3K signal transduction pathway, suggesting its importance for columnaris resistance.