A Genome-Wide mQTL-seq Scan Identifies Potential Molecular Signatures Regulating Plant Height in Chickpea

The current study employed a high-throughput genome-wide next-generation sequencing-led multiple QTL-seq (mQTL-seq) strategy in two inter- and intra-specific recombinant inbred line (RIL) mapping populations to identify the major genomic regions underlying robust quantitative trait loci (QTLs) regulating plant height in chickpea. The whole genome resequencing discovered 446,475 and 150,434 high-quality homozygous single nucleotide polymorphisms (SNPs) exhibiting polymorphism between tall and dwarf/semi-dwarf mapping parents and bulk/homozygous individuals selected from each of two chickpea RIL populations. These SNP-led mQTL-seq assays in RIL mapping populations scaled-down two longer major genomic regions (1.26–1.34 Mb) underlying robust plant height QTLs into the shorter high-resolution QTL intervals (653.2–756.3 kb) on chickpea chromosomes 3 and 8. This essentially delineated regulatory novel natural SNP allelic variants from brassinosteroid insensitive 1-receptor kinase 1 (BAK1) and gibberellin (GA) 20-oxidase genes governing plant height in chickpea. A strong impact of evolutionary bottlenecks including strong artificial/natural selection on two plant height gene loci during chickpea domestication was observed. The shoot apical meristem-specific expression aside from down-regulation of two plant height genes especially in dwarf/semi-dwarf as compared to tall parents and homozygous mapping individuals of two aforementioned RIL populations was apparent. The integrated genomics-assisted breeding strategy combining mQTL-seq with differential gene expression profiling and functional allelic diversity-based trait domestication study collectively identified potential natural allelic variants of candidate genes underlying major plant height QTLs in chickpea. These functionally relevant molecular signatures can be of immense use for marker-aided genetic enhancement to develop high seed- and pod-yielding non-lodging cultivars restructured with desirable plant height in chickpea.     

Deploying QTL-seq for rapid delineation of a potential candidate gene underlying major trait-associated QTL in chickpea

A rapid high-resolution genome-wide strategy for molecular mapping of major QTL(s)/gene(s) regulating important agronomic traits is vital for in-depth dissection of complex quantitative traits and genetic enhancement in chickpea. The present study for the first time employed a NGS-based whole-genome QTL-seq strategy to identify one major genomic region harbouring a robust 100-seed weight QTL using an intra-specific 221 chickpea mapping population (desi cv. ICC 7184 × desi cv. ICC 15061). The QTL-seq-derived major SW QTL (CaqSW1.1) was further validated by single-nucleotide polymorphism (SNP) and simple sequence repeat (SSR) marker-based traditional QTL mapping (47.6% R² at higher LOD >19). This reflects the reliability and efficacy of QTL-seq as a strategy for rapid genome-wide scanning and fine mapping of major trait regulatory QTLs in chickpea. The use of QTL-seq and classical QTL mapping in combination narrowed down the 1.37 Mb (comprising 177 genes) major SW QTL (CaqSW1.1) region into a 35 kb genomic interval on desi chickpea chromosome 1 containing six genes. One coding SNP (G/A)-carrying constitutive photomorphogenic9 (COP9) signalosome complex subunit 8 (CSN8) gene of these exhibited seed-specific expression, including pronounced differential up-/down-regulation in low and high seed weight mapping parents and homozygous individuals during seed development. The coding SNP mined in this potential seed weight-governing candidate CSN8 gene was found to be present exclusively in all cultivated species/genotypes, but not in any wild species/genotypes of primary, secondary and tertiary gene pools. This indicates the effect of strong artificial and/or natural selection pressure on target SW locus during chickpea domestication. The proposed QTL-seq-driven integrated genome-wide strategy has potential to delineate major candidate gene(s) harbouring a robust trait regulatory QTL rapidly with optimal use of resources. This will further assist us to extrapolate the molecular mechanism underlying complex quantitative traits at a genome-wide scale leading to fast-paced marker-assisted genetic improvement in diverse crop plants, including chickpea.     

Identification and genetic mapping for <Emphasis Type="Italic">rht-DM</Emphasis>, a dominant dwarfing gene in mutant semi-dwarf maize using QTL-seq approach

Semi-dwarfism is an agronomically important trait in breeding for stable high yields and for resistance to damage by wind and rain (lodging resistance). Many QTLs and genes causing dwarf phenotype have been found in maize. However, because of the yield loss associated with these QTLs and genes, they have been difficult to use in breeding for dwarf stature in maize. Therefore, it is important to find the new dwarfing genes or materials without undesirable characters. The objectives of this study were: (1) to figure out the inheritance of semi-dwarfism in mutants; (2) mapping dwarfing gene or QTL. Maize inbred lines ‘18599’ and ‘DM173’, which is the dwarf mutant derived from the maize inbred line ‘173’ through ⁶⁰Co-γ ray irradiation. F₂ and BC₁F₁ population were used for genetic analysis. Whole genome resequencing-based technology (QTL-seq) were performed to map dwarfing gene and figured out the SNP markers in predicted region using dwarf bulk and tall bulk from F₂ population. Based on the polymorphic SNP markers from QTL-seq, we were fine-mapping the dwarfing gene using F₂ population. In F₂ population, 398 were dwarf plants and 135 were tall plants. Results of χ² tests indicated that the ratio of dwarf plants to tall plants was fitted to 3:1 ratio. Furthermore, the χ² tests of BC₁F₁ population showed that the ratio was fitted to 1:1 ratio. Based on QTL-seq, the dwarfing gene was located at the region from 111.07 to 124.56 Mb of chromosome 9, and we named it rht-DM. Using traditional QTL mapping with SNP markers, the rht-DM was narrowed down to 400 kb region between SNP-21 and SNP-24. The two SNPs were located at 0.43 and 0.11 cM. Segregation analysis of F₂ and BC₁F₁ indicated that the dwarfing gene was likely a dominant gene. This dwarfing gene was located in the region between 115.02 and 115.42 Mb on chromosome 9.     

Single nucleotide polymorphism and haplotype effects associated with somatic cell score in German Holstein cattle

Background

To better understand the genetic determination of udder health, we performed a genome-wide association study (GWAS) on a population of 2354 German Holstein bulls for which daughter yield deviations (DYD) for somatic cell score (SCS) were available. For this study, we used genetic information of 44 576 informative single nucleotide polymorphisms (SNPs) and 11 725 inferred haplotype blocks.

Results

When accounting for the sub-structure of the analyzed population, 16 SNPs and 10 haplotypes in six genomic regions were significant at the Bonferroni threshold of P ≤ 1.14 × 10^-6. The size of the identified regions ranged from 0.05 to 5.62 Mb. Genomic regions on chromosomes 5, 6, 18 and 19 coincided with known QTL affecting SCS, while additional genomic regions were found on chromosomes 13 and X. Of particular interest is the region on chromosome 6 between 85 and 88 Mb, where QTL for mastitis traits and significant SNPs for SCS in different Holstein populations coincide with our results. In all identified regions, except for the region on chromosome X, significant SNPs were present in significant haplotypes. The minor alleles of identified SNPs on chromosomes 18 and 19, and the major alleles of SNPs on chromosomes 6 and X were favorable for a lower SCS. Differences in somatic cell count (SCC) between alternative SNP alleles reached 14 000 cells/mL.

Conclusions

The results support the polygenic nature of the genetic determination of SCS, confirm the importance of previously reported QTL, and provide evidence for the segregation of additional QTL for SCS in Holstein cattle. The small size of the regions identified here will facilitate the search for causal genetic variations that affect gene functions.     

QTL-seq for rapid identification of candidate genes for flowering time in broccoli × cabbage

Key message

A major QTL controlling early flowering in broccoli × cabbage was identified by marker analysis and next-generation sequencing, corresponding to GRF6 gene conditioning flowering time in Arabidopsis.

Abstract

Flowering is an important agronomic trait for hybrid production in broccoli and cabbage, but the genetic mechanism underlying this process is unknown. In this study, segregation analysis with BC₁P1, BC₁P2, F₂, and F_2:3 populations derived from a cross between two inbred lines “195” (late-flowering) and “93219” (early flowering) suggested that flowering time is a quantitative trait. Next, employing a next-generation sequencing-based whole-genome QTL-seq strategy, we identified a major genomic region harboring a robust flowering time QTL using an F₂ mapping population, designated Ef2.1 on cabbage chromosome 2 for early flowering. Ef2.1 was further validated by indel (insertion or deletion) marker-based classical QTL mapping, explaining 51.5% (LOD = 37.67) and 54.0% (LOD = 40.5) of the phenotypic variation in F₂ and F_2:3 populations, respectively. Combined QTL-seq and classical QTL analysis narrowed down Ef1.1 to a 228-kb genomic region containing 29 genes. A cabbage gene, Bol024659, was identified in this region, which is a homolog of GRF6, a major gene regulating flowering in Arabidopsis, and was designated BolGRF6. qRT-PCR study of the expression level of BolGRF6 revealed significantly higher expression in the early flowering genotypes. Taken together, our results provide support for BolGRF6 as a possible candidate gene for early flowering in the broccoli line 93219. The identified candidate genomic regions and genes may be useful for molecular breeding to improve broccoli and cabbage flowering times.

     

Genome-Wide Association Study Identifies Major Loci for Carcass Weight on BTA14 in Hanwoo (Korean Cattle)

This genome-wide association study (GWAS) was conducted to identify major loci that are significantly associated with carcass weight, and their effects, in order to provide increased understanding of the genetic architecture of carcass weight in Hanwoo. This genome-wide association study identified one major chromosome region ranging from 23 Mb to 25 Mb on chromosome 14 as being associated with carcass weight in Hanwoo. Significant Bonferroni-corrected genome-wide associations (P<1.52×10⁻⁶) were detected for 6 Single Nucleotide Polymorphic (SNP) loci for carcass weight on chromosome 14. The most significant SNP was BTB-01280026 (P = 4.02×10⁻¹¹), located in the 25 Mb region on Bos taurus autosome 14 (BTA14). The other 5 significant SNPs were Hapmap27934-BTC-065223 (P = 4.04×10⁻¹¹) in 25.2 Mb, BTB-01143580 (P = 6.35×10⁻¹¹) in 24.3 Mb, Hapmap30932-BTC-011225 (P = 5.92×10⁻¹⁰) in 24.8 Mb, Hapmap27112-BTC-063342 (P = 5.18×10⁻⁹) in 25.4 Mb, and Hapmap24414-BTC-073009 (P = 7.38×10⁻⁸) in 25.4 Mb, all on BTA 14. One SNP (BTB-01143580; P = 6.35×10⁻¹¹) lies independently from the other 5 SNPs. The 5 SNPs that lie together showed a large Linkage disequilibrium (LD) block (block size of 553 kb) with LD coefficients ranging from 0.53 to 0.89 within the block. The most significant SNPs accounted for 6.73% to 10.55% of additive genetic variance, which is quite a large proportion of the total additive genetic variance. The most significant SNP (BTB-01280026; P = 4.02×10⁻¹¹) had 16.96 kg of allele substitution effect, and the second most significant SNP (Hapmap27934-BTC-065223; P = 4.04×10⁻¹¹) had 18.06 kg of effect on carcass weight, which correspond to 44% and 47%, respectively, of the phenotypic standard deviation for carcass weight in Hanwoo cattle. Our results demonstrated that carcass weight was affected by a major Quantitative Trait Locus (QTL) with a large effect and by many SNPs with small effects that are normally distributed.     

High-Throughput SNP Discovery and Genotyping for Constructing a Saturated Linkage Map of Chickpea (Cicer arietinum L.)

The present study reports the large-scale discovery of genome-wide single-nucleotide polymorphisms (SNPs) in chickpea, identified mainly through the next generation sequencing of two genotypes, i.e. Cicer arietinum ICC4958 and its wild progenitor C. reticulatum PI489777, parents of an inter-specific reference mapping population of chickpea. Development and validation of a high-throughput SNP genotyping assay based on Illumina''s GoldenGate Genotyping Technology and its application in building a high-resolution genetic linkage map of chickpea is described for the first time. In this study, 1022 SNPs were identified, of which 768 high-confidence SNPs were selected for designing the custom Oligo Pool All (CpOPA-I) for genotyping. Of these, 697 SNPs could be successfully used for genotyping, demonstrating a high success rate of 90.75%. Genotyping data of the 697 SNPs were compiled along with those of 368 co-dominant markers mapped in an earlier study, and a saturated genetic linkage map of chickpea was constructed. One thousand and sixty-three markers were mapped onto eight linkage groups spanning 1808.7 cM (centiMorgans) with an average inter-marker distance of 1.70 cM, thereby representing one of the most advanced maps of chickpea. The map was used for the synteny analysis of chickpea, which revealed a higher degree of synteny with the phylogenetically close Medicago than with soybean. The first set of validated SNPs and map resources developed in this study will not only facilitate QTL mapping, genome-wide association analysis and comparative mapping in legumes but also help anchor scaffolds arising out of the whole-genome sequencing of chickpea.     

Genetic Dissection of Drought and Heat Tolerance in Chickpea through Genome-Wide and Candidate Gene-Based Association Mapping Approaches

To understand the genetic basis of tolerance to drought and heat stresses in chickpea, a comprehensive association mapping approach has been undertaken. Phenotypic data were generated on the reference set (300 accessions, including 211 mini-core collection accessions) for drought tolerance related root traits, heat tolerance, yield and yield component traits from 1–7 seasons and 1–3 locations in India (Patancheru, Kanpur, Bangalore) and three locations in Africa (Nairobi, Egerton in Kenya and Debre Zeit in Ethiopia). Diversity Array Technology (DArT) markers equally distributed across chickpea genome were used to determine population structure and three sub-populations were identified using admixture model in STRUCTURE. The pairwise linkage disequilibrium (LD) estimated using the squared-allele frequency correlations (r²; when r²<0.20) was found to decay rapidly with the genetic distance of 5 cM. For establishing marker-trait associations (MTAs), both genome-wide and candidate gene-sequencing based association mapping approaches were conducted using 1,872 markers (1,072 DArTs, 651 single nucleotide polymorphisms [SNPs], 113 gene-based SNPs and 36 simple sequence repeats [SSRs]) and phenotyping data mentioned above employing mixed linear model (MLM) analysis with optimum compression with P3D method and kinship matrix. As a result, 312 significant MTAs were identified and a maximum number of MTAs (70) was identified for 100-seed weight. A total of 18 SNPs from 5 genes (ERECTA, 11 SNPs; ASR, 4 SNPs; DREB, 1 SNP; CAP2 promoter, 1 SNP and AMDH, 1SNP) were significantly associated with different traits. This study provides significant MTAs for drought and heat tolerance in chickpea that can be used, after validation, in molecular breeding for developing superior varieties with enhanced drought and heat tolerance.     

Genome-wide SNP identification for the construction of a high-resolution genetic map of Japanese flounder (Paralichthys olivaceus): applications to QTL mapping of Vibrio anguillarum disease resistance and comparative genomic analysis

High-resolution genetic maps are essential for fine mapping of complex traits, genome assembly, and comparative genomic analysis. Single-nucleotide polymorphisms (SNPs) are the primary molecular markers used for genetic map construction. In this study, we identified 13,362 SNPs evenly distributed across the Japanese flounder (Paralichthys olivaceus) genome. Of these SNPs, 12,712 high-confidence SNPs were subjected to high-throughput genotyping and assigned to 24 consensus linkage groups (LGs). The total length of the genetic linkage map was 3,497.29 cM with an average distance of 0.47 cM between loci, thereby representing the densest genetic map currently reported for Japanese flounder. Nine positive quantitative trait loci (QTLs) forming two main clusters for Vibrio anguillarum disease resistance were detected. All QTLs could explain 5.1–8.38% of the total phenotypic variation. Synteny analysis of the QTL regions on the genome assembly revealed 12 immune-related genes, among them 4 genes strongly associated with V. anguillarum disease resistance. In addition, 246 genome assembly scaffolds with an average size of 21.79 Mb were anchored onto the LGs; these scaffolds, comprising 522.99 Mb, represented 95.78% of assembled genomic sequences. The mapped assembly scaffolds in Japanese flounder were used for genome synteny analyses against zebrafish (Danio rerio) and medaka (Oryzias latipes). Flounder and medaka were found to possess almost one-to-one synteny, whereas flounder and zebrafish exhibited a multi-syntenic correspondence. The newly developed high-resolution genetic map, which will facilitate QTL mapping, scaffold assembly, and genome synteny analysis of Japanese flounder, marks a milestone in the ongoing genome project for this species.     

Highly efficient genotyping of rice biparental populations by GoldenGate assays based on parental resequencing

Key message

A new time- and cost-effective strategy was developed for medium-density SNP genotyping of rice biparental populations, using GoldenGate assays based on parental resequencing.

Abstract

Since the advent of molecular markers, crop researchers and breeders have dedicated huge amounts of effort to detecting quantitative trait loci (QTL) in biparental populations for genetic analysis and marker-assisted selection (MAS). In this study, we developed a new time- and cost-effective strategy for genotyping a population of progeny from a rice cross using medium-density single nucleotide polymorphisms (SNPs). Using this strategy, 728,362 “high quality” SNPs were identified by resequencing Teqing and Lemont, the parents of the population. We selected 384 informative SNPs that were evenly distributed across the genome for genotyping the biparental population using the Illumina GoldenGate assay. 335 (87.2 %) validated SNPs were used for further genetic analyses. After removing segregation distortion markers, 321 SNPs were used for linkage map construction and QTL mapping. This strategy generated SNP markers distributed more evenly across the genome than previous SSR assays. Taking the GW5 gene that controls grain shape as an example, our strategy provided higher accuracy (0.8 Mb) and significance (LOD 5.5 and 10.1) in QTL mapping than SSR analysis. Our study thus provides a rapid and efficient strategy for genetic studies and QTL mapping using SNP genotyping assays.     

Development and evaluation of high‐density Axiom®CicerSNP Array for high‐resolution genetic mapping and breeding applications in chickpea

To accelerate genomics research and molecular breeding applications in chickpea, a high‐throughput SNP genotyping platform ‘Axiom^®CicerSNP Array’ has been designed, developed and validated. Screening of whole‐genome resequencing data from 429 chickpea lines identified 4.9 million SNPs, from which a subset of 70 463 high‐quality nonredundant SNPs was selected using different stringent filter criteria. This was further narrowed down to 61 174 SNPs based on p‐convert score ≥0.3, of which 50 590 SNPs could be tiled on array. Among these tiled SNPs, a total of 11 245 SNPs (22.23%) were from the coding regions of 3673 different genes. The developed Axiom^®CicerSNP Array was used for genotyping two recombinant inbred line populations, namely ICCRIL03 (ICC 4958 × ICC 1882) and ICCRIL04 (ICC 283 × ICC 8261). Genotyping data reflected high success and polymorphic rate, with 15 140 (29.93%; ICCRIL03) and 20 018 (39.57%; ICCRIL04) polymorphic SNPs. High‐density genetic maps comprising 13 679 SNPs spanning 1033.67 cM and 7769 SNPs spanning 1076.35 cM were developed for ICCRIL03 and ICCRIL04 populations, respectively. QTL analysis using multilocation, multiseason phenotyping data on these RILs identified 70 (ICCRIL03) and 120 (ICCRIL04) main‐effect QTLs on genetic map. Higher precision and potential of this array is expected to advance chickpea genetics and breeding applications.     

The Identification of Two Head Smut Resistance-Related QTL in Maize by the Joint Approach of Linkage Mapping and Association Analysis

Head smut, caused by the fungus Sphacelotheca reiliana (Kühn) Clint, is a devastating threat to maize production. In this study, QTL mapping of head smut resistance was performed using a recombinant inbred line (RIL) population from a cross between a resistant line “QI319” and a susceptible line “Huangzaosi” (HZS) with a genetic map constructed from genotyping-by-sequencing (GBS) data and composed of 1638 bin markers. Two head smut resistance QTL were identified, located on Chromosome 2 (q2.09HR) and Chromosome 5 (q5.03HR), q2.09HR is co-localized with a previously reported QTL for head smut resistance, and the effect of q5.03HR has been validated in backcross populations. It was also observed that pyramiding the resistant alleles of both QTL enhanced the level of resistance to head smut. A genome-wide association study (GWAS) using 277 diverse inbred lines was processed to validate the mapped QTL and to identify additional head smut resistance associations. A total of 58 associated SNPs were detected, which were distributed in 31 independent regions. SNPs with significant association to head smut resistance were detected within the q2.09HR and q5.03HR regions, confirming the linkage mapping results. It was also observed that both additive and epistastic effects determine the genetic architecture of head smut resistance in maize. As shown in this study, the combined strategy of linkage mapping and association analysis is a powerful approach in QTL dissection for disease resistance in maize.     

Integrated physical,genetic and genome map of chickpea (Cicer arietinum L.)

Physical map of chickpea was developed for the reference chickpea genotype (ICC 4958) using bacterial artificial chromosome (BAC) libraries targeting 71,094 clones (~12× coverage). High information content fingerprinting (HICF) of these clones gave high-quality fingerprinting data for 67,483 clones, and 1,174 contigs comprising 46,112 clones and 3,256 singletons were defined. In brief, 574 Mb genome size was assembled in 1,174 contigs with an average of 0.49 Mb per contig and 3,256 singletons represent 407 Mb genome. The physical map was linked with two genetic maps with the help of 245 BAC-end sequence (BES)-derived simple sequence repeat (SSR) markers. This allowed locating some of the BACs in the vicinity of some important quantitative trait loci (QTLs) for drought tolerance and reistance to Fusarium wilt and Ascochyta blight. In addition, fingerprinted contig (FPC) assembly was also integrated with the draft genome sequence of chickpea. As a result, ~965 BACs including 163 minimum tilling path (MTP) clones could be mapped on eight pseudo-molecules of chickpea forming 491 hypothetical contigs representing 54,013,992 bp (~54 Mb) of the draft genome. Comprehensive analysis of markers in abiotic and biotic stress tolerance QTL regions led to identification of 654, 306 and 23 genes in drought tolerance “QTL-hotspot” region, Ascochyta blight resistance QTL region and Fusarium wilt resistance QTL region, respectively. Integrated physical, genetic and genome map should provide a foundation for cloning and isolation of QTLs/genes for molecular dissection of traits as well as markers for molecular breeding for chickpea improvement.     

Overlapping mouse subcongenic strains successfully separate two linked body fat QTL on distal MMU 2

Background

Mouse chromosome 2 is linked to growth and body fat phenotypes in many mouse crosses. With the goal to identify the underlying genes regulating growth and body fat on mouse chromosome 2, we developed five overlapping subcongenic strains that contained CAST/EiJ donor regions in a C57BL/6J^hg/hg background (hg is a spontaneous deletion of 500 Kb on mouse chromosome 10). To fine map QTL on distal mouse chromosome 2 a total of 1,712 F2 mice from the five subcongenic strains, plus 278 F2 mice from the HG2D founder congenic strain were phenotyped and analyzed. Interval mapping (IM) and composite IM (CIM) were performed on body weight and body fat traits on a combination of SNP and microsatellite markers, which generated a high-density genotyping panel.

Results

Phenotypic analysis and interval mapping of total fat mass identified two QTL on distal mouse chromosome 2. One QTL between 150 and 161 Mb, Fatq2a, and the second between 173.3 and 175.6 Mb, Fatq2b. The two QTL reside in different congenic strains with significant total fat differences between homozygous cast/cast and b6/b6 littermates. Both of these QTL were previously identified only as a single QTL affecting body fat, Fatq2. Furthermore, through a novel approach referred here as replicated CIM, Fatq2b was mapped to the Gnas imprinted locus.

Conclusions

The integration of subcongenic strains, high-density genotyping, and CIM succesfully partitioned two previously linked QTL 20 Mb apart, and the strongest QTL, Fatq2b, was fine mapped to a ~2.3 Mb region interval encompassing the Gnas imprinted locus.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-014-1191-8) contains supplementary material, which is available to authorized users.     

Mapping and Introgression of QTL Involved in Fruit Shape Transgressive Segregation into ‘Piel de Sapo’ Melon (Cucucumis melo L.)

A mapping F₂ population from the cross ‘Piel de Sapo’ × PI124112 was selectively genotyped to study the genetic control of morphological fruit traits by QTL (Quantitative Trait Loci) analysis. Ten QTL were identified, five for FL (Fruit Length), two for FD (Fruit Diameter) and three for FS (Fruit Shape). At least one robust QTL per character was found, flqs8.1 (LOD = 16.85, R² = 34%), fdqs12.1 (LOD = 3.47, R² = 11%) and fsqs8.1 (LOD = 14.85, R² = 41%). flqs2.1 and fsqs2.1 cosegregate with gene a (andromonoecious), responsible for flower sex determination and with pleiotropic effects on FS. They display a positive additive effect (a) value, so the PI124112 allele causes an increase in FL and FS, producing more elongated fruits. Conversely, the negative a value for flqs8.1 and fsqs8.1 indicates a decrease in FL and FS, what results in rounder fruits, even if PI124112 produces very elongated melons. This is explained by a significant epistatic interaction between fsqs2.1 and fsqs8.1, where the effects of the alleles at locus a are attenuated by the additive PI124112 allele at fsqs8.1. Roundest fruits are produced by homozygous for PI124112 at fsqs8.1 that do not carry any dominant A allele at locus a (PiPiaa). A significant interaction between fsqs8.1 and fsqs12.1 was also detected, with the alleles at fsqs12.1 producing more elongated fruits. fsqs8.1 seems to be allelic to QTL discovered in other populations where the exotic alleles produce elongated fruits. This model has been validated in assays with backcross lines along 3 years and ultimately obtaining a fsqs8.1-NIL (Near Isogenic Line) in ‘Piel de Sapo’ background which yields round melons.     

Genome Assembly Anchored QTL Map of Bovine Chromosome 14

Bovine chromosome 14 (BTA14) has been widely explored for quantitative trait loci (QTL) and genes related to economically important traits in both dairy and beef cattle. We reviewed more than 40 investigations and anchored 126 QTL to the current genome assembly (Btau 4_0). Using this anchored QTL map, we observed that, in dairy cattle, the region spanning 0 – 10 Mb on BTA14 has the highest density QTL map with a total of 56 QTL, mainly for milk production traits. It is very likely that both somatic cell score (SCS) and clinical mastitis share some common QTL in two regions: 61.48 Mb - 73.84 Mb and 7.86 Mb – 39.55 Mb, respectively. As well, both ovulation rate and twinning rate might share a common QTL region from 34.16 Mb to 65.38 Mb. However, there are no common QTL locations in three pregnancy related phenotypes: non-return rate, pregnancy rate and daughter pregnancy rate. In beef cattle, the majority of QTL are located in a broad region of 15 Mb – 45 Mb on the chromosome. Functional genes, such as CRH, CYP11B1, DGAT1, FABP4 and TG, as potential candidates for some of these QTL, were also reviewed. Therefore, our review provides a standardized QTL map anchored within the current genome assembly, which would enhance the process of selecting positional and physiological candidate genes for many important traits in cattle.