Quantitative Trait Loci Regions Research Articles

Complex genetic architecture of the chicken Growth1 QTL region.

The genetic complexity of polygenic traits represents a captivating and intricate facet of biological inheritance. Unlike Mendelian traits controlled by a single gene, polygenic traits are influenced by multiple genetic loci, each exerting a modest effect on the trait. This cumulative impact of numerous genes, interactions among them, environmental factors, and epigenetic modifications results in a multifaceted architecture of genetic contributions to complex traits. Given the well-characterized genome, diverse traits, and range of genetic resources, chicken (Gallus gallus) was employed as a model organism to dissect the intricate genetic makeup of a previously identified major Quantitative Trait Loci (QTL) for body weight on chromosome 1. A multigenerational advanced intercross line (AIL) of 3215 chickens whose genomes had been sequenced to an average of 0.4x was analyzed using genome-wide association study (GWAS) and variance-heterogeneity GWAS (vGWAS) to identify markers associated with 8-week body weight. Additionally, epistatic interactions were studied using the natural and orthogonal interaction (NOIA) model. Six genetic modules, two from GWAS and four from vGWAS, were strongly associated with the studied trait. We found evidence of both additive- and non-additive interactions between these modules and constructed a putative local epistasis network for the region. Our screens for functional alleles revealed a missense variant in the gene ribonuclease H2 subunit B (RNASEH2B), which has previously been associated with growth-related traits in chickens and Darwin's finches. In addition, one of the most strongly associated SNPs identified is located in a non-coding region upstream of the long non-coding RNA, ENSGALG00000053256, previously suggested as a candidate gene for regulating chicken body weight. By studying large numbers of individuals from a family material using approaches to capture both additive and non-additive effects, this study advances our understanding of genetic complexities in a highly polygenic trait and has practical implications for poultry breeding and agriculture.

Genome-wide association study on seed dormancy in barley

Abstract Seed dormancy is an important trait associated with pre-sprouting and malting quality in barley. Genome-wide association studies (GWASs) have been used to detect quantitative trait loci (QTLs) underlying complex traits in major crops. In the present study, we collected 295 barley (Hordeum vulgare L.) accessions from Australia, Europe, Canada and China. A total of 25,179 single nucleotide polymorphism (SNP)/diversity arrays technology sequence markers were used for population structure, linkage disequilibrium and GWAS analysis. Candidate genes within QTL regions were investigated, and their expression levels were analysed using RNAseq data. Five QTLs for seed dormancy were identified. One QTL was mapped on chromosome 1H, and one QTL was mapped on chromosome 4H, while three QTLs were located on chromosome 5H. This is the first report of a QTL on the short arm of chromosome 5H in barley. Molecular markers linked to the QTL can be used for marker-assisted selection in barley breeding programmes.

Identification of Quantitative Trait Loci Controlling Root Morphological Traits in an Interspecific Soybean Population Using 2D Imagery Data.

Roots are the hidden and most important part of plants. They serve as stabilizers and channels for uptaking water and nutrients and play a crucial role in the growth and development of plants. Here, two-dimensional image data were used to identify quantitative trait loci (QTL) controlling root traits in an interspecific mapping population derived from a cross between wild soybean 'PI366121' and cultivar 'Williams 82'. A total of 2830 single-nucleotide polymorphisms were used for genotyping, constructing genetic linkage maps, and analyzing QTLs. Forty-two QTLs were identified on twelve chromosomes, twelve of which were identified as major QTLs, with a phenotypic variation range of 36.12% to 39.11% and a logarithm of odds value range of 12.01 to 17.35. Two significant QTL regions for the average diameter, root volume, and link average diameter root traits were detected on chromosomes 3 and 13, and both wild and cultivated soybeans contributed positive alleles. Six candidate genes, Glyma.03G027500 (transketolase/glycoaldehyde transferase), Glyma.03G014500 (dehydrogenases), Glyma.13G341500 (leucine-rich repeat receptor-like protein kinase), Glyma.13G341400 (AGC kinase family protein), Glyma.13G331900 (60S ribosomal protein), and Glyma.13G333100 (aquaporin transporter) showed higher expression in root tissues based on publicly available transcriptome data. These results will help breeders improve soybean genetic components and enhance soybean root morphological traits using desirable alleles from wild soybeans.

Genome-wide association study identified five quantitative trait loci and two candidate genes for digestive traits in Suhuai pigs.

This work aimed to identify markers and candidate genes underlying porcine digestive traits. In total, 331 pigs were genotyped by 80 K Chip data or 50 K Chip data. For apparent neutral detergent fiber digestibility, a total of 19 and 21 candidate single nucleotide polymorphisms (SNP) were respectively identified using a genome-wide efficient mixed-model association algorithm and linkage-disequilibrium adjusted kinship. Among them, three quantitative trait locus (QTL) regions were identified. For apparent acid detergent fiber digestibility, a total of 16 and 17 SNPs were identified by these two methods, respectively. Of these, three QTL regions were also identified. Moreover, two candidate genes (MST1 and LATS1), which are functionally related to intestinal homeostasis and health, were detected near these significant SNPs. Taken together, our results could provide a basis for deeper research on digestive traits in pigs.

A Summary of Two Decades of QTL and Candidate Genes That Control Seed Tocopherol Contents in Maize (Zea mays L.).

Tocopherols are secondary metabolites synthesized through the shikimate biosynthetic pathway in the plastids of most plants. It is well known that α-Tocopherol (vitamin E) has many health benefits for humans and animals; therefore, it is highly used in human and animal diets. Tocopherols vary considerably in most crop (and plant) species and within cultivars of the same species depending on environmental and growth conditions; tocopherol content is a polygenic, complex traits, and its inheritance is poorly understood. The objective of this review paper was to summarize all identified quantitative trait loci (QTL) that control seed tocopherols and related contents identified in maize (Zea mays) during the past two decades (2002-2022). Candidate genes identified within these QTL regions are also discussed. The QTL described here, and candidate genes identified within these genomic regions could be used in breeding programs to develop maize cultivars with high, beneficial levels of seed tocopherol contents.

Molecular mapping of the broad bean wilt virus 2 resistance locus bwvr in Capsicum annuum using BSR-seq

Key messageBulked segregant RNA seq of pools of pepper accessions that are susceptible or resistant to Broad bean wilt virus 2 identifies a gene that might confer resistance to this devastating pathogen.The single-stranded positive-sense RNA virus Broad bean wilt virus 2 (BBWV2) causes substantial damage to pepper (Capsicum annuum) cultivation. Here, we describe mapping the BBWV2 resistance locus bwvr using a F7:8 recombinant inbred line (RIL) population constructed by crossing the BBWV2-resistant pepper accession ‘SNU-C’ with the susceptible pepper accession ‘ECW30R.’ All F1 plants infected with the BBWV2 strain PAP1 were susceptible to the virus, and the RIL population showed a 1:1 ratio of resistance to susceptibility, indicating that this trait is controlled by a single recessive gene. To map bwvr, we performed bulked segregant RNA-seq (BSR-seq). We sequenced pools of resistant and susceptible lines from the RILs and aligned the reads to the high-quality ‘Dempsey’ reference genome to identify variants between the pools. This analysis identified 519,887 variants and selected the region from 245.9–250.8 Mb of the Dempsey reference genome as the quantitative trait locus region for bwvr. To finely map bwvr, we used newly designed high-resolution melting (HRM) and Kompetitive allele specific PCR (KASP) markers based on variants obtained from the BSR-seq reads and the PepperSNP16K array. Comparative analysis identified 11 SNU-C-specific SNPs within the bwvr locus. Using markers derived from these variants, we mapped the candidate bwvr locus to the region from 246.833–246.949 kb. SNU-C-specific variants clustered near DEM.v1.00035533 within the bwvr locus. DEM.v1.00035533 encodes the nitrate transporter NPF1.2 and contains a SNP within its 5′ untranslated region. The bwvr locus, which contains four genes including DEM.v1.00035533, could represent a valuable resource for global pepper breeding programs.

Transcriptome sequencing and expression analysis in peanut reveal the potential mechanism response to Ralstonia solanacearum infection

BackgroundBacterial wilt caused by Ralstonia solanacearum severely affects peanut (Arachis hypogaea L.) yields. The breeding of resistant cultivars is an efficient means of controlling plant diseases. Therefore, identification of resistance genes effective against bacterial wilt is a matter of urgency. The lack of a reference genome for a resistant genotype severely hinders the process of identification of resistance genes in peanut. In addition, limited information is available on disease resistance-related pathways in peanut.ResultsFull-length transcriptome data were used to generate wilt-resistant and -susceptible transcript pools. In total, 253,869 transcripts were retained to form a reference transcriptome for RNA-sequencing data analysis. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of differentially expressed genes revealed the plant-pathogen interaction pathway to be the main resistance-related pathway for peanut to prevent bacterial invasion and calcium plays an important role in this pathway. Glutathione metabolism was enriched in wilt-susceptible genotypes, which would promote glutathione synthesis in the early stages of pathogen invasion. Based on our previous quantitative trait locus (QTL) mapping results, the genes arahy.V6I7WA and arahy.MXY2PU, which encode nucleotide-binding site-leucine-rich repeat receptor proteins, were indicated to be associated with resistance to bacterial wilt.ConclusionsThis study identified several pathways associated with resistance to bacterial wilt and identified candidate genes for bacterial wilt resistance in a major QTL region. These findings lay a foundation for investigation of the mechanism of resistance to bacterial wilt in peanut.

Genetic variability and QTL mapping of morphological traits and inflorescence prolificacy in African bermudagrass

AbstractAfrican bermudagrass (Cynodon transvaalensis Burtt‐Davy) (2n = 2x = 18) has unique morphological characteristics such as finer leaf blades and shorter internodes that are important to improving turf qualities (i.e., texture and density) as compared to other Cynodon species. It has been extensively used to cross with common bermudagrass (Cynodon dactylon Pers. var. dactylon) in developing high‐quality interspecific [C. dactylon (L.) Pers. × C. transvaalensis Burtt‐Davy] F1 hybrid cultivars for turf use. However, the molecular basis of its morphological variation is unknown. Accordingly, the objectives of this study were to estimate the heritability and identify quantitative trait loci (QTL) associated with morphological and reproductive traits. A first‐generation self‐pollinated (S1) population of 109 individuals was evaluated for plant height (PH), leaf blade width (LBW) and leaf blade length, stem internode diameter and length, and inflorescence prolificacy (IP) in a replicated field trial over three seasons (2018–2020). The broad‐sense heritability estimates ranged from 0.31 (PH) to 0.80 (IP). Twenty‐four QTL were identified and nine of them were consistent ones. Fifteen candidate genes were found at a major and consistent QTL region associated with LBW. Frequent QTL colocations were found among morphological traits and between morphological traits and IP, partially explaining the significant correlation. The findings provide critical information and resources toward understanding the genetic basis associated with morphological and reproductive traits within C. transvaalensis and could contribute to marker‐assisted selection for breeding new turf‐type bermudagrass cultivars.

Meta-QTL analysis and candidate genes for quality traits, mineral content, and abiotic-related traits in wild emmer.

Wild emmer (Triticum turgidum ssp. dicoccoides) genotypes were studied for their high-nutritional value and good tolerance to various types of stress; for this reason, several QTL (quantitative trait loci) studies have been conducted to find favorable alleles to be introgressed into modern wheat cultivars. Given the complexity of the QTL nature, their interaction with the environment, and other QTLs, a small number of genotypes have been used in wheat breeding programs. Meta-QTL (MQTL) analysis helps to simplify the existing QTL information, identifying stable genomic regions and possible candidate genes for further allele introgression. The study aimed to identify stable QTL regions across different environmental conditions and genetic backgrounds using the QTL information of the past 14 years for different traits in wild emmer based upon 17 independent studies. A total of 41 traits were classified as quality traits (16), mineral composition traits (11), abiotic-related traits (13), and disease-related traits (1). The analysis revealed 852 QTLs distributed across all 14 chromosomes of wild emmer, with an average of 61 QTLs per chromosome. Quality traits had the highest number of QTLs (35%), followed by mineral content (33%), abiotic-related traits (28%), and disease-related traits (4%). Grain protein content (GPC) and thousand kernel weight (TKW) were associated with most of the QTLs detected. A total of 43 MQTLs were identified, simplifying the information, and reducing the average confidence interval (CI) from 22.6 to 4.78 cM. These MQTLs were associated with multiple traits across different categories. Nine candidate genes were identified for several stable MQTLs, potentially contributing to traits such as quality, mineral content, and abiotic stress resistance. These genes play essential roles in various plant processes, such as carbohydrate metabolism, nitrogen assimilation, cell wall biogenesis, and cell wall extensibility. Overall, this study underscores the importance of considering MQTL analysis in wheat breeding programs, as it identifies stable genomic regions associated with multiple traits, offering potential solutions for improving wheat varieties under diverse environmental conditions.

A high-density genetic linkage map and QTL identification for growth traits in dusky kob (Argyrosomus japonicus)

High-density genetic linkage maps provide a foundational genomic resource for quantitative trait locus (QTL) mapping, candidate gene localisation, and comparative genomic analysis. Data from such studies, in turn, can be incorporated into marker or genomics assisted selective breeding strategies for increased efficiency of aquaculture production. This study aimed to construct the first high-density genetic linkage map for dusky kob (Argyrosomus japonicus) a commercially important marine finfish in Asia, Australia, and South Africa. Using 2b-restriction site-associated DNA (2b-RAD) sequencing for genome-wide single nucleotide polymorphism (SNP) genotyping, in three full-sib F1 families (Family 1, n = 69 offspring; Family 2, n = 73 offspring; and Family 3, n = 70 offspring), led to the discovery and genotyping of 22,789 SNPs. Among these, 3992 quality filtered and informative SNPs were mapped to 24 linkage groups (LGs), aligning with the species' proposed haploid chromosome number. The total integrated genetic map spanned a length of 2550 cM with an average marker spacing of 0.68 cM, achieving a genome coverage of 98.61%. Male and female specific maps highlighted differential map lengths and recombination rates between the sexes, with the female map slightly longer and a lower recombination frequency than for the male. QTL analysis for growth-related traits, weight (W), standard length (sL), and Fulton's condition factor (K), identified a total of 25 QTLs of which five QTLs were found to be consistently shared across the traits. Collectively, these shared QTLs explained a substantial proportion of the phenotypic variance, accounting for between 9.30 and 19.30% of the observed variation, suggesting potential major genes for growth. These QTL regions led to the discovery of 65 potential candidate genes, including 11 genes that were specifically located within the shared QTLs. These genes were linked to essential biological processes, including metabolism, immunity, stress response, development, and vascular function. The establishment of this high-density genetic linkage map represents an important genomic resource for comprehending the genetic determinants of economically important traits in A. japonicus, with implications for advancing and optimising ongoing marker-assisted selection (MAS) initiatives in the species.

Identification of QTLs associated with seed protein concentration in two diverse recombinant inbred line populations of pea.

Improving the seed protein concentration (SPC) of pea (Pisum sativum L.) has turned into an important breeding objective because of the consumer demand for plant-based protein and demand from protein fractionation industries. To support the marker-assisted selection (MAS) of SPC towards accelerated breeding of improved cultivars, we have explored two diverse recombinant inbred line (RIL) populations to identify the quantitative trait loci (QTLs) associated with SPC. The two RIL populations, MP 1918 × P0540-91 (PR-30) and Ballet × Cameor (PR-31), were derived from crosses between moderate SPC × high SPC accessions. A total of 166 and 159 RILs of PR-30 and PR-31, respectively, were genotyped using an Axiom® 90K SNP array and 13.2K SNP arrays, respectively. The RILs were phenotyped in replicated trials in two and three locations of Saskatchewan, Canada in 2020 and 2021, respectively, for agronomic assessment and SPC. Using composite interval mapping, we identified three QTLs associated with SPC in PR-30 and five QTLs in PR-31, with the LOD value ranging from 3.0 to 11.0. A majority of these QTLs were unique to these populations compared to the previously known QTLs for SPC. The QTL SPC-Ps-5.1 overlapped with the earlier reported SPC associated QTL PC-QTL-3. Three QTLs, SPC-Ps-4.2, SPC-Ps-5.1, and SPC-Ps-7.2 with LOD scores of 7.2, 7.9, and 11.3, and which explained 14.5%, 11.6%, and 11.3% of the phenotypic variance, respectively, can be used for marker-assisted breeding to increase SPC in peas. Eight QTLs associated with the grain yield were identified with LOD scores ranging from 3.1 to 8.2. Two sets of QTLs, SPC-Ps-2.1 and GY-Ps-2.1, and SPC-Ps-5.1 and GY-Ps-5.3, shared the QTL/peak regions. Each set of QTLs contributed to either SPC or grain yield depending on which parent the QTL region is derived from, thus confirming that breeding for SPC should take into consideration the effects on grain yield.

Genome-Wide Association Analysis of Effective Tillers in Rice under Different Nitrogen Gradients.

Nitrogen is a crucial element that impacts rice yields, and effective tillering is a significant agronomic characteristic that can influence rice yields. The way that reduced nitrogen affects effective tillering is a complex quantitative trait that is controlled by multiple genes, and its genetic basis requires further exploration. In this study, 469 germplasm varieties were used for a genome-wide association analysis aiming to detect quantitative trait loci (QTL) associated with effective tillering at low (60 kg/hm2) and high (180 kg/hm2) nitrogen levels. QTLs detected over multiple years or under different treatments were scrutinized in this study, and candidate genes were identified through haplotype analysis and spatio-temporal expression patterns. A total of seven genes (NAL1, OsCKX9, Os01g0690800, Os02g0550300, Os02g0550700, Os04g0615700, and Os04g06163000) were pinpointed in these QTL regions, and were considered the most likely candidate genes. These results provide favorable information for the use of auxiliary marker selection in controlling effective tillering in rice for improved yields.

Identification of the domestication gene GmCYP82C4 underlying the major quantitative trait locus for the seed weight in soybean.

A major quantitative trait locus (QTL) for the hundred-seed weight (HSW) was identified and confirmed in the two distinct soybean populations, and the target gene GmCYP82C4 underlying this locus was identified that significantly associated with soybean seed weight, and it was selected during the soybean domestication and improvement process. Soybean is a major oil crop for human beings and the seed weight is a crucial goal of soybean breeding. However, only a limited number of target genes underlying the quantitative trait loci (QTLs) controlling seed weight in soybean are known so far. In the present study, six loci associated with hundred-seed weight (HSW) were detected in the first population of 573 soybean breeding lines by genome-wide association study (GWAS), and 64 gene models were predicted in these candidate QTL regions. The QTL qHSW_1 exhibits continuous association signals on chromosome four and was also validated by region association study (RAS) in the second soybean population (409 accessions) with wild, landrace, and cultivar soybean accessions. There were seven genes in qHSW_1 candidate region by linkage disequilibrium (LD) block analysis, and only Glyma.04G035500 (GmCYP82C4) showed specifically higher expression in flowers, pods, and seeds, indicating its crucial role in the soybean seed development. Significant differences in HSW trait were detected when the association panels are genotyped by single-nucleotide polymorphisms (SNPs) in putative GmCYP82C4 promoter region. Eight haplotypes were generated by six SNPs in GmCYP82C4 in the second soybean population, and two superior haplotypes (Hap2 and Hap4) of GmCYP82C4 were detected with average HSW of 18.27g and 18.38g, respectively. The genetic diversity of GmCYP82C4 was analyzed in the second soybean population, and GmCYP82C4 was most likely selected during the soybean domestication and improvement process, leading to the highest proportion of Hap2 of GmCYP82C4 both in landrace and cultivar subpopulations. The QTLs and GmCYP82C4 identified in this study provide novel genetic resources for soybean seed weight trait, and the GmCYP82C4 could be used for soybean molecular breeding to develop desirable seed weight in the future.

Identification of quantitative trait loci (QTLs) regulating leaf SPAD value and trichome density in mungbean (Vigna radiata L.) using genotyping-by-sequencing (GBS) approach.

Quantitative trait loci (QTL) mapping is used for the precise localization of genomic regions regulating various traits in plants. Two major QTLs regulating Soil Plant Analysis Development (SPAD) value (qSPAD-7-1) and trichome density (qTric-7-2) in mungbean were identified using recombinant inbred line (RIL) populations (PMR-1×Pusa Baisakhi) on chromosome 7. Functional analysis of QTL region identified 35 candidate genes for SPAD value (16 No) and trichome (19 No) traits. The candidate genes regulating trichome density on the dorsal leaf surface of the mungbean include VRADI07G24840, VRADI07G17780, and VRADI07G15650, which encodes for ZFP6, TFs bHLH DNA-binding superfamily protein, and MYB102, respectively. Also, candidate genes having vital roles in chlorophyll biosynthesis are VRADIO7G29860, VRADIO7G29450, and VRADIO7G28520, which encodes for s-adenosyl-L-methionine, FTSHI1 protein, and CRS2-associated factor, respectively. The findings unfolded the opportunity for the development of customized genotypes having high SPAD value and high trichome density having a possible role in yield and mungbean yellow vein mosaic India virus (MYMIV) resistance in mungbean.

Dissection of the E8 locus in two early maturing Canadian soybean populations.

Soybean [Glycine max (L.) Merr.] is a short-day crop for which breeders want to expand the cultivation range to more northern agro-environments by introgressing alleles involved in early reproductive traits. To do so, we investigated quantitative trait loci (QTL) and expression quantitative trait loci (eQTL) regions comprised within the E8 locus, a large undeciphered region (~7.0 Mbp to 44.5 Mbp) associated with early maturity located on chromosome GM04. We used a combination of two mapping algorithms, (i) inclusive composite interval mapping (ICIM) and (ii) genome-wide composite interval mapping (GCIM), to identify major and minor regions in two soybean populations (QS15524F2:F3 and QS15544RIL) having fixed E1, E2, E3, and E4 alleles. Using this approach, we identified three main QTL regions with high logarithm of the odds (LODs), phenotypic variation explained (PVE), and additive effects for maturity and pod-filling within the E8 region: GM04:16,974,874-17,152,230 (E8-r1); GM04:35,168,111-37,664,017 (E8-r2); and GM04:41,808,599-42,376,237 (E8-r3). Using a five-step variant analysis pipeline, we identified Protein far-red elongated hypocotyl 3 (Glyma.04G124300; E8-r1), E1-like-a (Glyma.04G156400; E8-r2), Light-harvesting chlorophyll-protein complex I subunit A4 (Glyma.04G167900; E8-r3), and Cycling dof factor 3 (Glyma.04G168300; E8-r3) as the most promising candidate genes for these regions. A combinatorial eQTL mapping approach identified significant regulatory interactions for 13 expression traits (e-traits), including Glyma.04G050200 (Early flowering 3/E6 locus), with the E8-r3 region. Four other important QTL regions close to or encompassing major flowering genes were also detected on chromosomes GM07, GM08, and GM16. In GM07:5,256,305-5,404,971, a missense polymorphism was detected in the candidate gene Glyma.07G058200 (Protein suppressor of PHYA-105). These findings demonstrate that the locus known as E8 is regulated by at least three distinct genomic regions, all of which comprise major flowering genes.

Genome-Wide Association Study Reveals a QTL Region for Ashy Stem Blight Resistance in PRA154 Andean Common Bean.

Ashy stem blight (ASB) caused by Macrophomina phaseolina (Tassi) Goidanich affects the common bean (Phaseolus vulgaris L.) at all growing stages. Higher levels of resistance were observed in Andean common beans, but specific resistant quantitative trait loci (QTLs) conferring resistance to this pathogen have not been reported in this gene pool. The objectives of this research were to: (i) conduct a genome-wide association study (GWAS) and QTL mapping for resistance in the Andean breeding line PRA154; and (ii) identify single nucleotide polymorphism (SNP) markers and candidate genes for ASB resistance. Phenotyping was conducted under greenhouse conditions by inoculating the 107 F6:7 recombinant inbred lines (RILs) derived from the cross between the susceptible cultivar 'Verano' and the partial-resistant breeding line PRA154 twice with the M. phaseolina isolate PRI21. Genotyping was performed with 109,040 SNPs distributed across all 11 P. vulgaris chromosomes. A novel major QTL was located between 28,761,668 and 31,263,845 bp, extending 2.5 Mbp on chromosome Pv07, and the highest significant SNP markers were Chr07_28761668_A_G, Chr07_29131720_G_A, and Chr07_31263845_C_T with the highest LOD (more than 10 in most of the cases) and R-squared values, explaining 40% of the phenotypic variance of the PRI21 isolate. The gene Phvul.007G173900 (methylcrotonyl-CoA carboxylase alpha chain and mitochondrial 3-methylcrotonyl-CoA carboxylase 1 [MCCA]) with a size of 10,891 bp, located between 29,131,591 and 29,142,481 bp on Pv07, was identified as one candidate for ASB resistance in PRA154, and it contained Chr07_29131720_G_A. The QTL and genetic marker information could be used to assist common bean breeders to develop germplasm and cultivars with ASB resistance through molecular breeding.

Genotyping by sequencing for the construction of oil palm (Elaeis guineensis Jacq.) genetic linkage map and mapping of yield related quantitative trait loci.

Oil palm (Elaeis guineensis Jacq.) is one of the major oil-producing crops. Improving the quality and increasing the production yield of oil palm have been the primary focuses of both conventional and modern breeding approaches. However, the conventional breeding approach for oil palm is very challenging due to its longevity, which results in a long breeding cycle. Thus, the establishment of marker assisted selection (MAS) for oil palm breeding programs would speed up the breeding pipeline by generating new oil palm varieties that possess high commercial traits. With the decreasing cost of sequencing, Genotyping-by-sequencing (GBS) is currently feasible to many researchers and it provides a platform to accelerate the discovery of single nucleotide polymorphism (SNP) as well as insertion and deletion (InDel) markers for the construction of a genetic linkage map. A genetic linkage map facilitates the identification of significant DNA regions associated with the trait of interest via quantitative trait loci (QTL) analysis. A mapping population of 112 F1 individuals from a cross of Deli dura and Serdang pisifera was used in this study. GBS libraries were constructed using the double digestion method with HindIII and TaqI enzymes. Reduced representation libraries (RRL) of 112 F1 progeny and their parents were sequenced and the reads were mapped against the E. guineensis reference genome. To construct the oil palm genetic linkage map, informative SNP and InDel markers were used to discover significant DNA regions associated with the traits of interest. The nine traits of interest in this study were fresh fruit bunch (FFB) yield, oil yield (OY), oil to bunch ratio (O/B), oil to dry mesocarp ratio (O/DM) ratio, oil to wet mesocarp ratio (O/WM), mesocarp to fruit ratio (M/F), kernel to fruit ratio (K/F), shell to fruit ratio (S/F), and fruit to bunch ratio (F/B). A total of 2.5 million SNP and 153,547 InDel markers were identified. However, only a subset of 5,278 markers comprising of 4,838 SNPs and 440 InDels were informative for the construction of a genetic linkage map. Sixteen linkage groups were produced, spanning 2,737.6 cM for the maternal map and 4,571.6 cM for the paternal map, with average marker densities of one marker per 2.9 cM and one per 2.0 cM respectively, were produced. A QTL analysis was performed on nine traits; however, only QTL regions linked to M/F, K/F and S/F were declared to be significant. Of those QTLs were detected: two for M/F, four for K/F and one for S/F. These QTLs explained 18.1-25.6% of the phenotypic variance and were located near putative genes, such as casein kinase II and the zinc finger CCCH domain, which are involved in seed germination and growth. The identified QTL regions for M/F, K/F and S/F from this study could be applied in an oil palm breeding program and used to screen palms with desired traits via marker assisted selection (MAS).

Uncovering the Genomic Regions Associated with Yield Maintenance in Rice Under Drought Stress Using an Integrated Meta-Analysis Approach.

The complex trait of yield is controlled by several quantitative trait loci (QTLs). Given the global water deficit issue, the development of rice varieties suitable for non-flooded cultivation holds significant importance in breeding programs. The powerful approach of Meta-QTL (MQTL) analysis can be used for the genetic dissection of complicated quantitative traits. In the current study, a comprehensive MQTL analysis was conducted to identify consistent QTL regions associated with drought tolerance and yield-related traits under water deficit conditions in rice. In total, 1087 QTLs from 134 rice populations, published between 2000 to 2021, were utilized in the analysis. Distinct MQTL analysis of the relevant traits resulted in the identification of 213 stable MQTLs. The confidence interval (CI) for the detected MQTLs was between 0.12 and 19.7cM. The average CI of the identified MQTLs (4.68cM) was 2.74 times narrower compared to the average CI of the initial QTLs. Interestingly, 63 MQTLs coincided with SNP peak positions detected by genome-wide association studies for yield and drought tolerance-associated traits under water deficit conditions in rice. Considering the genes located both in the QTL-overview peaks and the SNP peak positions, 19 novel candidate genes were introduced, which are associated with drought response index, plant height, panicle number, biomass, and grain yield. Moreover, an inclusive MQTL analysis was performed on all the traits to obtain "Breeding MQTLs". This analysis resulted in the identification of 96 MQTLs with a CI ranging from 0.01 to 9.0cM. The mean CI of the obtained MQTLs (2.33cM) was 4.66 times less than the mean CI of the original QTLs. Thirteen MQTLs fulfilling the criteria of having more than 10 initial QTLs, CI < 1cM, and an average phenotypic variance explained greater than 10%, were designated as "Breeding MQTLs". These findings hold promise for assisting breeders in enhancing rice yield under drought stress conditions.

Identification of two major QTLs for pod shell thickness in peanut (Arachis hypogaea L.) using BSA-seq analysis.

Pod shell thickness (PST) is an important agronomic trait of peanut because it affects the ability of shells to resist pest infestations and pathogen attacks, while also influencing the peanut shelling process. However, very few studies have explored the genetic basis of PST. An F2 segregating population derived from a cross between the thick-shelled cultivar Yueyou 18 (YY18) and the thin-shelled cultivar Weihua 8 (WH8) was used to identify the quantitative trait loci (QTLs) for PST. On the basis of a bulked segregant analysis sequencing (BSA-seq), four QTLs were preliminarily mapped to chromosomes 3, 8, 13, and 18. Using the genome resequencing data of YY18 and WH8, 22 kompetitive allele-specific PCR (KASP) markers were designed for the genotyping of the F2 population. Two major QTLs (qPSTA08 and qPSTA18) were identified and finely mapped, with qPSTA08 detected on chromosome 8 (0.69-Mb physical genomic region) and qPSTA18 detected on chromosome 18 (0.15-Mb physical genomic region). Moreover, qPSTA08 and qPSTA18 explained 31.1-32.3% and 16.7-16.8% of the phenotypic variation, respectively. Fifteen genes were detected in the two candidate regions, including three genes with nonsynonymous mutations in the exon region. Two molecular markers (Tif2_A08_31713024 and Tif2_A18_7198124) that were developed for the two major QTL regions effectively distinguished between thick-shelled and thin-shelled materials. Subsequently, the two markers were validated in four F2:3 lines selected. The QTLs identified and molecular markers developed in this study may lay the foundation for breeding cultivars with a shell thickness suitable for mechanized peanut shelling.

QTL mapping: insights into genomic regions governing component traits of yield under combined heat and drought stress in wheat.

Drought and heat frequently co-occur during crop growth leading to devastating yield loss. The knowledge of the genetic loci governing component traits of yield under combined drought and heat stress is essential for enhancing the climate resilience. The present study employed a mapping population of 180 recombinant inbred lines (RILs) derived from a cross between GW322 and KAUZ to identify quantitative trait loci (QTLs) governing the component traits of yield under heat and combined stress conditions. Phenotypic evaluation was conducted across two consecutive crop seasons (2021-2022 and 2022-2023) under late sown irrigation (LSIR) and late sown restricted irrigation (LSRI) conditions at the Indian Council of Agricultural Research Institute-Indian Agricultural Research Institute (ICAR-IARI), New Delhi. Various physiological and agronomic traits of importance were measured. Genotyping was carried out with 35K SNP Axiom breeder's genotyping array. The linkage map spanned a length of 6769.45cM, ranging from 2.28cM/marker in 1A to 14.21cM/marker in 5D. A total of 35 QTLs were identified across 14 chromosomes with 6B containing the highest (seven) number of QTLs. Out of 35 QTLs, 16 were major QTLs explaining the phenotypic variance greater than 10%. The study identified eight stable QTLs along with two hotspots on chromosomes 6B and 5B. Five QTLs associated with traits thousand-grain weight (TGW), normalized difference vegetation index (NDVI), and plant height (PH) were successfully validated. Candidate genes encoding antioxidant enzymes, transcription factors, and growth-related proteins were identified in the QTL regions. In silico expression analysis highlighted higher expression of transcripts TraesCS2D02G021000.1, TraesCS2D02G031000, TraesCS6A02G247900, and TraesCS6B02G421700 under stress conditions. These findings contribute to a deeper understanding of the genetic architecture underlying combined heat and drought tolerance in wheat, providing valuable insights for wheat improvement strategies under changing climatic conditions.