Interval Mapping Research Articles

Reproducible Quantitative Trait Loci for Resistance to Soft Rot Caused by Dickeya dianthicola Derived from the Wild Potato Solanum microdontum (PI 458355) Are Located on Chromosomes 1, 3, and 5.

The potato wild relative Solanum microdontum is a breeder-friendly source of genetic resistance to soft rot. Our objectives were to (i) identify loci associated with soft rot resistance in S. microdontum germplasm and (ii) develop bi-parental populations in a self-compatible S. tuberosum genetic background to recover segregating F2 progenies, construct a linkage map, and identify quantitative trait loci (QTLs). Under objective (i), tubers from 103 S. microdontum genotypes from the United States Potato Genebank were inoculated with a high virulence strain of Dickeya dianthicola, and lesion size was measured after a 24-h incubation period at 30°C. Association analysis using 3,490 polymorphic Infinium array SNP markers identified soft rot resistance loci on chromosomes 1, 2, 3, 5, 7, 8, 11, and 12. Under objective (ii), a resistant S. microdontum accession PI 458355 was crossed with a highly fertile, self-compatible, diploid S. tuberosum pollen parent (PI 654351) to generate segregating F2 populations. Composite interval mapping was conducted using a genetic linkage map with 970 GBS-based SNP markers. Reproducible QTLs were detected on chromosomes 1, 3, and 5, explaining 11, 13, and 23% of the phenotypic variation, respectively. Homozygous S. microdontum alleles at the QTL on chromosome 5 and heterozygous or homozygous S. microdontum alleles at QTLs on chromosomes 1 and 3 significantly decrease lesion size compared with the homozygous S. tuberosum parent. The germplasm created in these studies provides a resource for studying traits from S. microdontum, and we can use the advanced F2 selections for future potato improvement. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Linkage mapping of root shape traits in two carrot populations.

This study investigated the genetic basis of carrot root shape traits using composite interval mapping in two biparental populations (n = 119 and n = 128). The roots of carrot F2:3 progenies were grown over 2 years and analyzed using a digital imaging pipeline to extract root phenotypes that compose market class. Broad-sense heritability on an entry-mean basis ranged from 0.46 to 0.80 for root traits. Reproducible quantitative trait loci (QTL) were identified on chromosomes 2 and 6 on both populations. Colocalization of QTLs for phenotypically correlated root traits was also observed and coincided with previously identified QTLs in published association and linkage mapping studies. Individual QTLs explained between 14 and 27% of total phenotypic variance across traits, while four QTLs for length-to-width ratio collectively accounted for up to 73% of variation. Predicted genes associated with the OFP-TRM (OVATE Family Proteins-TONNEAU1 Recruiting Motif) and IQD (IQ67 domain) pathway were identified within QTL support intervals. This observation raises the possibility of extending the current regulon model of fruit shape to include carrot storage roots. Nevertheless, the precise molecular mechanisms through which this pathway operates in roots characterized by secondary growth originating from cambium layers remain unknown.

Mapping of Genomic Regions for Biochemical and Physiological Parameters Contributing Towards Drought Tolerance in Horsegram (Macrotyloma uniflorum (Lam.) Verdc.).

Horsegram (Macrotyloma uniflorum (Lam.) Verdc.) is a drought hardy legume which can be grown in varied soil and temperature regimes. Though it has numerous, nutritive and medicinal benefits, it still lags behind other legumes in terms of genomic resources and genetic improvement. This crop is mostly cultivated on marginal and drought-prone area; thus, genetics of drought stress tolerance can be understood by studying the various drought parameters. To get insight, quantitative trait loci for drought-tolerant traits were identified using an intraspecific mapping population of 162 F8 recombinant inbred lines derived from a cross between HPKM249 and HPK4. The linkage map already developed was used along with the phenotypic data for biochemical and physiological parameters to identify genomic regions which are linked to drought tolerance. In the study, a total of seven QTLs were identified for ten different drought-related traits. One QTL for malondialdehyde content on linkage group 2, two QTLs for root length on linkage groups 3 and 9, one QTL each for proline and chlorophyll content under drought stress on linkage group 4, and one QTL each for root dry weight and root fresh weight on linkage group 5 were identified using composite interval mapping. The identified QTLs will be utilized in marker-assisted breeding and increase our understanding on the physiology of drought stress tolerance.

Rapidly mining candidate cotton drought resistance genes based on key indicators of drought resistance.

Focusing on key indicators of drought resistance is highly important for quickly mining candidate genes related to drought resistance in cotton. In the present study, drought resistance was identified in drought resistance-related RIL populations during the flowering and boll stages, and multiple traits were evaluated; these traits included three key indicators: plant height (PH), single boll weight (SBW) and transpiration rate (Tr). Based on these three key indicators, three groups of extreme mixing pools were constructed for BSA-seq. Based on the mapping interval of each trait, a total of 6.27 Mb QTL intervals were selected on chromosomes A13 (3.2 Mb), A10 (2.45 Mb) and A07 (0.62 Mb) as the focus of this study. Based on the annotation information and qRT‒PCR analysis, three key genes that may be involved in the drought stress response of cotton were screened: GhF6'H1, Gh3AT1 and GhPER55. qRT‒PCR analysis of parental and extreme germplasm materials revealed that the expression of these genes changed significantly under drought stress. Cotton VIGS experiments verified the important impact of key genes on cotton drought resistance. This study focused on the key indicators of drought resistance, laying the foundation for the rapid mining of drought-resistant candidate genes in cotton and providing genetic resources for directed molecular breeding of drought resistance in cotton.

Linkage map construction and QTL identification for yield related traits of rice (Oryza sativa L.)

The study was undertaken to construct a linkage map and to identify the QTLs for heading date, plant height, and culm strength using a F2 population derived from Ayeyarmin/IR 64, two varieties distinctly different in heading date and plant height. A total of 233 F2 individuals were genotyped using 70 SSR primers, and phenotypic data were collected for heading date, plant height and culm strength traits. Fifty-four out of 70 marker loci were mapped across twelve rice chromosomes spanning a total map length of 1592.30 cM with an average distance of 29.49 cM between adjacent marker loci using MAPMAKER/EXP 3. Composite interval mapping for QTL analysis was conducted with 1000 permutation time at 0.05 probability level using WinQTL Cartographer version 2.5. One major QTL for heading date was mapped between marker RM204 and RM50 on chromosome 6. One major QTL for culm strength was mapped between marker RM1003-RM5931, and two major QTLs for plant height were mapped between RM84-RM1003 and RM1003-RM5931 on chromosome 1. The molecular markers linked to the major QTLs could be potentially used in marker-assisted selection for the rainfed rice improvement breeding program.

Parametrizability of infinitely generated attractors

An infinite iterated function system (IIFS) is a countable collection of contraction maps on a compact metric space. In this paper we study the conditions under which the attractor of such a system admits a parameterization by a continuous or Hölder continuous map of the unit interval.

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

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

Mapping crown rust resistance in the oat diploid accession PI 258731 (Avena strigosa).

Oat crown rust, caused by Puccinia coronata Corda f. sp. avenae Eriks. (Pca), is a major biotic impediment to global oat production. Crown rust resistance has been described in oat diploid species A. strigosa accession PI 258731 and resistance from this accession has been successfully introgressed into hexaploid A. sativa germplasm. The current study focuses on 1) mapping the location of QTL containing resistance and evaluating the number of quantitative trait loci (QTL) conditioning resistance in PI 258731; 2) understanding the relationship between the original genomic location in A. strigosa and the location of the introgression in the A. sativa genome; 3) identifying molecular markers tightly linked with PI 258731 resistance loci that could be used for marker assisted selection and detection of this resistance in diverse A. strigosa accessions. To achieve this, A. strigosa accessions, PI 258731 and PI 573582 were crossed to produce 168 F5:6 recombinant inbred lines (RILs) through single seed descent. Parents and RILs were genotyped with the 6K Illumina SNP array which generated 168 segregating SNPs. Seedling reactions to two isolates of Pca (races TTTG, QTRG) were conditioned by two genes (0.6 cM apart) in this population. Linkage mapping placed these two resistant loci to 7.7 (QTRG) to 8 (TTTG) cM region on LG7. Field reaction data was used for QTL analysis and the results of interval mapping (MIM) revealed a major QTL (QPc.FD-AS-AA4) for field resistance. SNP marker assays were developed and tested in 125 diverse A. strigosa accessions that were rated for crown rust resistance in Baton Rouge, LA and Gainesville, FL and as seedlings against races TTTG and QTRG. Our data proposed SNP marker GMI_ES17_c6425_188 as a candidate for use in marker-assisted selection, in addition to the marker GMI_ES02_c37788_255 suggested by Rine's group, which provides an additional tool in facilitating the utilization of this gene in oat breeding programs.

Quantitative Trait Loci Mapping for Adult-Plant Stripe Rust Resistance in Chinese Wheat Cultivar Weimai 8

Wheat stripe rust, triggered by Puccinia striiformis f. sp. tritici, is among the most widespread and damaging wheat (Triticum L.) diseases. The development of cultivars harboring adult plant resistance (APR) to stripe rust is a better approach to control the disease. The current study aimed to map APR to stripe rust via the QTL mapping of 165 F2–6 recombinant inbred lines (RILs), derivatives of Weimai 8/Zhengzhou 5389. The collection of phenotypic data for the stripe-rust resistance of both parents and all 165 RILs were conducted at Baoding, Hebei Province, during the 2016–2017 cropping seasons, and at Mianyang, Sichuan Province, during the 2017–2018 and 2018–2019 sowing seasons. The RIL populations and parents were also genotyped with 860 pairs of simple-sequence-repeat (SSR) primers to map APR QTLs to stripe-rust resistant. Moreover, a 55K SNP chip was used for small group bulk segregant analysis conducted to locate the genetic map location and concentration of the SNP markers on the wheat genome. Inclusive composite interval mapping (IciMapping 3.2) software identified four QTLs of stripe-rust resistance on chromosomes 1B, 2AS, 2BS, and 7DS, named QYr.wmy-1B, QYr.wmy-2AS, QYr.wmy-2BS, and QYr.wmy-7DS, which significantly explained 11.56–16.64%, 9.35–12.70%, 7.27–9.95%, and 11.49–15.07% of the phenotypic variation, respectively. All these QTLs were found from the resistant parent, Weimai 8. Meanwhile, the QTLs located on chromosomes 1B and 2AS were found close to Yr18 and Yr9, respectively. Furthermore, the results indicated that QYr.wmy-1B is possibly Yr9 and QYr.wmy-2AS is similar to Yr18 or might be a new QTL, whilst QYr.wmy-2BS and QYr.wmy-7DS were found to be different from previously reported stripe-rust-resistance QTLs and are possibly new QTLs. Overall, the QTLs and their closely associated molecular markers detected in this study could be a great source of input for marker-assisted selection to adult plant stripe-rust resistance in wheat-breeding programs.

Heat Stress-Tolerant Quantitative Trait Loci Identified Using Backcrossed Recombinant Inbred Lines Derived from Intra-Specifically Diverse Aegilops tauschii Accessions

In the face of climate change, bringing more useful alleles and genes from wild relatives of wheat is crucial to develop climate-resilient varieties. We used two populations of backcrossed recombinant inbred lines (BIL1 and BIL2), developed by crossing and backcrossing two intra-specifically diverse Aegilops tauschii accessions from lineage 1 and lineage 2, respectively, with the common wheat cultivar ‘Norin 61′. This study aimed to identify quantitative trait loci (QTLs) associated with heat stress (HS) tolerance. The two BILs were evaluated under heat stress environments in Sudan for phenology, plant height (PH), grain yield (GY), biomass (BIO), harvest index (HI), and thousand-kernel weight (TKW). Grain yield was significantly correlated with BIO and TKW under HS; therefore, the stress tolerance index (STI) was calculated for these traits as well as for GY. A total of 16 heat-tolerant lines were identified based on GY and STI-GY. The QTL analysis performed using inclusive composite interval mapping identified a total of 40 QTLs in BIL1 and 153 QTLs in BIL2 across all environments. We detected 39 QTLs associated with GY-STI, BIO-STI, and TKW-STI in both populations (14 in BIL1 and 25 in BIL2). The QTLs associated with STI were detected on chromosomes 1A, 3A, 5A, 2B, 4B, and all the D-subgenomes. We found that QTLs were detected only under HS for GY on chromosome 5A, TKW on 3B and 5B, PH on 3B and 4B, and grain filling duration on 2B. The higher number of QTLs identified in BIL2 for heat stress tolerance suggests the importance of assessing the effects of intraspecific variation of Ae. tauschii in wheat breeding as it could modulate the heat stress responses/adaptation. Our study provides useful genetic resources for uncovering heat-tolerant QTLs for wheat improvement for heat stress environments.

Identification of major QTLs for drought tolerance in soybean, together with a novel candidate gene, GmUAA6.

Drought tolerance is a complex trait in soybean that is controlled by polygenetic quantitative trait loci (QTLs). In this study, wilting score, days-to-wilting, leaf relative water content, and leaf relative conductivity were used to identify QTLs associated with drought tolerance in recombinant inbred lines derived from a cross between a drought-sensitive variety, Lin, and a drought-tolerant variety, Meng. A total of 33 drought-tolerance QTLs were detected. Of these 17 were major QTLs. In addition, 15 were novel drought-tolerance QTLs. The most predominant QTL was on chromosome 11. This was detected in at least three environments. The overlapped mapping interval of the four measured traits was 0.2 cM in genetic distance (about 220 kb in physical length). Glyma.11g143500 (designated as GmUAA6), which encodes a UDP-N-acetylglucosamine transporter, was identified as the most likely candidate gene. The allele of GmUAA6 from Lin (GmUAA6Lin) was associated with improved soybean drought tolerance. Overexpression of GmUAA6Lin in Arabidopsis and soybean hairy roots enhanced drought tolerance. Furthermore, a 3-bp insertion/deletion (InDel) in the coding sequence of GmUAA6 explained up to 49.9% of the phenotypic variation in drought tolerance-related traits, suggesting that this InDel might be used in future marker-assisted selection of drought-tolerant lines in soybean breeding programs.

High density linkage mapping and QTL analysis of salt tolerance-related traits using ddRAD sequencing in Indian mustard [Brassica juncea (L.) Czern & Coss

KeyMessage: We identified QTLs for salinity tolerance in Indian mustard, paving the way for salt-tolerant mustard genotypes, enhancing crop resilience in saline environments.Soil salinity is a major abiotic stress that reduced the growth and productivity of numerous crops, including Indian mustard. Natural genetic variations in salt tolerance (ST) were observed but no QTL mapping has been done for ST related traits in Indian mustard. In this study, to identify QTLs for ST traits, 250 stabilized F7:8 recombinant inbred lines (RILs) mapping population of Indian mustard was developed from a cross CS 614–1-1–100-13 × CS 56 and evaluated them under control, and irrigated water salinity of ECiw 12 dSm−1 for two Rabi seasons 2020–21, and 2021–22 for 18 traits. Most of traits were shown normal distribution curve, indicating population suitable for QTLs identification. Genotyping-by-sequencing (GBS) using ddRAD sequencing of the population was performed and total 2506 SNPs sorted from total 2594,666 variants after discarding markers as no progeny polymorphism and not passing chi square test which cover 1127.85 cM distance on linkage map. A total of 47 QTLs associated with ST traits were identified using composite interval mapping (CIM). Out of 47 QTLs, 12 QTLs were major QTL such as: for days to 50% flowering (qDFsB07.1), days to maturity (qDMsB08.1), number of primary branches per plant (qPBRcA05.1, and qPBRsA07.1), main shoot length (qMSLsB05.1, and qMSLcB05.1), seeds per siliqua (qSPScA09.1), yield per plant (qYPPsB06.1), transpiration rate (qEsA01.1), Na+ concentration of stem (qSNasA01.1), K+ concentration of stem (qSKsB03.1), and K+ concentration of root (qRKsB01.1) was identified with SNPs. This novel research findings will facilitate QTLs/genes mapping that can be used to identify candidate gene and transfer this with marker assistant selection (MAS).

The Lyapunov spectrum as the Newton-Raphson method for countable Markov interval maps

We consider MRL maps (Markov-Renyi-Lüroth), a class of interval maps with infinitely many branches that can have parabolic fixed points. We prove that for every MRL map T, the Lyapunov spectrum can be expressed in terms of the Legendre transform of the topological pressure of −tlog⁡|T′|, generalizing previous results in the area. We also show that the Lyapunov spectrum coincides with a function directly related to the Newton-Raphson method applied to the topological pressure of −tlog⁡|T′|.

Finding stable and closely linked QTLs against spot blotch in different planting dates during the adult stage in barley

The common resistance to Spot Blotch (SB) and drought stress in barley was studied using a RILs population caused Kavir × Badia cross. These lines were inoculated with Cochliobolus sativus Gonbad isolate during the adult stage and were evaluated for three crop seasons in different planting dates. The different osmotic potentials during the flowering were regulated by changing the planting dates. In total, 43 lines had resistant to SB and drought. The high-density linkage map covered 1045 cM of barley genome. A total of five stable and closely linked QTLs to SB resistance were mapped on chromosomes 2H, 3H, 4H and 7H using genome-wide composite interval mapping. Moreover, four stable and closely linked QTLs to SB susceptibility were located on chromosomes 3H, 4H, 5H and 7H. Additionally, the ISJ19-A, SCoT7-C, ISJ17-B, Bmac0144k, iPBS2415-1, Bmac0282b and EBmatc0016 markers can be used for positive screening of resistant cultivars. However, ISJ3-C, UMB310, ISJ9-B, UMB706, D03-D and iPBS2257-A markers can be used for negative screening of susceptible cultivars in marker-assisted selection. The bioinformatics studies showed that QRCsa-2H (ISJ19-A region), QRCsa-2H (SCoT7-C-ISJ17-B region), QRCsa-3H (Bmac0144k region), QRCsa-4H (iPBS2415-1 region) and QRCsa-7H (Bmac0282b-EBmatc0016 region) are involved in the carboxypeptidase, Glycosyltransferase, transcription factors, kinase and AP2/ERF, respectively.

Bifurcation structure of interval maps with orbits homoclinic to a saddle-focus

UDC 517.9 We study homoclinic bifurcations in an interval map associated with a saddle-focus of (2, 1)-type in Z 2 -symmetric systems. Our study of this map reveals a homoclinic structure of the saddle-focus, with bifurcation unfolding guided by the codimension-two Belyakov bifurcation. We consider three parameters of the map corresponding to the saddle quantity, splitting parameter, and the focal frequency of the smooth saddle-focus in a neighborhood of homoclinic bifurcations. We symbolically encode the dynamics of the map in order to find stability windows and locate homoclinic bifurcation sets in a computationally efficient manner. The organization and possible shapes of homoclinic bifurcation curves in the parameter space are examined, taking into account the symmetry and discontinuity of the map. Sufficient conditions for stability and local symbolic constancy of the map are presented. This study provides insights into the structure of homoclinic bifurcations of the saddle-focus map, furthering comprehension of low-dimensional chaotic systems.

QTL Mapping Using RIL Population.

Genetic maps are an excellent tool for the analysis of important traits, the development of which is the result of the combined expression of several genes, enabling the genomic localization of the factors determining them. Such features, characterized by a normal distribution of values, are referred to as quantitative or polygenic. The analysis of their genetic background using a chromosome map is called the mapping of quantitative traits loci (QTL). QTL analysis is a statistical method of determining the genetic association of phenotypic data (trait measurements) with genotypic data (DNA markers assigned to linkage groups).There are numerous tools developed for QTL mapping. This chapter introduces Windows QTL Cartographer with Composite Interval Mapping (CIM) method, which estimates the QTL position by combining interval mapping with multiple regression. The genotypic and phenotypic data used in the exemplary QTL mapping procedure were obtained for the recombinant inbred line (RIL) population of rye. Plant height, assessed in three seasons, was the exemplary trait under study.

Identification of stable QTLs and candidate genes associated with plant height and spike length in common wheat

Context Plant height and spike length are important traits with significant impact on the potential yield of wheat. It is essential to dissect their genetic control in order to improve yield potential through breeding. Aims We aimed to identify genomic regions with stable and major-effect quantitative trail loci (QTLs) associated with plant height and spike length in common wheat (Triticum aestivum L.). Methods Three recombinant inbred line (RIL) mapping populations were created by crossing a common parental line, Roshan, with three different cultivars: Sabalan, Falat, and Superhead. The RILs were grown over 3 years at five locations under water-stressed and well-watered conditions. Plant height and spike length were measured during each experiment, and inclusive composite interval mapping was used to analyse the data in both single-and multi-environment analyses. Key results Single-environment analysis detected 32 additive QTLs in the three populations. Three novel, stable and non-epistatic QTLs were identified: qPH1D for plant height in the Sabalan background; and qSL1D and qSL1B.3 for spike length in the Falat and Superhead backgrounds, respectively. Between the flanking markers on chromosomes 1B, 1D and 4B, 191 genes were predicted, 20 of which were identified as potential candidates with roles in wheat plant height and spike-related traits. Conclusions The study identified stable QTLs for plant height and spike length in wheat, providing valuable markers for future breeding programs and enhancing yield potential. Implications These results lay a good foundation for fine mapping of QTLs/genes for molecular marker-assisted breeding in wheat in the future.

Inheritance of factors and validation of loci linked to the kernel row number in tropical field corn (Zea mays L.)

Sustainable feeding of a growing population with nutritional security in the era of climate change is the leading challenge facing adeveloping nation. Field corn is one of those crops that can help achieve this goal due to its high productivity and wide adaptation. There is scope for further improving field corn productivity by targeting component traits such as kernel row number (KRN). In the present investigation, the kernel row number displayed significant variation as well asa positive correlation with yield and yield component traits under the study. The inheritance of the KRN trait was analyzed using the Wright-Castle estimator and chi-square test in two sets of F2 populations (AH4499 and AH4500) and parental lines (AI 505, AI 541 and AI 542). The analyses by the Wright-Castle estimator revealed that KRN is governed by two effective factors (1.92@ 2) with four contributing alleles in the AH-4499 population and four effective factors (3.93 @ 4) with eight contributing alleles in the AH-4500 population. Further analysis by East’s hypothesis (frequency of recessivehomozygote in F2=1/4n) produced similar results and the Chi-square test (0.01 level of significance) confirmed the non-significant difference between expected and observed recessive frequency in F2sof both the populations. This suggested that KRN is governed at least four genes with eight contributing alleles. In both the F2 populations, F1 value was non-significantly close to the mid-parent value suggesting the additive nature of KRN. Further, Bulked Segregant Analysis was carried out using AH-4500-F2 population having 231 individuals to validate linked loci. Out of 58 flanking SSR markers previously reported for the KRN trait, only nine markers were polymorphic for this population. These linked markers identified two putative QTLs for KRN i.e., qKRN2.1 and qKRN2.2 on chromosome 2 through inclusive composite interval mapping. The genetic distance with closely associated markers, bnlg 1017 was 9 cM for qKRN2.1 with a LOD score of 10.24 and a Proportion of Variance Explained (PVE%) of 16.86. The marker-trait association was further validated using F2:3 population and it was found that the marker bnlg 1017 showed a significant association with the KRN trait. Thus, the marker bnlg 1017 could be used to identify high KRN genotypes for use in breeding programs to enhance the productivity of tropical field corn.

Genetic Architecture of Body Fat Composition in Mice

Body fat composition is a quantitative measure of obesity, a major health concern in humans. Laboratory mice are considered an excellent model for dissecting the genetic basis of obesity due to the genetic variation present in inbred strains for body size and fat composition, as well as their historical use as a model organism for human disease studies. To identify the loci controlling fat pad weights and body weight in mice, we performed a quantitative trait loci (QTL) analysis of 513 (SM/J x NZB/BINJ) F2 individuals fed a high-fat diet for 14 weeks. Our analysis separated fat composition genetic effects from those affecting overall body sizes in mouse. Composite interval mapping (CIM) results showed that body weight was conditioned by three major additive QTLs, explaining 3 to 30% of the phenotypic variation. One significant QTL on chromosome 19 conditioned all fat pads with the exception of the inguinal fat weight, which was controlled by a different QTL also on chromosome 19. Significant QTLs associated with fat compositions were detected on chromosomes 17 and 19 and differed from those of body weight. The fat pad QTLs also showed mainly additive gene effects and they explained 2 to 7% of variation in fat composition. Joint analysis of correlated traits detected five additional large effect QTLs on five different linkage groups. These findings have indicated that fat composition and body weight in mouse are conditioned by one to three major additive genes and can therefore be potentially manipulated in controlling obesity.

Genetic mapping of dynamic control of leaf angle across multiple canopy levels in maize.

Optimizing leaf angle and other canopy architecture traits has helped modern maize (Zea mays L.) become adapted to higher planting densities over the last 60 years. Traditional investigations into genetic control of leaf angle have focused on one leaf or the average of multiple leaves; as a result, our understanding of genetic control across multiple canopy levels is still limited. To address this, genetic mapping across four canopy levels was conducted in the present study to investigate the genetic control of leaf angle across the canopy. We developed two populations of doubled haploid lines derived from three inbreds with distinct leaf angle phenotypes. These populations were genotyped with genotyping-by-sequencing and phenotyped for leaf angle at four different canopy levels over multiple years. To understand how leaf angle changes across the canopy, the four measurements were used to derive three additional traits. Composite interval mapping was conducted with the leaf-specific measurements and the derived traits. A set of 59 quantitative trait loci (QTLs) were uncovered for seven traits, and two genomic regions were consistently detected across multiple canopy levels. Additionally, seven genomic regions were found to contain consistent QTLs with either relatively stable or dynamic effects at different canopy levels. Prioritizing the selection of QTLs with dynamic effects across the canopy will aid breeders in selecting maize hybrids with the ideal canopy architecture that continues to maximize yield on a per area basis under increasing planting densities.