Fine Mapping Of Quantitative Trait Loci Research Articles

Bulk segregation analysis in the NGS era: a review of its teenage years.

Bulk segregation analysis (BSA) utilizes a strategy of pooling individuals with extreme phenotypes to conduct economical and rapidly linked marker screening or quantitative trait locus (QTL) mapping. With the development of next-generation sequencing (NGS) technology in the past 10 years, BSA methods and technical systems have been gradually developed and improved. At the same time, the ever-decreasing costs of sequencing accelerate NGS-based BSA application in different species, including eukaryotic yeast, grain crops, economic crops, horticultural crops, trees, aquatic animals, and insects. This paper provides a landscape of BSA methods and reviews the BSA development process in the past decade, including the sequencing method for BSA, different populations, different mapping algorithms, associated region threshold determination, and factors affecting BSA mapping. Finally, we summarize related strategies in QTL fine mapping combining BSA.

Unraveling the complex genetic basis of growth in New Zealand silver trevally (Pseudocaranx georgianus).

Growth directly influences production rate and therefore is one of the most important and well-studied traits in animal breeding. However, understanding the genetic basis of growth has been hindered by its typically complex polygenic architecture. Here, we performed quantitative trait locus mapping and genome-wide association studies for 10 growth traits that were observed over 2 years in 1,100 F1 captive-bred trevally (Pseudocaranx georgianus). We constructed the first high-density linkage map for trevally, which included 19,861 single nucleotide polymorphism markers, and discovered 8 quantitative trait loci for height, length, and weight on linkage groups 3, 14, and 18. Using genome-wide association studies, we further identified 113 single nucleotide polymorphism-trait associations, uncovering 10 genetic hot spots involved in growth. Two of the markers found in the genome-wide association studies colocated with the quantitative trait loci previously mentioned, demonstrating that combining quantitative trait locus mapping and genome-wide association studies represents a powerful approach for the identification and validation of loci controlling complex traits. This is the first study of its kind for trevally. Our findings provide important insights into the genetic architecture of growth in this species and supply a basis for fine mapping quantitative trait loci, genomic selection, and further detailed functional analysis of the genes underlying growth in trevally.

Construction of a high-density bin-map and identification of fruit quality-related quantitative trait loci and functional genes in pear.

Pear (Pyrus spp.) is one of the most common fruit crops grown in temperate regions worldwide. Genetic enhancement of fruit quality is a fundamental goal of pear breeding programs. The genetic control of pear fruit quality traits is highly quantitative, and development of high-density genetic maps can facilitate fine-mapping of quantitative trait loci (QTLs) and gene identification. Bin-mapping is a powerful method of constructing high-resolution genetic maps from large-scale genotyping datasets. We performed whole-genome sequencing of pear cultivars ‘Niitaka’ and ‘Hongxiangsu’ and their 176 F1 progeny to identify genome-wide single-nucleotide polymorphism (SNP) markers for constructing a high-density bin-map of pear. This analysis yielded a total of 1.93 million SNPs and a genetic bin-map of 3190 markers spanning 1358.5 cM, with an average adjacent interval of 0.43 cM. This bin-map, along with other high-density genetic maps in pear, improved the reference genome assembly from 75.5 to 83.7% by re-anchoring the scaffolds. A quantitative genetic analysis identified 148 QTLs for 18 fruit-related traits; among them, QTLs for stone cell content, several key monosaccharides, and fruit pulp acids were identified for the first time in pear. A gene expression analysis of six pear cultivars identified 399 candidates in the identified QTL regions, which showed expression specific to fruit developmental stages in pear. Finally, we confirmed the function of PbrtMT1, a tonoplast monosaccharide transporter-related gene responsible for the enhancement of fructose accumulation in pear fruit on linkage group 16, in a transient transformation experiment. This study provides genomic and genetic resources as well as potential candidate genes for fruit quality improvement in pear.

Combining QTL-seq and linkage mapping to uncover the genetic basis of single vs. paired spikelets in the advanced populations of two-ranked maize\xd7teosinte

BackgroundTeosinte ear bears single spikelet, whereas maize ear bears paired spikelets, doubling the number of grains in each cupulate during maize domestication. In the past 20 years, genetic analysis of single vs. paired spikelets (PEDS) has been stagnant. A better understanding of genetic basis of PEDS could help fine mapping of quantitative trait loci (QTL) and cloning of genes.ResultsIn this study, the advanced mapping populations (BC3F2 and BC4F2) of maize × teosinte were developed by phenotypic recurrent selection. Four genomic regions associated with PEDS were detected using QTL-seq, located on 194.64–299.52 Mb, 0–162.80 Mb, 12.82–97.17 Mb, and 125.06–157.01 Mb of chromosomes 1, 3, 6, and 8, respectively. Five QTL for PEDS were identified in the regions of QTL-seq using traditional QTL mapping. Each QTL explained 1.12–38.05% of the phenotypic variance (PVE); notably, QTL qPEDS3.1 with the average PVE of 35.29% was identified in all tests. Moreover, 14 epistatic QTL were detected, with the total PVE of 47.57–66.81% in each test. The QTL qPEDS3.1 overlapped with, or was close to, one locus of 7 epistatic QTL. Near-isogenic lines (NILs) of QTL qPEDS1.1, qPEDS3.1, qPEDS6.1, and qPEDS8.1 were constructed. All individuals of NIL-qPEDS6.1(MT1) and NIL-qPEDS8.1(MT1) showed paired spikelets (PEDS = 0), but the flowering time was 7 days shorter in the NIL-qPEDS8.1(MT1). The ratio of plants with PEDS > 0 was low (1/18 to 3/18) in the NIL-qPEDS1.1(MT1) and NIL-qPEDS3.1(MT1), maybe due to the epistatic effect.ConclusionOur results suggested that major QTL, minor QTL, epistasis and photoperiod were associated with the variation of PEDS, which help us better understand the genetic basis of PEDS and provide a genetic resource for fine mapping of QTL.

Long-range linkage disequilibrium in French beef cattle breeds

BackgroundLinkage disequilibrium (LD) is a key parameter to study the history of populations and to identify and fine map quantitative trait loci (QTL) and it has been studied for many years in animal populations. The advent of new genotyping technologies has allowed whole-genome LD studies in most cattle populations. However, to date, long-range LD (LRLD) between distant variants on the genome has not been investigated in detail in cattle. Here, we present the first comprehensive study of LRLD in French beef cattle by analysing data on 672 Charolais (CHA), 462 Limousine (LIM) and 326 Blonde d’Aquitaine (BLA) individuals that were genotyped on the Illumina BovineHD Beadchip. Furthermore, whole-genome LD and haplotype block structure were analysed in these three breeds.ResultsWe computed linkage disequilibrium (r2) values for 5.9, 5.6 and 6.0 billion pairs of SNPs on the 29 autosomes of CHA, LIM and BLA, respectively. Mean r2 values drop to less than 0.1 for distances between SNPs greater than 120 kb. However, for the first time, we detected the existence of LRLD in the three main French beef breeds. In total, 598, 266, and 795 LRLD events (r2 ≥ 0.6) were detected in CHA, LIM and BLA, respectively. Each breed had predominantly population-specific LRLD interactions, although shared LRLD events occurred in a number of regions (55 LRLD events were shared between two breeds and nine between the three breeds). Examples of possible functional gene interactions and QTL co-location were observed with some of these LRLD events, which suggests epistatic selection.ConclusionsWe identified long-range linkage disequilibrium for the first time in French beef cattle populations. Epistatic selection may be the main source of the observed LRLD events, but other forces may also be involved. LRLD information should be accounted for in genome-wide association studies.

High-resolution genome-wide association study and genomic prediction for disease resistance and cold tolerance in wheat.

High-resolution genome-wide association study (GWAS) facilitated QTL fine mapping and candidate gene identification, and the GWAS based genomic prediction models were highly predictive and valuable in wheat genomic breeding. Wheat is a major staple food crop and provides more than one-fifth of the daily calories and dietary proteins for humans. Genome-wide association study (GWAS) and genomic selection (GS) for wheat stress resistance and tolerance related traits are critical to understanding their genetic architecture for improvement of breeding selection efficiency. However, the insufficient marker density in previous studies limited the utility of GWAS and GS in wheat genomic breeding. Here, we conducted a high-resolution GWAS for wheat leaf rust (LR), yellow rust (YR), powdery mildew (PM), and cold tolerance (CT) by genotyping a panel of 768 wheat cultivars using genotyping-by-sequencing. Among 153 quantitative trait loci (QTLs) identified, 81 QTLs were delimited to ≤ 1.0Mb intervals with three validated using bi-parental populations. Furthermore, 837 stress resistance-related genes were identified in the QTL regions with 12 showing induced expression by YR and PM pathogens. Genomic prediction using 2608, 4064, 3907, and 2136 pre-selected SNPs based on GWAS and genotypic correlations between the SNPs showed high prediction accuracies of 0.76, 0.73, and 0.78 for resistance to LR, YR, and PM, respectively, and 0.83 for resistance to cold damage. Our study laid a solid foundation for large-scale QTL fine mapping, candidate gene validation and GS in wheat.

On the use of whole-genome sequence data for across-breed genomic prediction and fine-scale mapping of QTL

BackgroundWhole-genome sequence (WGS) data are increasingly available on large numbers of individuals in animal and plant breeding and in human genetics through second-generation resequencing technologies, 1000 genomes projects, and large-scale genotype imputation from lower marker densities. Here, we present a computationally fast implementation of a variable selection genomic prediction method, that could handle WGS data on more than 35,000 individuals, test its accuracy for across-breed predictions and assess its quantitative trait locus (QTL) mapping precision.MethodsThe Monte Carlo Markov chain (MCMC) variable selection model (Bayes GC) fits simultaneously a genomic best linear unbiased prediction (GBLUP) term, i.e. a polygenic effect whose correlations are described by a genomic relationship matrix (G), and a Bayes C term, i.e. a set of single nucleotide polymorphisms (SNPs) with large effects selected by the model. Computational speed is improved by a Metropolis–Hastings sampling that directs computations to the SNPs, which are, a priori, most likely to be included into the model. Speed is also improved by running many relatively short MCMC chains. Memory requirements are reduced by storing the genotype matrix in binary form. The model was tested on a WGS dataset containing Holstein, Jersey and Australian Red cattle. The data contained 4,809,520 genotypes on 35,549 individuals together with their milk, fat and protein yields, and fat and protein percentage traits.ResultsThe prediction accuracies of the Jersey individuals improved by 1.5% when using across-breed GBLUP compared to within-breed predictions. Using WGS instead of 600 k SNP-chip data yielded on average a 3% accuracy improvement for Australian Red cows. QTL were fine-mapped by locating the SNP with the highest posterior probability of being included in the model. Various QTL known from the literature were rediscovered, and a new SNP affecting milk production was discovered on chromosome 20 at 34.501126 Mb. Due to the high mapping precision, it was clear that many of the discovered QTL were the same across the five dairy traits.ConclusionsAcross-breed Bayes GC genomic prediction improved prediction accuracies compared to GBLUP. The combination of across-breed WGS data and Bayesian genomic prediction proved remarkably effective for the fine-mapping of QTL.

Construction of a high-density linkage map and QTL detection of growth and sex in blotched snakehead (Channa maculata)

High-density genetic linkage maps are comprehensively used in quantitative trait loci (QTLs) mapping, trait-related genes identification, marker-assisted selection, genome assembly and comparative genomic analysis in domestic species. The blotched snakehead (Channa maculata) is an important aquaculture species in China and Southeast Asia, but the genetic improvement was lagged by lacking of genomic information. In this study, the first high-density genetic linkage map was constructed for blotched snakehead using 2b-RAD sequencing technology. The consensus map consists of 6352 single nucleotide polymorphism (SNP) markers that are assigned to 21 linkage groups (LGs) and spanning 2143.7 cM with an average marker interval of 0.34 cM. Comparative genomic analysis revealed 1:1 and/or 1:2 syntenic relationships between blotched snakehead LGs and chromosomes of medaka, European seabass and northern snakehead separately. Nine chromosome-wide QTLs associated with body weight were identified in three LGs, explaining 9.8–11.9% of the phenotypic variation. Putative candidate genes for growth rate, such as bone morphogenetic protein 6 (BMP 6), were identified adjacent to the QTLs. A single major locus tightly linked to sex was identified in LG2 that explained 98.8–100% of the phenotypic variation.Four markers within the sex-linked locus were found to be heterogametic in males and homogametic in females. One sex-specific SNP marker located in the sex QTL region was validated for practical verification of sex in blotched snakehead. Those results suggest that the LG2 may be a sex-related chromosome in blotched snakehead and an XX/XY chromosomal sex determination system in this species. The high-density linkage map and QTLs supply a basis for genome evolutionary studies, QTL fine mapping of economic traits and sex-control breeding in blotched snakehead.

Construction of high-density genetic linkage map of Pyropia yezoensis (Bangiales, Rhodophyta) and identification of red color trait QTLs in the thalli

Pyropia yezoensis is an important macroalga because of its extensive global distribution and economic importance. Color is an important trait in the thalli of P. yezoensis, it is also an effective marker to identify the hybridization in genetic breeding. In this study, a high-density genetic linkage map was constructed based on high-throughput single nucleotide polymorphism (SNP) markers, and used for analyzing the quantitative trait loci (QTLs) of red color trait in the thalli of P. yezoensis. The conchospore undergoes meiosis to develop into an ordered tetrad, and each cell has a haploid phenotype and can grow into a single individual. Based on this theory, F1 haploid population was used as the mapping population. The map included 531 SNP markers, 394.57 cM long on average distance of 0.74 cM. Collinear analysis of the genetic linkage map and the physical map indicated that the coverage between the two maps was 79.42%. Furthermore, QTL mapping identified six QTLs for the chromosomal regions associated with the red color trait of the thalli. The value of phenotypic variance explained (PVE) by an individual QTL ranged from 4.71%–63.11%. And QTL qRed-1-1, with a PVE of 63.11%, was considered the major QTL. Thus, these data may provide a platform for gene and QTL fine mapping, and marker-assisted breeding in P. yezoensis in the future.

Fine mapping QTL and mining genes for protein content in soybean by the combination of linkage and association analysis.

Soybean is one main source of dietary protein; therefore, improving protein content is an important objective in breeding programs. There is a significant negative correlation between protein and oil content, which influenced mapping quantitative trait locus (QTL) and quantitative trait nucleotides for these two traits. In this study, a linkage map was created with 2232 single-nucleotide polymorphism markers for the four-way recombinant inbred line (FW-RIL) population derived from the cross (Kenfeng 14 × Kenfeng 15) × (Heinong 48 × Kenfeng 19), and then conditional and unconditional QTL analyses were carried out by inclusive complete interval mapping based on the phenotypic data of protein and oil content collected in 10 different environments. As shown in the results of linkage analysis, a total of 85 QTL have been detected. We have performed association analysis using 109,676 markers after quality filtering for FW-RIL, and the results have shown that a total of 60 QTNs were detected. We have performed association analysis using 63,306 markers after quality filtering for resource population, and the results have shown that a total of 123 QTNs were detected. We have combined linkage and association analysis, and there are six QTNs verified by FW-RIL and resource population. We have performed pathway analysis on the genes in these six QTN attenuation regions, and the result shows that a total of four candidate genes are related to the synthesis or metabolism of soybean protein. These findings will facilitate marker-assisted selection and molecular breeding of soybean.

Application of 50K chip-based genetic map to QTL mapping of stem-related traits in wheat

Stem strength is critical for lodging resistance in wheat. The present study utilised a high-density genetic map generated with a 50K single-nucleotide polymorphism (SNP) chip to map quantitative trait loci (QTLs) for stem-related traits. The analysis involved 198 recombinant inbred lines (RILs) derived from the cross between bread wheat (Triticum aestivum L.) varieties Xinong1376 and Xiaoyan81. The RILs were planted in randomised complete blocks with two replicates at three locations. The constructed genetic map contained 13571 SNP markers spanning 3605.53 cM across the 21 wheat chromosomes. The genetic and physical positions of SNP markers in 28 linkage groups were approximately the same. Twelve major QTLs related to stem strength were detected for eight traits and were distributed on chromosomes 2D (three QTLs), 3A, 4B, 4D (three QTLs), 5A, 5B, 5D and 6B. Each QTL explained 0.6-34.6% of the phenotypic variation. Taking full advantage of the available resources for fine-mapping of these stable QTLs will benefit molecular marker-assisted breeding and facilitate the dissection of molecular regulatory mechanisms underlying the mechanical properties of the wheat stem.

Fine-Mapping of Sorghum Stay-Green QTL on Chromosome10 Revealed Genes Associated with Delayed Senescence.

This study was conducted to dissect the genetic basis and to explore the candidate genes underlying one of the important genomic regions on an SBI-10 long arm (L), governing the complex stay-green trait contributing to post-flowering drought-tolerance in sorghum. A fine-mapping population was developed from an introgression line cross—RSG04008-6 (stay-green) × J2614-11 (moderately senescent). The fine-mapping population with 1894 F2 was genotyped with eight SSRs and a set of 152 recombinants was identified, advanced to the F4 generation, field evaluated with three replications over 2 seasons, and genotyped with the GBS approach. A high-resolution linkage map was developed for SBI-10L using 260 genotyping by sequencing—Single Nucleotide Polymorphism (GBS–SNPs). Using the best linear unpredicted means (BLUPs) of the percent green leaf area (%GL) traits and the GBS-based SNPs, we identified seven quantitative trait loci (QTL) clusters and single gene, mostly involved in drought-tolerance, for each QTL cluster, viz., AP2/ERF transcription factor family (Sobic.010G202700), NBS-LRR protein (Sobic.010G205600), ankyrin-repeat protein (Sobic.010G205800), senescence-associated protein (Sobic.010G270300), WD40 (Sobic.010G205900), CPK1 adapter protein (Sobic.010G264400), LEA2 protein (Sobic.010G259200) and an expressed protein (Sobic.010G201100). The target genomic region was thus delimited from 15 Mb to 8 genes co-localized with QTL clusters, and validated using quantitative real-time (qRT)–PCR.

Development of Chromosome Segment Substitution Lines (CSSLs) Derived from Guangxi Wild Rice (Oryza rufipogon Griff.) under Rice (Oryza sativa L.) Background and the Identification of QTLs for Plant Architecture, Agronomic Traits and Cold Tolerance.

Common wild rice contains valuable resources of novel alleles for rice improvement. It is well known that genetic populations provide the basis for a wide range of genetic and genomic studies. In particular, chromosome segment substitution lines (CSSLs) ais a powerful tool for fine mapping of quantitative traits, new gene discovery and marker-assisted breeding. In this study, 132 CSSLs were developed from a cultivated rice (Oryza sativa) cultivar (93-11) and common wild rice (Oryza rufipogon Griff. DP30) by selfing-crossing, backcrossing and marker-assisted selection (MAS). Based on the high-throughput sequencing of the 93-11 and DP30, 285 pairs of Insertion-deletions (InDel) markers were selected with an average distance of 1.23 Mb. The length of this DP30-CSSLs library was 536.4 cM. The coverage rate of substitution lines cumulatively overlapping the whole genome of DP30 was about 91.55%. DP30-CSSLs were used to analyze the variation for 17 traits leading to the detection of 36 quantitative trait loci (QTLs) with significant phenotypic effects. A cold-tolerant line (RZ) was selected to construct a secondary mapping F2 population, which revealed that qCT2.1 is in the 1.7 Mb region of chromosome 2. These CSSLs may, therefore, provide powerful tools for genome wide large-scale gene discovery in wild rice. This research will also facilitate fine mapping and cloning of QTLs and genome-wide study of wild rice. Moreover, these CSSLs will provide a foundation for rice variety improvement.

High-Resolution Genome-wide Association Study Identifies Genomic Regions and Candidate Genes for Important Agronomic Traits in Wheat

Wheat (Triticum aestivum) is a major staple food crop worldwide. Genetic dissection of important agronomic traits is essential for continuous improvement of wheat yield to meet the demand of the world's growing population. We conducted a large-scale genome-wide association study (GWAS) using a panel of 768 wheat cultivars that were genotyped with 327 609 single-nucleotide polymorphisms generated by genotyping-by-sequencing and detected 395 quantitative trait loci (QTLs) for 12 traits under 7 environments. Among them, 273 QTLs were delimited to ≤1.0-Mb intervals and 7 of them are either known genes (Rht-D, Vrn-B1, and Vrn-D1) that have been cloned or known QTLs (TaGA2ox8, APO1, TaSus1-7B, and Rht12) that were previously mapped. Eight putative candidate genes were identified for three QTLs that enhance spike seed setting and grain size using gene expression data and were validated in three bi-parental populations. Protein sequence analysis identified 33 putative wheat orthologs that have high identity with rice genes in QTLs affecting similar traits. Large r2 values for additive effects observed among the QTLs for most traits indicated that the phenotypes of these identified QTLs were highly predictable. Results from this study demonstrated that significantly increasing GWAS population size and marker density greatly improves detection and identification of candidate genes underlying a QTL, solidifying the foundation for large-scale QTL fine mapping, candidate gene validation, and developing functional markers for genomics-based breeding in wheat.

Design of experiments for fine-mapping quantitative trait loci in livestock populations

BackgroundSingle nucleotide polymorphisms (SNPs) which capture a significant impact on a trait can be identified with genome-wide association studies. High linkage disequilibrium (LD) among SNPs makes it difficult to identify causative variants correctly. Thus, often target regions instead of single SNPs are reported. Sample size has not only a crucial impact on the precision of parameter estimates, it also ensures that a desired level of statistical power can be reached. We study the design of experiments for fine-mapping of signals of a quantitative trait locus in such a target region.MethodsA multi-locus model allows to identify causative variants simultaneously, to state their positions more precisely and to account for existing dependencies. Based on the commonly applied SNP-BLUP approach, we determine the z-score statistic for locally testing non-zero SNP effects and investigate its distribution under the alternative hypothesis. This quantity employs the theoretical instead of observed dependence between SNPs; it can be set up as a function of paternal and maternal LD for any given population structure.ResultsWe simulated multiple paternal half-sib families and considered a target region of 1 Mbp. A bimodal distribution of estimated sample size was observed, particularly if more than two causative variants were assumed. The median of estimates constituted the final proposal of optimal sample size; it was consistently less than sample size estimated from single-SNP investigation which was used as a baseline approach. The second mode pointed to inflated sample sizes and could be explained by blocks of varying linkage phases leading to negative correlations between SNPs. Optimal sample size increased almost linearly with number of signals to be identified but depended much stronger on the assumption on heritability. For instance, three times as many samples were required if heritability was 0.1 compared to 0.3. An R package is provided that comprises all required tools.ConclusionsOur approach incorporates information about the population structure into the design of experiments. Compared to a conventional method, this leads to a reduced estimate of sample size enabling the resource-saving design of future experiments for fine-mapping of candidate variants.

Quantitative trait loci analysis for root traits in synthetic hexaploid wheat under drought stress conditions

Synthetic hexaploid wheat (SHW), possesses numerous genes for drought that can help breeding for drought-tolerant wheat varieties. We evaluated 10 root traits at seedling stage in 111 F9 recombinant inbred lines derived from a F2 population of a SHW line (SHW-L1) and a common wheat line, under normal (NC) and polyethylene glycol-simulated drought stress conditions (DC). We mapped quantitative trait loci (QTLs) for root traits using an enriched high-density genetic map containing 120370 single nucleotide polymorphisms (SNPs), 733 diversity arrays technology markers (DArT) and 119 simple sequence repeats (SSRs). With four replicates per treatment, we identified 19 QTLs for root traits under NC and DC, and 12 of them could be consistently detected with three or four replicates. Two novel QTLs for root fresh weight and root diameter under NC explained 9 and 15.7% of the phenotypic variation respectively, and six novel QTLs for root fresh weight, the ratio of root water loss, total root surface area, number of root tips, and number of root forks under DC explained 8.5–14% of the phenotypic variation. Here seven of eight novel QTLs could be consistently detected with more than three replicates. Results provide essential information for fine-mapping QTLs related to drought tolerance that will facilitate breeding drought-tolerant wheat cultivars.

High-density genetic linkage map construction and cane cold hardiness QTL mapping for Vitis based on restriction site-associated DNA sequencing

BackgroundCold hardiness is an important agronomic trait and can significantly affect grape production and quality. Until now, there are no reports focusing on cold hardiness quantitative trait loci (QTL) mapping. In this study, grapevine interspecific hybridisation was carried out with the maternal parent ‘Cabernet sauvignon’ and paternal parent ‘Zuoyouhong’. A total of 181 hybrid offspring and their parents were used as samples for restriction-site associated DNA sequencing (RAD). Grapevine cane phloem and xylem cold hardiness of the experimental material was detected using the low-temperature exotherm method in 2016, 2017 and 2018. QTL mapping was then conducted based on the integrated map.ResultsWe constructed a high-density genetic linkage map with 16,076, 11,643, and 25,917 single-nucleotide polymorphism (SNP) markers anchored in the maternal, paternal, and integrated maps, respectively. The average genetic distances of adjacent markers in the maps were 0.65 cM, 0.77 cM, and 0.41 cM, respectively. Colinearity analysis was conducted by comparison with the grape reference genome and showed good performance. Six QTLs were identified based on the phenotypic data of 3 years and they were mapped on linkage group (LG) 2, LG3, and LG15. Based on QTL results, candidate genes which may be involved in grapevine cold hardiness were selected.ConclusionsHigh-density linkage maps can facilitate grapevine fine QTL mapping, genome comparison, and sequence assembly. The cold hardiness QTL mapping and candidate gene discovery performed in this study provide an important reference for molecular-assisted selection in grapevine cold hardiness breeding.

Target region sequencing and applications in plants

Fine mapping quantitative trait loci (QTL) and understanding the molecular mechanism and underlying functions of genes are vital to the advancement of plant research and plant breeding. Target region sequencing (TRS) provides an efficient way to assist in accomplishing these goals. Once a genomic region is found to be responsible for a phenotype, TRS can be deployed to characterize that region. Understanding the available methodologies to perform TRS will be advantageous when determining how to proceed with specific projects. In this article, we reviewed and summarized four main methods that are being used for TRS in plants, including hybrid capture, PCR-based enrichment, DNA fragment circularization, and RNA capture. We also discussed the potential uses of these technologies in plant research, especially the marker discovery for breeding selection. The TRS techniques outlined in this review have the potential to become commonplace in plant research, thereby having a large impact.

Genetic dissection of husk number and length across multiple environments and fine-mapping of a major-effect QTL for husk number in maize (Zea mays L.)

Husk number (HN) and husk length (HL) influence the mechanical harvesting of maize grain. We investigated the genetic basis of HN and HL using a population of 204 recombinant inbred lines phenotypically evaluated in five environments. The two husk traits showed broad phenotypic variation and high heritability. Nine stable quantitative trait loci (QTL) were identified by single-environment mapping, comprising four QTL for HN and five for HL, and three QTL explained >10% of the phenotypic variation. Joint mapping revealed 22 additive QTL and 46 epistatic QTL. Both additive and epistatic (additive × additive) effects as well as a few large-effect QTL and some minor-effect QTL appeared to contribute to the genetic architecture of HN and HL. The QTL for HN located on chromosome 7, qHN7, which accounted for ~20% of phenotypic variation, was detected in all five environments. qHN7 was fine-mapped to a 721.1 kb physical region based on the maize B73 RefGen_v3 genome assembly. Within this interval, four genes associated with plant growth and development were selected as candidate genes. The results will be useful for improvement of maize husk traits by molecular breeding and provide a basis for the cloning of qHN7.

Fine-mapping and candidate gene analysis of qFS-Chr. D02, a QTL for fibre strength introgressed from a semi-wild cotton into Gossypium hirsutum

Fibre strength (FS) is an important quality attribute in the modern textile industry, which is genetically controlled by quantitative trait loci (QTLs). Fine-mapping stable QTLs for FS to identify candidate genes would be valuable for uncovering the genetic basis of fibre quality traits in cotton. Here, a single segment introgression line, IL-D2−2, from the cross of (TM-1×TX-1046) reported in our previous studies, was found to have significantly improved FS compared with the recurrent parent TM-1. To fine-map the QTLs of the FS, we further crossed IL-D2−2 with its recurrent parent TM-1 to produce F2 and F2:3 populations. QTL analysis and substitution mapping showed qFS-Chr. D02 was anchored into a 550.66 kb-interval between two markers, INTR1027 and JESPR-231. This interval contained 67 genes, among which 27 genes related to cell-wall synthesis were selected to conduct qRT-PCR. The results revealed seven genes were expressed significantly differently during the fibre secondary-wall-thickening stage (10–25 days post-anthesis), three being upregulated and four downregulated in IL-D2−2. Both GH_D02G2269 (UDP-glucosyl transferase 84B1) and GH_D02G2289 (unknown function (DUF869)) with nonsynonymous SNPs in IL-D2−2 had significantly downregulated expression, suggesting they were candidates for qFS-Chr. D02. This research provides information about marker-assisted selection for cotton fibre strength improvement.