The genetic basis of structural colour variation in mimetic Heliconius butterflies

Blood pressure (BP) in any human population exhibits as a continuous variable that fits a bell-shaped curve. Hypertensive individuals are those whose BP is maintained at one extreme of the curve and above a defined cutoff. Despite progress made in identifying the mechanisms underlying certain rare monogenic forms of hypertension,1,2 the etiology and pathogenesis of essential hypertension remain poorly understood. Because existing human populations are genetically heterogeneous, and because environmental factors impacting on the pathogenesis of hypertension cannot be controlled in a given population, it is difficult to identify the molecular mechanisms that transduce the sequela of essential hypertension via direct human studies.3 To alleviate the drawbacks of human investigations, animal models, especially inbred rodents, have been developed and experimentally manipulated to identify quantitative trait loci (QTLs) for BP, because major confounding environmental factors, such as diet and genetic background, can be systematically controlled. Once identified in animal models, the molecular basis may be translated into physiological understandings of essential hypertension in humans. It is with this expectation that efforts have been launched to identify the molecular basis of BP QTLs in animal models. Because the identification of individual QTLs is primarily based on their chromosome locations unbiased by, or unrestricted to, their physiological roles, positional cloning is believed to be the most efficient strategy. Before we embark on discussions regarding QTL discovery, a definition is in order. Semantic arguments abound as to exactly what a QTL, that is, a locus,4 entails. Is it 1 gene or a collection of genes? As genetic mapping progresses from a large chromosome segment to an interval of submegabase, several regions initially thought to contain 1 BP QTL5 appear to harbor >1 in each of them,6–10 whereas several other regions turned out to harbor 1 QTL as expected.11,12 …

Grain dimensions (length, breadth and length/breadth ratio) are important quality attributes of Basmati rice for its high consumer acceptance. Earlier we identified two significant quantitative trait loci (QTL) intervals on chromosomes 1 and 7 for grain dimensions in Basmati rice using a population of recombinant inbred lines (RILs) from cross between Basmati variety Pusa 1121 and a short grain non-aromatic variety Pusa 1342. For fine mapping of these QTLs, 184 F6 RILs were grown and phenotyped in the normal rice growing season at two different locations. Forty-nine new SSR markers targeting these QTL intervals were tested and nine were found polymorphic between the parents. Using revised genetic maps adding new markers, the grain length QTL qGRL1.1 on chromosome 1 was narrowed down to 108 kbp from the earlier reported 6,133 kbp. There were total 13 predicted gene models in this interval which includes the probable candidate gene for the exceptionally high grain length of Basmati variety Pusa 1121. Similarly, two tandem QTL intervals qGRL7.1 and qGRL7.2 on chromosome 7 were merged into a single one narrowed down to 2,390 kbp from the earlier reported length of 5,269 kbp. This region of chromosome 7 also has co-localized QTLs for grain breadth and length to breadth ratio. SSR markers tightly linked to the QTL at a map distance of ≤0.2 cM were developed for the qGRL1.1 and qGRL7.1 loci that could be used for marker-assisted breeding. Validation of earlier published markers for the grain length gene GS3 on chromosome 3 showed no difference between Pusa 1121 and Pusa 1342, highlighting the significance of qGRL1.1 and qGRL7.1 for the extra grain length of Basmati variety Pusa 1121.

Genetic dissection of grain weight in bread wheat was undertaken through both genome-wide quantitative trait locus (QTL) interval mapping and association mapping. QTL interval mapping involved preparation of a framework linkage map consisting of 294 loci {194 simple sequence repeats (SSRs), 86 amplified fragment length polymorphisms (AFLPs) and 14 selective amplifications of microsatellite polymorphic loci (SAMPL)} using a bi-parental recombinant inbred line (RIL) mapping population derived from Rye Selection111 × Chinese Spring. Using the genotypic data and phenotypic data on grain weight (GW) of RILs collected over six environments, genome-wide single locus QTL analysis was conducted to identify main effect QTL. This led to identification of as many as ten QTL including four major QTL (three QTL were stable), each contributing >20% phenotypic variation (PV) for GW. The above study was supplemented with association mapping, which allowed identification of 11 new markers in the genomic regions that were not reported earlier to harbour any QTL for GW. It also allowed identification of closely linked markers for six known QTL, and validation of eight QTL reported earlier. The QTL identified through QTL interval mapping and association mapping may prove useful in marker-assisted selection (MAS) for the development of cultivars with high GW in bread wheat.

Quantitative trait locus (QTL) mapping in the mouse typically utilizes inbred strains that exhibit significant genetic and phenotypic diversity. The development of dense SNP panels in a large number of inbred strains has eliminated the need to maximize genetic diversity in QTL studies as plenty of SNP markers are now available for almost any combination of strains. We conducted a QTL mapping experiment using both a backcross (N(2)) and an intercross (F(2)) between two genetically similar inbred mouse strains: C57BL/6J (B6) and C57L/J (C57). A set of additive QTLs for activity behaviors was identified on Chrs 1, 9, 13, and 15. We also identified additive QTLs for anxiety-related behaviors on Chrs 7, 9, and 16. A QTL on Chr 11 is sex-specific, and we revealed pairwise interactions between QTLs on Chrs 1 and 13 and Chrs 10 and 18. The Chr 9 activity QTL accounts for the largest amount of phenotypic variance and was not present in our recent analysis of a B6 x C58/J (C58) intercross (Bailey et al. in Genes Brain Behav 7:761-769, 2008). To narrow this QTL interval, we used a dense SNP haplotype map with over 7 million real and imputed SNP markers across 74 inbred mouse strains (Szatkiewicz et al. in Mamm Genome 19(3):199-208, 2008). Evaluation of shared and divergent haplotype blocks among B6, C57, and C58 strains narrowed the Chr 9 QTL interval considerably and highlights the utility of QTL mapping in closely related inbred strains.

Salinity stress is the most prominent stress impacting rice productivity worldwide. In the past, several quantitative trait loci (QTLs) for salinity tolerance had been identified in rice, however their utilization in rice breeding programs is largely confounded due to the unwanted linkage drag associated with the QTL region. Thus, it is strongly desirable to delimit the QTL region to a least possible chromosomal interval minimising any unwanted association. Addressing this, we have evaluated 68 recombinant inbred lines (RILs) derived from a cross between a salinity tolerant parent ‘Kolajoha’ and a salinity sensitive parent ‘Ranjit’ for identification of QTL(s) involved in imparting salinity tolerance at seedling stage. Genotyping by sequencing approach (GBS) was followed for SNP identification at genome wide scale. Around 3649 SNPs were identified by GBS method initially at 20% minor allele frequency. After filtering of SNPs with polymorphism with less than 10–15% of missing data, a total of 1248 SNPs were mapped to 1247 recombination points and the genetic map was constructed with a total map length of 1201.21 cM and resolution of 0.95 cM between markers. For 10 traits, a total of 23 additive QTLs were identified of which only 1 was a major QTL and 22 were minor QTLs. The average QTL interval size is about 2945 kb. Epistatic QTL mapping had identified one pair of QTLs that contribute significantly in the phenotypic variation of traits among the RILs. Total 1895 genes were identified in the QTL intervals, majority of them are located in Chr1 of rice genome between 22.09 and 38.29 Mb region. Although, this region is not very narrow, some of the genes falling in this region can be utilized for validation of QTLs in future. One differentially methylated region was found to be colocalized within the QTL intervals determined in Chr2 which indicates their potential role in epigenetic modifications in improving stress tolerance in rice.

BackgroundSalinity is one of the many abiotic stresses limiting rice production worldwide. Several studies were conducted to identify quantitative trait loci (QTLs) for traits associated to salinity tolerance. However, due to large confidence interval for the position of QTLs, utility of reported QTLs and the associated markers has been limited in rice breeding programs. The main objective of this study is to construct a high-density rice genetic map for identification QTLs and candidate genes for salinity tolerance at seedling stage.ResultsWe evaluated a population of 187 recombinant inbred lines (RILs) developed from a cross between Bengal and Pokkali for nine traits related to salinity tolerance. A total of 9303 SNP markers generated by genotyping-by-sequencing (GBS) were mapped to 2817 recombination points. The genetic map had a total map length of 1650 cM with an average resolution of 0.59 cM between markers. For nine traits, a total of 85 additive QTLs were identified, of which, 16 were large-effect QTLs and the rest were small-effect QTLs. The average interval size of QTL was about 132 kilo base pairs (Kb). Eleven of the 85 additive QTLs validated 14 reported QTLs for shoot potassium concentration, sodium-potassium ratio, salt injury score, plant height, and shoot dry weight. Epistatic QTL mapping identified several pairs of QTLs that significantly contributed to the variation of traits. The QTL for high shoot K+ concentration was mapped near the qSKC1 region. However, candidate genes within the QTL interval were a CC-NBS-LRR protein, three uncharacterized genes, and transposable elements. Additionally, many QTLs flanked small chromosomal intervals containing few candidate genes. Annotation of the genes located within QTL intervals indicated that ion transporters, osmotic regulators, transcription factors, and protein kinases may play essential role in various salt tolerance mechanisms.ConclusionThe saturation of SNP markers in our linkage map increased the resolution of QTL mapping. Our study offers new insights on salinity tolerance and presents useful candidate genes that will help in marker-assisted gene pyramiding to develop salt tolerant rice varieties.Electronic supplementary materialThe online version of this article (doi:10.1186/s12284-016-0125-2) contains supplementary material, which is available to authorized users.

St. Augustinegrass is a warm-season grass species widely utilized as turf in the southeastern U.S. It shows significant variation in plant growth and morphological characteristics, some of which are potentially associated with drought tolerance. However, the genetic basis of these variations is not well understood. Detecting quantitative trait loci (QTL) associated with morphological traits will provide a foundation for the application of genetic and molecular breeding in St. Augustinegrass. In this study, we report QTL associated with morphological traits, including leaf blade width (LW), leaf blade length (LL), canopy density (CD), and shoot growth orientation (SGO) in a St. Augustinegrass ‘Raleigh’ x ‘Seville’ mapping population containing 115 F1 hybrids. Phenotypic data were collected from one greenhouse and two field trials. Single and joint trial analyses were performed, finding significant phenotypic variance among the hybrids for all traits. Interval mapping (IM) and multiple QTL method (MQM) analysis detected seven QTL for CD, four for LL, five for LW, and two for SGO, which were distributed on linkage groups RLG1, RLG9, SLG3, SLG7, SLG8 and SLG9. In addition, three genomic regions where QTL colocalized were identified on Raleigh LG1 and Seville LG3. One genomic region on Seville LG3 overlapped with two previously reported drought-related QTL for leaf relative water content (RWC) and percent green cover (GC). Several candidate genes related to plant development and drought stress response were identified within QTL intervals. The QTL identified in this study represent a first step in identifying genes controlling morphological traits that might accelerate progress in selection of St. Augustinegrass lines with lower water usage.

Although genetic mapping of quantitative trait loci for blood pressure to large chromosome segments is readily achievable, their final identification confronts formidable hurdles. Restriction of the genes lodging in one quantitative trait locus interval to experimental limitation can facilitate their positional cloning. We previously delineated several quantitative trait loci for blood pressure on chromosome 10 of Dahl salt-sensitive rats, but their chromosome delimitations were either large or not definitive. In this study, we systematically and comprehensively constructed congenic strains with submegabase (Mb) genome resolution and analyzed their blood pressure by telemetry. Three quantitative trait loci have been conclusively delimited by three congenic strains, each independently lowering the blood pressure. Their intervals are demarcated by genomic regions between 350 and 910 kilobases (kb) in size. Two of the three quantitative trait loci share an epistatic relationship and are separated from one another by less than 170 kb. Two additional quantitative trait loci for blood pressure were also tentatively delineated and their intervals range from 520 kb to 1.75 Mb. Possible genes dwelling in each quantitative trait locus-interval number between 11 and 17. None of these genes is known to exert a functional impact on blood pressure. Work is underway to find candidate genes with mutations that could be responsible for the blood pressure effect. Novel diagnostic, prognostic, preventive and/or therapeutic targets for essential hypertension and hypertension-associated diseases are likely to emerge from the identification of these quantitative trait loci. Potential applications of these quantitative trait loci to humans are suggested from the positive results from several association studies, demonstrating the existence of quantitative trait loci in the broad homologous regions.

Understanding the genetic architecture of adventitious root and related shoot traits will facilitate the cultivation of superior genotypes. In this study, we measured 12 adventitious root and related shoot traits of 434 F1 genotypes originating from Populus deltoides ‘Danhong’ × Populus simonii ‘Tongliao1’ and conducted an integrative analysis of quantitative trait locus (QTL) mapping and RNA-Seq data to dissect their genetic architecture and regulatory genes. Extensive segregation, high repeatability, and significant correlation relationship were detected for the investigated traits. A total of 150 QTLs were associated with adventitious root traits, explaining 3.1–6.1% of phenotypic variation (PVE); while 83 QTLs were associated with shoot traits, explaining 3.1–19.8% of PVE. Twenty-five QTL clusters and 40 QTL hotspots were identified for the investigated traits. Ten QTL clusters were overlapped in both adventitious root traits and related shoot traits. Transcriptome analysis identified 10,172 differentially expressed genes (DEGs) among two parents, three fine rooting and three poor-rooting genotypes, 143 of which were physically located within the QTL intervals. K-means cluster and weighted gene co-expression network analysis showed that PtAAAP19 (Potri.004G111400) encoding amino acid transport protein was tightly associated with adventitious roots and highly expressed in fine-rooting genotypes. Compare with ‘Danhong’, 153 bp deletion in the coding sequence of PtAAAP19 in ‘Tongliao1’ gave rise to lack one transmembrane domain, which might cause the variation of adventitious roots. Taken together, this study deciphered the genetic basis of adventitious root and related shoot traits and provided potential function genes for genetic improvement of poplar breeding.

High-density genetic map and quantitative trait loci (QTL) mapping are powerful tools for identifying genomic regions that may be responsible for such polygenic trait as growth. A high-density genetic linkage map was constructed by sequencing 198 individuals in a F1 family of silver carp (Hypophthalmichthys molitrix) in this study. This genetic map spans a length of 2,721.07 cM with 3,134 SNPs distributed on 24 linkage groups (LGs). Comparative genomic mapping presented a high level of syntenic relationship between silver carp and zebrafish. We detected one major and nineteen suggestive QTL for 4 growth-related traits (body length, body height, head length and body weight) at 6, 12 and 18 months post hatch (mph), explaining 10.2~19.5% of phenotypic variation. All six QTL for growth traits of 12 mph generally overlapped with QTL for 6 mph, while the majority of QTL for 18 mph were identified on two additional LGs, which may reveal a different genetic modulation during early and late muscle growth stages. Four potential candidate genes were identified from the QTL regions by homology searching of marker sequences against zebrafish genome. Hepcidin, a potential candidate gene identified from a QTL interval on LG16, was significantly associated with growth traits in the analyses of both phenotype-SNP association and mRNA expression between small-size and large-size groups of silver carp. These results provide a basis for elucidating the genetic mechanisms for growth and body formation in silver carp, a world aquaculture fish.

BackgroundPre-harvest sprouting (PHS) is a major problem for wheat production due to its direct detrimental effects on wheat yield, end-use quality and seed viability. Annually, PHS is estimated to cause > 1.0 billion USD in losses worldwide. Therefore, identifying PHS resistance quantitative trait loci (QTLs) is crucial to aid molecular breeding efforts to minimize losses. Thus, a doubled haploid mapping population derived from a cross between white-grained PHS susceptible cv AAC Innova and red-grained resistant cv AAC Tenacious was screened for PHS resistance in four environments and utilized for QTL mapping.ResultsTwenty-one PHS resistance QTLs, including seven major loci (on chromosomes 1A, 2B, 3A, 3B, 3D, and 7D), each explaining ≥10% phenotypic variation for PHS resistance, were identified. In every environment, at least one major QTL was identified. PHS resistance at most of these loci was contributed by AAC Tenacious except at two loci on chromosomes 3D and 7D where it was contributed by AAC Innova. Thirteen of the total twenty-one identified loci were located to chromosome positions where at least one QTL have been previously identified in other wheat genotype(s). The remaining eight QTLs are new which have been identified for the first time in this study. Pedigree analysis traced several known donors of PHS resistance in AAC Tenacious genealogy. Comparative analyses of the genetic intervals of identified QTLs with that of already identified and cloned PHS resistance gene intervals using IWGSC RefSeq v2.0 identified MFT-A1b (in QTL interval QPhs.lrdc-3A.1) and AGO802A (in QTL interval QPhs.lrdc-3A.2) on chromosome 3A, MFT-3B-1 (in QTL interval QPhs.lrdc-3B.1) on chromosome 3B, and AGO802D, HUB1, TaVp1-D1 (in QTL interval QPhs.lrdc-3D.1) and TaMyb10-D1 (in QTL interval QPhs.lrdc-3D.2) on chromosome 3D. These candidate genes are involved in embryo- and seed coat-imposed dormancy as well as in epigenetic control of dormancy.ConclusionsOur results revealed the complex PHS resistance genetics of AAC Tenacious and AAC Innova. AAC Tenacious possesses a great reservoir of important PHS resistance QTLs/genes supposed to be derived from different resources. The tracing of pedigrees of AAC Tenacious and other sources complements the validation of QTL analysis results. Finally, comparing our results with previous PHS studies in wheat, we have confirmed the position of several major PHS resistance QTLs and candidate genes.

Local properties of the genome can bias QTL analyses

High temperatures present considerable challenges to global fish growth and production, yet the genetic basis of heat tolerance remains underexplored. This study combines quantitative trait locus (QTL) mapping and genome-wide association studies (GWAS) to examine the genetics of heat tolerance in Larimichthys polyactis. As a result, a genetic linkage map was constructed with 3237 bin markers spanning 24 linkage groups and totaling 1900.84 centimorgans, using genotyping-by-sequencing of a full-sib family comprising 120 progeny and their two parents. Based on this genetic linkage map, QTL mapping identified four QTLs associated with heat tolerance, which encompassed 18 single nucleotide polymorphisms and harbored 648 genes within the QTL intervals. The GWAS further disclosed 76 candidate genes related to heat tolerance, 56 of which overlapped with the QTL results. Enrichment analysis indicated that these genes are involved in immune response, development, lipid metabolism, and endocrine regulation. qPCR validation of 14 prioritized genes, which were simultaneously enriched in Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways, confirmed significant upregulation of smpd5, polr3d, rab11fip2, and gfpt1, along with downregulation of gpat4 and grk5 after 6 h of heat stress. These findings demonstrate their responsiveness to elevated high temperatures. This meta-analysis of QTL mapping and GWAS has successfully identified functional genes related to heat tolerance, enhancing understanding of the genetic architecture underlying this critical trait in L. polyactis. It also provides a molecular breeding tool to improve genetic traits associated with heat tolerance in cultured L. polyactis.

BackgroundPigmentation patterns are one of the most recognizable phenotypes across the animal kingdom. They play an important role in camouflage, communication, mate recognition and mate choice. Most progress on understanding the genetics of pigmentation has been achieved via mutational analysis, with relatively little work done to understand variation in natural populations. Pigment patterns vary dramatically among species of cichlid fish from Lake Malawi, and are thought to be important in speciation. In this study, we crossed two species, Metriaclima zebra and M. mbenjii, that differ in several aspects of their body and fin color. We genotyped 798 SNPs in 160 F2 male individuals to construct a linkage map that was used to identify quantitative trait loci (QTL) associated with the pigmentation traits of interest. We also used the linkage map to anchor portions of the M. zebra genome assembly.ResultsWe constructed a linkage map consisting of 834 markers in 22 linkage groups that spanned over 1,933 cM. QTL analysis detected one QTL each for dorsal fin xanthophores, caudal fin xanthophores, and pelvic fin melanophores. Dorsal fin and caudal fin xanthophores share a QTL on LG12, while pelvic fin melanophores have a QTL on LG11. We used the mapped markers to anchor 66.5% of the M. zebra genome assembly. Within each QTL interval we identified several candidate genes that might play a role in pigment cell development.ConclusionThis is one of a few studies to identify QTL for natural variation in fish pigmentation. The QTL intervals we identified did not contain any pigmentation genes previously identified by mutagenesis studies in other species. We expect that further work on these intervals will identify new genes involved in pigment cell development in natural populations.

Meat quality traits are the most economically important traits affecting the beef industry in Korea. We performed a whole genome quantitative trait locus (QTL) mapping study of carcass data in Hanwoo Korean cattle. Two hundred sixty-six Hanwoo steers from 65 sires were genotyped using a 10K Affymetrix SNP chip. The average SNP interval across the bovine genome was 1.5Mb. Associations between each individual SNP and four carcass traits [carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT), and marbling (MAR)] were assessed using a linear mixed model of each trait. Combined linkage and linkage disequilibrium analysis (LDLA) detected six potential QTL on BTA04, 06, 13, 16, 17, and 23 at the chromosome-wise level (P<0.05). Two MAR QTL were detected at 52.2 cM of BTA06 and 46.04 cM of BTA17. We identified three genes (ARAP2, LOC539460, and LOC511424) in the QTL region of BTA06 and seven genes (RPS14, SCARB1, LOC782103, BRI3BP, AACS, DHX37, and UBC) in the QTL region of BTA17. One significant QTL for CWT was detected at 100 cM on BTA04 and the corresponding QTL region spanned 1.7 cM from 99.7 to 101.4 cM. For EMA QTL, one significant QTL was detected at 3.9 cM of BTA23 and the most likely QTL interval was 1.4 cM, placing 15 candidate genes in the marker bracket. Finally, two QTL for BFT were identified at 68 cM on BTA13 and 24 cM on BTA16. The LPIN3 gene, which is functionally associated with lipodystrophy in humans, is located in the BFT QTL on BTA13. Thus, two potential candidate genes, acetoacetyl-CoA synthetase (AACS) and lipin (LPIN), were detected in QTL regions on BTA17 for MAR and BTA13 for BFT, respectively. In conclusion, LDLA analysis can be used to detect chromosome regions harboring QTL and candidate genes with a low density SNP panel, yielding relatively narrow confidence intervals regarding location.