Composite Interval Mapping Research Articles

Quantitative trait loci mapping for survival of virus infection and virus levels in honey bees

Israeli acute paralysis virus (IAPV) is a highly virulent, Varroa-vectored virus that is of global concern for honey bee health. Little is known about the genetic basis of honey bees to withstand infection with IAPV or other viruses. We set up and analyzed a backcross between preselected honey bee colonies of low and high IAPV susceptibility to identify quantitative trait loci (QTL) associated with IAPV susceptibility. Experimentally inoculated adult worker bees were surveyed for survival and selectively sampled for QTL analysis based on SNPs identified by whole-genome resequencing and composite interval mapping. Additionally, natural titers of other viruses were quantified in the abdomen of these workers via qPCR and also used for QTL mapping. In addition to the full dataset, we analyzed distinct subpopulations of susceptible and non-susceptible workers separately. These subpopulations are distinguished by a single, suggestive QTL on chromosome 6, but we identified numerous other QTL for different abdominal virus titers, particularly in the subpopulation that was not susceptible to IAPV. The pronounced QTL differences between the susceptible and non-susceptible subpopulations indicate either an interaction between IAPV infection and the bees' interaction with other viruses or heterogeneity among workers of a single cohort that manifests itself as IAPV susceptibility and results in distinct subgroups that differ in their interaction with other viruses. Furthermore, our results indicate that low susceptibility of honey bees to viruses can be caused by both, virus tolerance and virus resistance. QTL were partially overlapping among different viruses, indicating a mixture of shared and specific processes that control viruses. Some functional candidate genes are located in the QTL intervals, but their genomic co-localization with numerous genes of unknown function delegates any definite characterization of the underlying molecular mechanisms to future studies.

Mapping of QTLs and meta-QTLs for Heterodera avenae Woll. resistance in common wheat (Triticum aestivum L.)

BackgroundIn hexaploid wheat, quantitative trait loci (QTL) and meta-QTL (MQTL) analyses were conducted to identify genomic regions controlling resistance to cereal cyst nematode (CCN), Heterodera avenae. A mapping population comprising 149 RILs derived from the cross HUW 468 × C 306 was used for composite interval mapping (CIM) and inclusive composite interval mapping (ICIM).ResultsEight main effect QTLs on three chromosomes (1B, 2A and 3A) were identified using two repeat experiments. One of these QTLs was co-localized with a previously reported wheat gene Cre5 for resistance to CCN. Seven important digenic epistatic interactions (PVE = 5% or more) were also identified, each involving one main effect QTL and another novel E-QTL. Using QTLs earlier reported in literature, two meta-QTLs were also identified, which were also used for identification of 57 candidate genes (CGs). Out of these, 29 CGs have high expression in roots and encoded the following proteins having a role in resistance to plant parasitic nematodes (PPNs): (i) NB-ARC,P-loop containing NTP hydrolase, (ii) Protein Kinase, (iii) serine-threonine/tyrosine-PK, (iv) protein with leucine-rich repeat, (v) virus X resistance protein-like, (vi) zinc finger protein, (vii) RING/FYVE/PHD-type, (viii) glycosyl transferase, family 8 (GT8), (ix) rubisco protein with small subunit domain, (x) protein with SANT/Myb domain and (xi) a protein with a homeobox.ConclusionIdentification and selection of resistance loci with additive and epistatic effect along with two MQTL and associated CGs, identified in the present study may prove useful for understanding the molecular basis of resistance against H. avenae in wheat and for marker-assisted selection (MAS) for breeding CCN resistant wheat cultivars.

QTL Verification and Candidate Gene Screening of Fiber Quality and Lint Percentage in the Secondary Segregating Population of Gossypium hirsutum.

Fiber quality traits, especially fiber strength, length, and micronaire (FS, FL, and FM), have been recognized as critical fiber attributes in the textile industry, while the lint percentage (LP) was an important indicator to evaluate the cotton lint yield. So far, the genetic mechanism behind the formation of these traits is still unclear. Quantitative trait loci (QTL) identification and candidate gene validation provide an effective methodology to uncover the genetic and molecular basis of FL, FS, FM, and LP. A previous study identified three important QTL/QTL cluster loci, harboring at least one of the above traits on chromosomes A01, A07, and D12 via a recombinant inbred line (RIL) population derived from a cross of Lumianyan28 (L28) × Xinluzao24 (X24). A secondary segregating population (F2) was developed from a cross between L28 and an RIL, RIL40 (L28 × RIL40). Based on the population, genetic linkage maps of the previous QTL cluster intervals on A01 (6.70-10.15 Mb), A07 (85.48-93.43 Mb), and D12 (0.40-1.43 Mb) were constructed, which span 12.25, 15.90, and 5.56 cM, with 2, 14, and 4 simple sequence repeat (SSR) and insertion/deletion (Indel) markers, respectively. QTLs of FL, FS, FM, and LP on these three intervals were verified by composite interval mapping (CIM) using WinQTL Cartographer 2.5 software via phenotyping of F2 and its derived F2:3 populations. The results validated the previous primary QTL identification of FL, FS, FM, and LP. Analysis of the RNA-seq data of the developing fibers of L28 and RIL40 at 10, 20, and 30 days post anthesis (DPA) identified seven differentially expressed genes (DEGs) as potential candidate genes. qRT-PCR verified that five of them were consistent with the RNA-seq result. These genes may be involved in regulating fiber development, leading to the formation of FL, FS, FM, and LP. This study provides an experimental foundation for further exploration of these functional genes to dissect the genetic mechanism of cotton fiber development.

Genetic Mapping for QTL Associated with Seed Nickel and Molybdenum Accumulation in the Soybean 'Forrest' by 'Williams 82' RIL Population.

Understanding the genetic basis of seed Ni and Mo is essential. Since soybean is a major crop in the world and a major source for nutrients, including Ni and Mo, the objective of the current research was to map genetic regions (quantitative trait loci, QTL) linked to Ni and Mo concentrations in soybean seed. A recombinant inbred line (RIL) population was derived from a cross between 'Forrest' and 'Williams 82' (F × W82). A total of 306 lines was used for genotyping using 5405 single nucleotides polymorphism (SNP) markers using Infinium SNP6K BeadChips. A two-year experiment was conducted and included the parents and the RIL population. One experiment was conducted in 2018 in North Carolina (NC), and the second experiment was conducted in Illinois in 2020 (IL). Logarithm of the odds (LOD) of ≥2.5 was set as a threshold to report identified QTL using the composite interval mapping (CIM) method. A wide range of Ni and Mo concentrations among RILs was observed. A total of four QTL (qNi-01, qNi-02, and qNi-03 on Chr 2, 8, and 9, respectively, in 2018, and qNi-01 on Chr 20 in 2020) was identified for seed Ni. All these QTL were significantly (LOD threshold > 2.5) associated with seed Ni, with LOD scores ranging between 2.71-3.44, and with phenotypic variance ranging from 4.48-6.97%. A total of three QTL for Mo (qMo-01, qMo-02, and qMo-03 on Chr 1, 3, 17, respectively) was identified in 2018, and four QTL (qMo-01, qMo-02, qMo-03, and qMo-04, on Chr 5, 11, 14, and 16, respectively) were identified in 2020. Some of the current QTL had high LOD and significantly contributed to the phenotypic variance for the trait. For example, in 2018, Mo QTL qMo-01 on Chr 1 had LOD of 7.8, explaining a phenotypic variance of 41.17%, and qMo-03 on Chr 17 had LOD of 5.33, with phenotypic variance explained of 41.49%. In addition, one Mo QTL (qMo-03 on Chr 14) had LOD of 9.77, explaining 51.57% of phenotypic variance related to the trait, and another Mo QTL (qMo-04 on Chr 16) had LOD of 7.62 and explained 49.95% of phenotypic variance. None of the QTL identified here were identified twice across locations/years. Based on a search of the available literature and of SoyBase, the four QTL for Ni, identified on Chr 2, 8, 9, and 20, and the five QTL associated with Mo, identified on Chr 1, 17, 11, 14, and 16, are novel and not previously reported. This research contributes new insights into the genetic mapping of Ni and Mo, and provides valuable QTL and molecular markers that can potentially assist in selecting Ni and Mo levels in soybean seeds.

Molecular identification and validation of four stable QTL for adult-plant resistance to powdery mildew in Chinese wheat cultivar Bainong 64.

Four stable QTL for adult-plant resistance (APR) to powdery mildew were identified on chromosome arms 1DL, 2BS, 2DL, and 6BL in the widely grown Chinese wheat cultivar Bainong 64. These QTL had no effect on response to stripe rust or leaf rust. Wheat powdery mildew, caused by Blumeria graminis f. sp. tritici (Bgt), is a devastating fungal disease. Seedlings of Chinese wheat Bainong 64 are susceptible to Bgt, but adult plants have maintained resistance since it was released in 1996. A population of 171 recombinant inbred lines (RILs) developed from cross Jingshuang 16/Bainong 64 (JS16/BN64) was used to dissect genetic components of powdery mildew resistance. A genetic map comprising 5383 polymorphic markers was constructed using the 15K SNP chip and kompetitive allele-specific PCR (KASP) markers. Composite interval mapping identified four stable QTL with favorable alleles all from BN64 on chromosome arms 1DL, 2BS, 2DL, and 6BL in at least four environments. They accounted for 8.3%, 13.8%, 14.4%, and 9.0% of the total phenotypic variation explained (PVE) in maximum, respectively. QPmjbr.caas-1DL, situated about 22Mb from centromere, is probably a new QTL. QPmjbr.caas-2DL located near the end of arm 2DL and explained the largest PVE. Using genetic maps populated with KASP markers, QPmjbr.caas-2BS and QPmjbr.caas-6BL were fine mapped to a 1.8cM genetic intervals spanning 13.6Mb (76.0-89.6Mb) and 1.7cM and 4.9Mb (659.9-664.8Mb), respectively. The four QTL independent of stripe rust and leaf rust resistance were validated for powdery mildew resistance in another RIL population related to BN64 and a cultivar panel using representative KASP markers. Since BN64 has been a leading cultivar and an important breeding parent in China, the QTL and markers reported in this study will be useful for marker-assisted selection of APR.

Identification of quantitative trait loci for drought tolerance in Bukoba/Kijivu Andean mapping population of common bean.

Quantitative Trait Loci "hotspots" for drought tolerance were identified on chromosomes Pv06, Pv07 and Pv10 of common bean. Drought is a major production constraint of common bean (Phaseolus vulgaris L.) worldwide. The objective of this study was to identify the Quantitative Trait Loci (QTL) for drought tolerance in an Andean population of Recombinant Inbred Lines (RILs). A total of 155 F5:7 RILs derived from a cross between Kijivu (drought tolerant) and Bukoba (drought susceptible) were evaluated for drought tolerance in field and pot experiments. Four field experiments were conducted at three locations in Zambia in 2020 and 2021. All field trials were conducted in the dry season under irrigation. The 155 RILs were genotyped with 11,292 SNPs, and composite interval mapping was conducted to identify QTL for drought tolerance. Seed yield for Kijivu under drought stress was consistently higher than for Bukoba across all four field trials. A total of 60 QTL were identified for morphological, agronomic, and physiological traits under drought stress and non-stress conditions. However, the majority of these QTL were specific to drought stress. QTL "hotspots" for drought tolerance were identified on chromosomes Pv06, Pv07, and Pv10. Extensive co-localizations for agronomic and morpho-physiological traits under drought stress were observed at the three drought-tolerance QTL hotspots. Additionally, these three QTL hotspots overlapped with previously identified QTL for drought tolerance, while several others identified QTL are novel. The three identified QTL hotspots could be used in future marker-assisted selection for drought tolerance in common bean.

Novel quantitative trait loci from an interspecific Brassica rapa derivative improve pod shatter resistance in Brassica napus.

Pod shatter is a trait of agricultural relevance that ensures plants dehisce seeds in their native environment and has been subjected to domestication and selection for non-shattering types in several broadacre crops. However, pod shattering causes a significant yield reduction in canola (Brassica napus L.) crops. An interspecific breeding line BC95042 derived from a B. rapa/B. napus cross showed improved pod shatter resistance (up to 12-fold than a shatter-prone B. napus variety). To uncover the genetic basis and improve pod shatter resistance in new varieties, we analysed F2 and F2:3 derived populations from the cross between BC95042 and an advanced breeding line, BC95041, and genotyped with 15,498 DArTseq markers. Through genome scan, interval and inclusive composite interval mapping analyses, we identified seven quantitative trait loci (QTLs) associated with pod rupture energy, a measure for pod shatter resistance or pod strength, and they locate on A02, A03, A05, A09 and C01 chromosomes. Both parental lines contributed alleles for pod shatter resistance. We identified five pairs of significant epistatic QTLs for additive x additive, additive dominance and dominance x dominance interactions between A01/C01, A03/A07, A07/C03, A03/C03, and C01/C02 chromosomes for rupture energy. QTL effects on A03/A07 and A01/C01 were in the repulsion phase. Comparative mapping identified several candidate genes (AG, ABI3, ARF3, BP1, CEL6, FIL, FUL, GA2OX2, IND, LATE, LEUNIG, MAGL15, RPL, QRT2, RGA, SPT and TCP10) underlying main QTL and epistatic QTL interactions for pod shatter resistance. Three QTLs detected on A02, A03, and A09 were near the FUL (FRUITFULL) homologues BnaA03g39820D and BnaA09g05500D. Focusing on the FUL, we investigated putative motifs, sequence variants and the evolutionary rate of its homologues in 373 resequenced B. napus accessions of interest. BnaA09g05500D is subjected to purifying selection as it had a low Ka/Ks ratio compared to other FUL homologues in B. napus. This study provides a valuable resource for genetic improvement for yield through an understanding of the genetic mechanism controlling pod shatter resistance in Brassica species.

Identification of QTL for kernel weight and size and analysis of the pentatricopeptide repeat (PPR) gene family in cultivated peanut (Arachis hypogaea L.)

Peanut (Arachis hypogaea L.) is an important oilseed crop worldwide. Improving its yield is crucial for sustainable peanut production to meet increasing food and industrial requirements. Deciphering the genetic control underlying peanut kernel weight and size, which are essential components of peanut yield, would facilitate high-yield breeding. A high-density single nucleotide polymorphism (SNP)-based linkage map was constructed using a recombinant inbred lines (RIL) population derived from a cross between the variety Yuanza9102 and a germplasm accession wt09-0023. Kernel weight and size quantitative trait loci (QTLs) were co-localized to a 0.16 Mb interval on Arahy07 using inclusive composite interval mapping (ICIM). Analysis of SNP, and Insertion or Deletion (INDEL) markers in the QTL interval revealed a gene encoding a pentatricopeptide repeat (PPR) superfamily protein as a candidate closely linked with kernel weight and size in cultivated peanut. Examination of the PPR gene family indicated a high degree of collinearity of PPR genes between A. hypogaea and its diploid progenitors, Arachis duranensis and Arachis ipaensis. The candidate PPR gene, Arahy.JX1V6X, displayed a constitutive expression pattern in developing seeds. These findings lay a foundation for further fine mapping of QTLs related to kernel weight and size, as well as validation of candidate genes in cultivated peanut.

Fine mapping and sequence analysis reveal a promising candidate gene encoding a novel NB-ARC domain derived from wild rice (Oryza officinalis) that confers bacterial blight resistance.

Bacterial blight disease of rice caused by Xanthomonas oryzae pv. oryzae (Xoo) is one of the most serious constraints in rice production. The most sustainable strategy to combat the disease is the deployment of host plant resistance. Earlier, we identified an introgression line, IR 75084-15-3-B-B, derived from Oryza officinalis possessing broad-spectrum resistance against Xoo. In order to understand the inheritance of resistance in the O. officinalis accession and identify genomic region(s) associated with resistance, a recombinant inbred line (RIL) mapping population was developed from the cross Samba Mahsuri (susceptible to bacterial blight) × IR 75084-15-3-B-B (resistant to bacterial blight). The F2 population derived from the cross segregated in a phenotypic ratio of 3: 1 (resistant susceptible) implying that resistance in IR 75084-15-3-B-B is controlled by a single dominant gene/quantitative trait locus (QTL). In the F7 generation, a set of 47 homozygous resistant lines and 47 homozygous susceptible lines was used to study the association between phenotypic data obtained through screening with Xoo and genotypic data obtained through analysis of 7K rice single-nucleotide polymorphism (SNP) chip. Through composite interval mapping, a major locus was detected in the midst of two flanking SNP markers, viz., Chr11.27817978 and Chr11.27994133, on chromosome 11L with a logarithm of the odds (LOD) score of 10.21 and 35.93% of phenotypic variation, and the locus has been named Xa48t. In silico search in the genomic region between the two markers flanking Xa48t identified 10 putatively expressed genes located in the region of interest. The quantitative expression and DNA sequence analysis of these genes from contrasting parents identified the Os11g0687900 encoding an NB-ARC domain-containing protein as the most promising gene associated with resistance. Interestingly, a 16-bp insertion was noticed in the untranslated region (UTR) of the gene in the resistant parent, IR 75084-15-3-B-B, which was absent in Samba Mahsuri. The association of Os11g0687900 with resistance phenotype was further established by sequence-based DNA marker analysis in the RIL population. A co-segregating PCR-based INDEL marker, Marker_Xa48, has been developed for use in the marker-assisted breeding of Xa48t.

Mapping of major QTL and candidate gene analysis for hull colour in foxtail millet (Setaria italica (L.) P. Beauv.)

BackgroundHull colour is an important morphological marker for selection in seed production of foxtail millet. However, the molecular mechanisms underlying hull colour variation remain unknown.ResultsAn F7 recombinant inbred line (RIL) population containing 215 lines derived from Hongjiugu × Yugu18 was used to analyze inheritance and detect the quantitative trait loci (QTL) for four hull colour traits using major gene plus polygene mixed inheritance analysis and composite interval mapping (CIM) in four environments. Genetic analysis revealed that the hull colour L* value (HCL*) was controlled by two major genes plus additive polygenes, the hull colour a* value (HCa*) was controlled by three major genes, the hull colour b* value (HCb*) was controlled by two major genes plus polygenes, and the hull colour C* value (HCC*) was controlled by four major genes. A high-density genetic linkage map covering 1227.383 cM of the foxtail millet genome, with an average interval of 0.879 cM between adjacent bin markers, was constructed using 1420 bin markers. Based on the genetic linkage map and the phenotypic data, a total of 39 QTL were detected for these four hull colour traits across four environments, each explaining 1.50%–49.20% of the phenotypic variation. Of these, six environmentally stable major QTL were co-localized to regions on chromosomes 1 and 9, playing a major role in hull colour. There were 556 annotated genes within the two QTL regions. Based on the functions of homologous genes in Arabidopsis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) gene annotations, five genes were predicted as candidate genes for further studies.ConclusionsThis is the first study to use an inheritance model and QTL mapping to determine the genetic mechanisms of hull colour trait in foxtail millet. We identified six major environmentally stable QTL and predicted five potential candidate genes to be associated with hull colour. These results advance the current understanding of the genetic mechanisms underlying hull colour traits in foxtail millet and provide additional resources for application in genomics-assisted breeding and potential isolation and functional characterization of the candidate genes.

Identification of genetic loci for powdery mildew resistance in bread wheat

AbstractPowdery mildew is a very destructive disease that threatens yield and quality of wheat. In the present study, 262 recombinant inbred lines derived from a cross between Zhongmai 578 (ZM578) with moderate resistance to powdery mildew and Jimai 22 (JM22) with high resistance to powdery mildew in the field were used to map powdery mildew resistance loci using the high‐density Illumina iSelect 50K single‐nucleotide polymorphism array. Three quantitative trait loci (QTLs) on chromosomes 2B, 5B, and 5D were identified by inclusive composite interval mapping, designated as QPm.caas‐2BL.1, QPm.caas‐5BL, and QPm.caas‐5DL, respectively, explaining 5.0%–37.0% of the phenotypic variances. The resistance allele of QPm.caas‐5BL was contributed by ZM578, whereas alleles of QPm.caas‐2BL.1 and QPm.caas‐5DL were from JM22. QPm.caas‐2BL.1, detected in all the six experimental environments, was a major and stable QTL. QPm.caas‐5DL was detected in five of the six environments and is likely to be a new QTL for powdery mildew resistance. The kompetitive allele‐specific PCR (KASP) markers Kpm‐2BL and Kpm‐5DL for QPm.caas‐2BL.1 and QPm.caas‐5DL, respectively, were validated in a diverse panel of 287 cultivars. These KASP markers, tightly linked with the QTLs, can be used for marker‐assisted breeding targeting the improvement of powdery mildew resistance in wheat.

Genomic Regions Associated with Resistance to Three Rusts in CIMMYT Wheat Line "Mokue#1".

Understanding the genetic basis of rust resistance in elite CIMMYT wheat germplasm enhances breeding and deployment of durable resistance globally. "Mokue#1", released in 2023 in Pakistan as TARNAB Gandum-1, has exhibited high levels of resistance to stripe rust, leaf rust, and stem rust pathotypes present at multiple environments in Mexico and Kenya at different times. To determine the genetic basis of resistance, a F5 recombinant inbred line (RIL) mapping population consisting of 261 lines was developed and phenotyped for multiple years at field sites in Mexico and Kenya under the conditions of artificially created rust epidemics. DArTSeq genotyping was performed, and a linkage map was constructed using 7892 informative polymorphic markers. Composite interval mapping identified three significant and consistent loci contributed by Mokue: QLrYr.cim-1BL and QLrYr.cim-2AS on chromosome 1BL and 2AS, respectively associated with stripe rust and leaf rust resistance, and QLrSr.cim-2DS on chromosome 2DS for leaf rust and stem rust resistance. The QTL on 1BL was confirmed to be the Lr46/Yr29 locus, whereas the QTL on 2AS represented the Yr17/Lr37 region on the 2NS/2AS translocation. The QTL on 2DS was a unique locus conferring leaf rust resistance in Mexico and stem rust resistance in Kenya. In addition to these pleiotropic loci, four minor QTLs were also identified on chromosomes 2DL and 6BS associated with stripe rust, and 3AL and 6AS for stem rust, respectively, using the Kenya disease severity data. Significant decreases in disease severities were also demonstrated due to additive effects of QTLs when present in combinations.

Slow stripe rusting in Chinese wheat Jimai 44 conferred by Yr29 in combination with a major QTL on chromosome arm 6AL.

YrJ44, a more effective slow rusting gene than Yr29, was localized to a 3.5-cM interval between AQP markers AX-109373479 and AX-109563479 on chromosome 6AL. "Slow rusting" (SR) is a type of adult plant resistance (APR) that can provide non-specific durable resistance to stripe rust in wheat. Chinese elite wheat cultivar Jimai 44 (JM44) has maintained SR to stripe rust in China since its release despite exposure to a changing and variable pathogen population. An F2:6 population comprising 295 recombinant inbred lines (RILs) derived from a cross between JM44 and susceptible cultivar Jimai 229 (JM229) was used in genetic analysis of the SR. The RILs and parental lines were evaluated for stripe rust response in five field environments and genotyped using the Affymetrix Wheat55K SNP array and 13 allele-specific quantitative PCR-based (AQP) markers. Two stable QTL on chromosome arms 1BL and 6AL were identified by inclusive composite interval mapping. The 1BL QTL was probably the pleiotropic gene Lr46/Yr29/Sr58. QYr.nwafu-6AL (hereafter named YrJ44), mapped in a 3.5-cM interval between AQP markers AX-109373479 and AX-109563479, was more effective than Yr29 in reducing disease severity and relative area under the disease progress curve (rAUDPC). RILs harboring both YrJ44 and Yr29 displayed levels of SR equal to the resistant parent JM44. The AQP markers linked with YrJ44 were polymorphic and significantly correlated with stripe rust resistance in a panel of 1,019 wheat cultivars and breeding lines. These results suggested that adequate SR resistance can be obtained by combining YrJ44 and Yr29 and the AQP markers can be used in breeding for durable stripe rust resistance.

Molecular Mapping to Discover Reliable Salinity-Resilient QTLs from the Novel Landrace Akundi in Two Bi-Parental Populations Using SNP-Based Genome-Wide Analysis in Rice

Achieving high-yield potential is always the ultimate objective of any breeding program. However, various abiotic stresses such as salinity, drought, cold, flood, and heat hampered rice productivity tremendously. Salinity is one of the most important abiotic stresses that adversely affect rice grain yield. The present investigation was undertaken to dissect new genetic loci, which are responsible for salt tolerance at the early seedling stage in rice. A bi-parental mapping population (F2:3) was developed from the cross between BRRI dhan28/Akundi, where BRRI dhan28 (BR28) is a salt-sensitive irrigated (boro) rice mega variety and Akundi is a highly salinity-tolerant Bangladeshi origin indica rice landrace that is utilized as a donor parent. We report reliable and stable QTLs for salt tolerance from a common donor (Akundi) irrespective of two different genetic backgrounds (BRRI dhan49/Akundi and BRRI dhan28/Akundi). A robust 1k-Rice Custom Amplicon (1k-RiCA) SNP marker genotyping platform was used for genome-wide analysis of this bi-parental population. After eliminating markers with high segregation distortion, 886 polymorphic SNPs built a genetic linkage map covering 1526.5 cM of whole rice genome with an average SNP density of 1.72 cM for the 12 genetic linkage groups. A total of 12 QTLs for nine different salt tolerance-related traits were identified using QGene and inclusive composite interval mapping of additive and dominant QTL (ICIM-ADD) under salt stress on seven different chromosomes. All of these 12 new QTLs were found to be unique, as no other map from the previous study has reported these QTLs in the similar chromosomal location and found them different from extensively studied Saltol, SKC1, OsSalT, and salT locus. Twenty-eight significant digenic/epistatic interactions were identified between chromosomal regions linked to or unlinked to QTLs. Akundi acts like a new alternate donor source of salt tolerance except for other usually known donors such as Nona Bokra, Pokkali, Capsule, and Hasawi used in salt tolerance genetic analysis and breeding programs worldwide, including Bangladesh. Integration of the seven novel, reliable, stable, and background independent salinity-resilient QTLs (qSES1, qSL1, qRL1, qSUR1, qSL8, qK8, qK1) reported in this investigation will expedite the cultivar development that is highly tolerant to salt stress.

Genetic analysis and physical mapping of oat adult plant resistance loci against Puccinia coronata f. sp. avenae.

Six quantitative trait loci (QTL) for adult plant resistance (APR) against oat crown rust (Puccinia coronata f. sp. avenae) were identified from mapping three recombinant inbred populations (RILs). Using genotyping-by-sequencing with markers called against the OT3098 v1 reference genome, the QTL were mapped on six different chromosomes, namely: Chr1D, Chr4D, Chr5A, Chr5D, Chr7A, and Chr7C. Composite interval mapping with marker cofactor selection showed that the phenotypic variance explained by all identified QTL for coefficient of infection range from 12.2 to 46.9%, while heritability estimates ranged from 0.11 to 0.38. The significant regions were narrowed down to intervals of 3.9 to 25 cM, equivalent to physical distances of 11 to 133 Mb. At least two flanking SNP markers were identified within 10 cM of each QTL that could be used in marker-assisted introgression, pyramiding, and selection. The additive effects of the QTL in each population were determined using SNP haplotype data, which showed significantly lower coefficient of infection (CI) in lines homozygous for the resistant alleles. Analysis of pairwise linkage disequilibrium also revealed high correlation of markers and presence of linkage blocks in the significant regions. To further facilitate marker-assisted breeding, Polymerase Chain Reaction Allelic Competitive Extension (PACE) markers for the APR loci were developed. Putative candidate genes were also identified in each of the significant regions, which include resistance gene analogs that encode for kinases, ligases, and predicted receptors of avirulence proteins from pathogens.

Consensus linkage map construction and QTL mapping for eight yield-related traits in wheat using the BAAFS Wheat 90K SNP array

Identifying stable quantitative trait loci (QTLs) for yield-related traits across populations and environments is crucial for wheat breeding and genetic studies. Consensus maps also play important roles in wheat genetic and genomic research. In the present study, a wheat consensus map was constructed using a doubled haploid (DH) population derived from Jinghua 1×Xiaobaidongmai (JX), an F2 population derived from L43×Shanxibaimai (LS) and the BAAFS Wheat 90K SNP array single nucleotide polymorphism (SNP) array. A total of 44,503 SNP markers were mapped on the constructed consensus map, and they covered 5,437.92 cM across 21 chromosomes. The consensus map showed high collinearity with the individual maps and the wheat reference genome IWGSC RefSeq v2.1. Phenotypic data on eight yield-related traits were collected in the JX population, as well as the F2:3 and F2:4 populations of LS, in six, two and two environments, respectively, and those data were used for QTL analysis. Inclusive composite interval mapping (ICIM) identified 32 environmentally stable QTLs for the eight yield-related traits. Among them, four QTLs (QPH.baafs-4B, QKNS.baafs-4B, QTGW.baafs-4B, and QSL.baafs-5A.3) were detected across mapping populations and environments, and nine stable QTLs (qKL.baafs-1D, QPH.baafs-2B, QKNS.baafs-3D, QSL.baafs-3D, QKW.baafs-4B, QPH.baafs-5D, QPH.baafs-6A.1, QSL.baafs-6A, and QSL.baafs-6D) are likely to be new. The physical region of 17.25–44.91 Mb on chromosome 4B was associated with six yield-related traits, so it is an important region for wheat yield. The physical region around the dwarfing gene Rht24 contained QTLs for kernel length (KL), kernel width (KW), spike length (SL), and thousand-grain weight (TGW), which are either from a pleiotropic effect of Rht24 or closely linked loci. For the stable QTLs, 254 promising candidate genes were identified. Among them, TraesCS5A03G1264300, TraesCS1B03G0624000 and TraesCS6A03G0697000 are particularly noteworthy since their homologous genes have similar functions for the corresponding traits. The constructed consensus map and the identified QTLs along with their candidate genes will facilitate the genetic dissection of wheat yield-related traits and accelerate the development of wheat cultivars with desirable plant morphology and high yield.

KASP mapping of QTLs for yield components using a RIL population in Basmati rice (Oryza sativa L.)

Super Basmati is an elite variety with extra-long grains and superior quality but its yield is severely affected by water stress. Recombinant inbred lines (RILs) developed from the cross of Super Basmati and IR55419-04 (a coarse grained variety with high yield potential) were used to identify and map yield-related quantitative trait loci (QTLs) under normal field conditions. Genotypes for 244 KASP markers were obtained from 188 F9 RILs and a linkage map constructed of 1369.4 cM, with average marker interval of 10.5 cM. Eleven agronomic traits were phenotyped in RILs and parents. Composite interval mapping and inclusive composite interval mapping model identified 21 common QTLs related to nine agronomic traits; of the QTLs identified, twelve mapped at novel positions. LOD scores ranged from 2.51 for number of filled grains (qFG1.1) to 16.8 for plant height (qPH1.1). Four major effect QTLs could explain 20% of variation related for plant height, hundred-grain weight and grain width. Alleles from IR55419-04 improved grain filling, grain weight and grain width, while alleles from Super Basmati contributed to longer grains, panicles and flag leaves. Putative candidate genes were identified for 18 yield-related QTLs. These results validate the use of KASP genotyping for QTL mapping of yield-related traits in a bi-parental segregating population. SNPs in the QTLs identified in this study can be used in marker assisted selection for yield components to improve Basmati rice.

Construction of a genetic linkage map and QTL mapping of fruit quality traits in guava (Psidium guajava L.).

Guava (Psidium guajava L.) is an important fruit crop of the Indian sub-continent, with potential for improvements in quality and yield. The goal of the present study was to construct a genetic linkage map in an intraspecific cross between the elite cultivar 'Allahabad Safeda' and the Purple Guava landrace to identify the genomic regions responsible for important fruit quality traits, viz., total soluble solids, titratable acidity, vitamin C, and sugars. This population was phenotyped in field trials (as a winter crop) for three consecutive years, and showed moderate-to-high values of heterogeneity coefficients along with higher heritability (60.0%-97.0%) and genetic-advance-over-mean values (13.23%-31.17%), suggesting minimal environmental influence on the expression of fruit-quality traits and indicating that these traits can be improved by phenotypic selection methods. Significant correlations and strong associations were also detected among fruit physico-chemical traits in segregating progeny. The constructed linkage map consisted of 195 markers distributed across 11 chromosomes, spanning a length of 1,604.47 cM (average inter-loci distance of 8.80 markers) and with 88.00% coverage of the guava genome. Fifty-eight quantitative trait loci (QTLs) were detected in three environments with best linear unbiased prediction (BLUP) values using the composite interval mapping algorithm of the BIP (biparental populations) module. The QTLs were distributed on seven different chromosomes, explaining 10.95%-17.77% of phenotypic variance, with the highest LOD score being 5.96 for qTSS.AS.pau-6.2. Thirteen QTLs detected across multiple environments with BLUPs indicate stability and utility in a future breeding program for guava. Furthermore, seven QTL clusters with stable or common individual QTLs affecting two or more different traits were located on six linkage groups (LGs), explaining the correlation among fruit-quality traits. Thus, the multiple environmental evaluations conducted here have increased our understanding of the molecular basis of phenotypic variation, providing the basis for future high-resolution fine-mapping and paving the way for marker-assisted breeding of fruit-quality traits.

Mapping of QTLs associated with yield and related traits under reproductive stage drought stress in rice using SNP linkage map.

Drought stress is a major constraint for rice production worldwide. Reproductive stage drought stress (RSDS) leads to heavy yield losses in rice. The prospecting of new donor cultivars for identification and introgression of QTLs of major effect (Quantitative trait locus) for drought tolerance is crucial for the development of drought-resilient rice varieties. Our study aimed to map QTLs associated with yield and its related traits under RSDS conditions. A saturated linkage map was constructed using 3417 GBS (Genotyping by sequencing) derived SNP (Single nucleotide polymorphism) markers spanning 1924.136cM map length with an average marker density of 0.56cM, in the F3 mapping population raised via cross made between the traditional ahu rice cultivar, Koniahu (drought tolerant) and a high-yielding variety, Disang (drought susceptible). Using the Inclusive composite interval mapping approach, 35 genomic regions governing yield and related traits were identified in pooled data from 198 F3 and F4 segregating lines evaluated for two consecutive seasons under both RSDS and irrigated control conditions. Of the 35 QTLs, 23 QTLs were identified under RSDS with LOD (Logarithm of odds) values ranging between 2.50 and 7.83 and PVE (phenotypic variance explained) values of 2.95-12.42%. Two major QTLs were found to be linked to plant height (qPH1.29) and number of filled grains per panicle (qNOG5.12) under RSDS. Five putative QTLs for grain yield namely, qGY2.00, qGY5.05, qGY6.16, qGY9.19, and qGY10.20 were identified within drought conditions. Fourteen QTL regions having ≤ 10Mb QTL interval size were further analysed for candidate gene identification and a total of 4146 genes were detected out of these 2263 (54.63%) genes were annotated to at least one gene ontology (GO) term. Several QTLs associated with grain yield and yield components and putative candidate genes were identified. The putative QTLs and candidate genes identified could be employed to augment drought resilience in rice after further validation through MAS strategies.

Identification of candidate genes for soybean seed coat-related traits using QTL mapping and GWAS.

Seed coat color is a typical morphological trait that can be used to reveal the evolution of soybean. The study of seed coat color-related traits in soybeans is of great significance for both evolutionary theory and breeding practices. In this study, 180 F10 recombinant inbred lines (RILs) derived from the cross between the yellow-seed coat cultivar Jidou12 (ZDD23040, JD12) and the wild black-seed coat accession Y9 (ZYD02739) were used as materials. Three methods, single-marker analysis (SMA), interval mapping (IM), and inclusive composite interval mapping (ICIM), were used to identify quantitative trait loci (QTLs) controlling seed coat color and seed hilum color. Simultaneously, two genome-wide association study (GWAS) models, the generalized linear model (GLM) and mixed linear model (MLM), were used to jointly identify seed coat color and seed hilum color QTLs in 250 natural populations. By integrating the results from QTL mapping and GWAS analysis, we identified two stable QTLs (qSCC02 and qSCC08) associated with seed coat color and one stable QTL (qSHC08) related to seed hilum color. By combining the results of linkage analysis and association analysis, two stable QTLs (qSCC02, qSCC08) for seed coat color and one stable QTL (qSHC08) for seed hilum color were identified. Upon further investigation using Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, we validated the previous findings that two candidate genes (CHS3C and CHS4A) reside within the qSCC08 region and identified a new QTL, qSCC02. There were a total of 28 candidate genes in the interval, among which Glyma.02G024600, Glyma.02G024700, and Glyma.02G024800 were mapped to the glutathione metabolic pathway, which is related to the transport or accumulation of anthocyanin. We considered the three genes as potential candidate genes for soybean seed coat-related traits. The QTLs and candidate genes detected in this study provide a foundation for further understanding the genetic mechanisms underlying soybean seed coat color and seed hilum color and are of significant value in marker-assisted breeding.