Initial Quantitative Trait Loci Research Articles

Integrated meta-analysis and transcriptomics pinpoint genomic loci and novel candidate genes associated with submergence tolerance in rice

BackgroundDue to rising costs, water shortages, and labour shortages, farmers across the globe now prefer a direct seeding approach. However, submergence stress remains a major bottleneck limiting the success of this approach in rice cultivation. The merger of accumulated rice genetic resources provides an opportunity to detect key genomic loci and candidate genes that influence the flooding tolerance of rice.ResultsIn the present study, a whole-genome meta-analysis was conducted on 120 quantitative trait loci (QTL) obtained from 16 independent QTL studies reported from 2004 to 2023. These QTL were confined to 18 meta-QTL (MQTL), and ten MQTL were successfully validated by independent genome-wide association studies from diverse natural populations. The mean confidence interval (CI) of the identified MQTL was 3.44 times narrower than the mean CI of the initial QTL. Moreover, four core MQTL loci with genetic distance less than 2 cM were obtained. By combining differentially expressed genes (DEG) from two transcriptome datasets with 858 candidate genes identified in the core MQTL regions, we found 38 common differentially expressed candidate genes (DECGs). In silico expression analysis of these DECGs led to the identification of 21 genes with high expression in embryo and coleoptile under submerged conditions. These DECGs encode proteins with known functions involved in submergence tolerance including WRKY, F-box, zinc fingers, glycosyltransferase, protein kinase, cytochrome P450, PP2C, hypoxia-responsive family, and DUF domain. By haplotype analysis, the 21 DECGs demonstrated distinct genetic differentiation and substantial genetic distance mainly between indica and japonica subspecies. Further, the MQTL7.1 was successfully validated using flanked marker S2329 on a set of genotypes with phenotypic variation.ConclusionThis study provides a new perspective on understanding the genetic basis of submergence tolerance in rice. The identified MQTL and novel candidate genes lay the foundation for marker-assisted breeding/engineering of flooding-tolerant cultivars conducive to direct seeding.

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

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

Detection of consensus genomic regions and candidate genes for quality traits in barley using QTL meta-analysis.

Improving barley grain quality is a major goal in barley breeding. In this study, a total of 35 papers focusing on quantitative trait loci (QTLs) mapping for barley quality traits published since 2000 were collected. Among the 454 QTLs identified in these studies, 349 of them were mapped onto high-density consensus maps, which were used for QTL meta-analysis. Through QTL meta-analysis, the initial QTLs were integrated into 41 meta-QTLs (MQTLs) with an average confidence interval (CI) of 1. 66 cM, which is 88.9% narrower than that of the initial QTLs. Among the 41 identified MQTLs, 25 were subsequently validated in publications using genome-wide association study (GWAS). From these 25 validated MQTLs, ten breeder's MQTLs were selected. Synteny analysis comparing barley and wheat MQTLs revealed orthologous relationships between eight breeder's MQTLs and 45 wheat MQTLs. Additionally, 17 barley homologs associated with rice quality traits were identified within the regions of the breeder's MQTLs through comparative analysis. The findings of this study provide valuable insights for molecular marker-assisted breeding and the identification of candidate genes related to quality traits in barley.

A meta-QTL analysis highlights genomic hotspots associated with phosphorus use efficiency in rice (Oryza sativa L.).

Phosphorus use efficiency (PUE) is a complex trait, governed by many minor quantitative trait loci (QTLs) with small effects. Advances in molecular marker technology have led to the identification of QTLs underlying PUE. However, their practical use in breeding programs remains challenging due to the unstable effects in different genetic backgrounds and environments, interaction with soil status, and linkage drag. Here, we compiled PUE QTL information from 16 independent studies. A total of 192 QTLs were subjected to meta-QTL (MQTL) analysis and were projected into a high-density SNP consensus map. A total of 60 MQTLs, with significantly reduced number of initial QTLs and confidence intervals (CI), were identified across the rice genome. Candidate gene (CG) mining was carried out for the 38 MQTLs supported by multiple QTLs from at least two independent studies. Genes related to amino and organic acid transport and auxin response were found to be abundant in the MQTLs linked to PUE. CGs were cross validated using a root transcriptome database (RiceXPro) and haplotype analysis. This led to the identification of the eight CGs (OsARF8, OsSPX-MFS3, OsRING141, OsMIOX, HsfC2b, OsFER2, OsWRKY64, and OsYUCCA11) modulating PUE. Potential donors for superior PUE CG haplotypes were identified through haplotype analysis. The distribution of superior haplotypes varied among subspecies being mostly found in indica but were largely scarce in japonica. Our study offers an insight on the complex genetic networks that modulate PUE in rice. The MQTLs, CGs, and superior CG haplotypes identified in our study are useful in the combination of beneficial alleles for PUE in rice.

Meta-analysis of the quantitative trait loci associated with agronomic traits, fertility restoration, disease resistance, and seed quality traits in pigeonpea (Cajanus cajan L.).

A meta-analysis of quantitative trait loci (QTLs), associated with agronomic traits, fertility restoration, disease resistance, and seed quality traits was conducted for the first time in pigeonpea (Cajanus cajan L.). Data on 498 QTLs was collected from 9 linkage mapping studies (involving 21 biparental populations). Of these 498, 203 QTLs were projected onto "PigeonPea_ConsensusMap_2022," saturated with 10,522 markers, which resulted in the prediction of 34 meta-QTLs (MQTLs). The average confidence interval (CI) of these MQTLs (2.54cM) was 3.37 times lower than the CI of the initial QTLs (8.56cM). Of the 34 MQTLs, 12 high-confidence MQTLs with CI (≤5cM) and a greater number of initial QTLs (≥5) were utilized to extract 2255 gene models, of which 105 were believed to be associated with different traits under study. Furthermore, eight of these MQTLs were observed to overlap with several marker-trait associations or significant SNPs identified in previous genome-wide association studies. Furthermore, synteny and ortho-MQTL analyses among pigeonpea and four related legumes crops, such as chickpea, pea, cowpea, and French bean, led to the identification of 117 orthologous genes from 20 MQTL regions. Markers associated with MQTLs can be employed for MQTL-assisted breeding as well as to improve the prediction accuracy of genomic selection in pigeonpea. Additionally, MQTLs may be subjected to fine mapping, and some of the promising candidate genes may serve as potential targets for positional cloning and functional analysis to elucidate the molecular mechanisms underlying the target traits.

Combination of meta-analysis of QTL and GWAS to uncover the genetic architecture of seed yield and seed yield components in common bean.

Increasing seed yield in common bean could help to improve food security and reduce malnutrition globally due to the high nutritional quality of this crop. However, the complex genetic architecture and prevalent genotype by environment interactions for seed yield makes increasing genetic gains challenging. The aim of this study was to identify the most consistent genomic regions related with seed yield components and phenology reported in the last 20 years in common bean. A meta-analysis of quantitative trait locus (QTL) for seed yield components and phenology (MQTL-YC) was performed for 394 QTL reported in 21 independent studies under sufficient water and drought conditions. In total, 58 MQTL-YC over different genetic backgrounds and environments were identified, reducing threefold on average the confidence interval (CI) compared with the CI for the initial QTL. Furthermore, 40 MQTL-YC identified were co-located with 210 SNP peak positions reported via genome-wide association (GWAS), guiding the identification of candidate genes. Comparative genomics among these MQTL-YC with MQTL-YC reported in soybean and pea allowed the identification of 14 orthologous MQTL-YC shared across species. The integration of MQTL-YC, GWAS, and comparative genomics used in this study is useful to uncover and refine the most consistent genomic regions related with seed yield components for their use in plant breeding.

Deciphering key genomic regions controlling flag leaf size in wheat via integration of meta-QTL and in silico transcriptome assessment

BackgroundGrain yield is a complex and polygenic trait influenced by the photosynthetic source-sink relationship in wheat. The top three leaves, especially the flag leaf, are considered the major sources of photo-assimilates accumulated in the grain. Determination of significant genomic regions and candidate genes affecting flag leaf size can be used in breeding for grain yield improvement.ResultsWith the final purpose of understanding key genomic regions for flag leaf size, a meta-analysis of 521 initial quantitative trait loci (QTLs) from 31 independent QTL mapping studies over the past decades was performed, where 333 loci eventually were refined into 64 meta-QTLs (MQTLs). The average confidence interval (CI) of these MQTLs was 5.28 times less than that of the initial QTLs. Thirty-three MQTLs overlapped the marker trait associations (MTAs) previously reported in genome-wide association studies (GWAS) for flag leaf traits in wheat. A total of 2262 candidate genes for flag leaf size, which were involved in the peroxisome, basal transcription factor, and tyrosine metabolism pathways were identified in MQTL regions by the in silico transcriptome assessment. Of these, the expression analysis of the available genes revealed that 134 genes with > 2 transcripts per million (TPM) were highly and specifically expressed in the leaf. These candidate genes could be critical to affect flag leaf size in wheat.ConclusionsThe findings will make further insight into the genetic determinants of flag leaf size and provide some reliable MQTLs and putative candidate genes for the genetic improvement of flag leaf size in wheat.

Delineating meta-quantitative trait loci for anthracnose resistance in common bean (Phaseolus vulgaris L.).

Anthracnose, caused by the fungus Colletotrichum lindemuthianum, is one of the devastating disease affecting common bean production and productivity worldwide. Several quantitative trait loci (QTLs) for anthracnose resistance have been identified. In order to make use of these QTLs in common bean breeding programs, a detailed meta-QTL (MQTL) analysis has been conducted. For the MQTL analysis, 92 QTLs related to anthracnose disease reported in 18 different earlier studies involving 16 mapping populations were compiled and projected on to the consensus map. This meta-analysis led to the identification of 11 MQTLs (each involving QTLs from at least two different studies) on 06 bean chromosomes and 10 QTL hotspots each involving multiple QTLs from an individual study on 07 chromosomes. The confidence interval (CI) of the identified MQTLs was found 3.51 times lower than the CI of initial QTLs. Marker-trait associations (MTAs) reported in published genome-wide association studies (GWAS) were used to validate nine of the 11 identified MQTLs, with MQTL4.1 overlapping with as many as 40 MTAs. Functional annotation of the 11 MQTL regions revealed 1,251 genes including several R genes (such as those encoding for NBS-LRR domain-containing proteins, protein kinases, etc.) and other defense related genes. The MQTLs, QTL hotspots and the potential candidate genes identified during the present study will prove useful in common bean marker-assisted breeding programs and in basic studies involving fine mapping and cloning of genomic regions associated with anthracnose resistance in common beans.

Meta-QTL analysis explores the key genes, especially hormone related genes, involved in the regulation of grain water content and grain dehydration rate in maize

BackgroundLow grain water content (GWC) at harvest of maize (Zea mays L.) is essential for mechanical harvesting, transportation and storage. Grain drying rate (GDR) is a key determinant of GWC. Many quantitative trait locus (QTLs) related to GDR and GWC have been reported, however, the confidence interval (CI) of these QTLs are too large and few QTLs has been fine-mapped or even been cloned. Meta-QTL (MQTL) analysis is an effective method to integrate QTLs information in independent populations, which helps to understand the genetic structure of quantitative traits.ResultsIn this study, MQTL analysis was performed using 282 QTLs from 25 experiments related GDR and GWC. Totally, 11 and 34 MQTLs were found to be associated with GDR and GWC, respectively. The average CI of GDR and GWC MQTLs was 24.44 and 22.13 cM which reduced the 57 and 65% compared to the average QTL interval for initial GDR and GWC QTL, respectively. Finally, 1494 and 5011 candidate genes related to GDR and GWC were identified in MQTL intervals, respectively. Among these genes, there are 48 genes related to hormone metabolism.ConclusionsOur studies combined traditional QTL analyses, genome-wide association study and RNA-seq to analysis major locus for regulating GWC in maize.

Unravelling consensus genomic regions associated with quality traits in wheat using meta-analysis of quantitative trait loci.

Meta-analysis in wheat for three major quality traits identified 110 meta-QTL (MQTL) with reduced confidence interval (CI). Five GWAS validated MQTL (viz., 1A.1, 1B.2, 3B.4, 5B.2, and 6B.2), each involving more than 20 initial QTL and reduced CI (95%) (< 2cM), were selected for quality breeding programmes. Functional characterization including candidate gene mining and expression analysis discovered 44 high confidence candidate genes associated with quality traits. A meta-analysis of quantitative trait loci (QTL) associated with dough rheology properties, nutritional traits, and processing quality traits was conducted in wheat. For this purpose, as many as2458 QTL were collected from 50 interval mapping studies published during 2013-2020. Of the total QTL, 1126 QTL were projected onto the consensus map saturated with 249,603 markers which led to the identification of 110 meta-QTL (MQTL). These MQTL exhibited an 18.84-fold reduction in the average CI compared to the average CI of the initial QTL (ranging from 14.87 to 95.55cM with an average of 40.35cM). Of the 110, 108 MQTL were physically anchored to the wheat reference genome, including 51 MQTL verified with marker-trait associations (MTAs) reported from earlier genome-wide association studies. Candidate gene (CG) mining allowed the identification of 2533 unique gene models from the MQTL regions. In-silico expression analysis discovered 439 differentially expressed gene models with > 2 transcripts per million expressions in grains and related tissues, which also included 44 high-confidence CGs involved in the various cellular and biochemical processes related to quality traits. Nine functionally characterized wheat genes associated with grain protein content, high-molecular-weight glutenin, and starch synthase enzymes were also found to be co-localized with some of the MQTL. Synteny analysis between wheat and rice MQTL regions identified 23 wheat MQTL syntenic to 16 rice MQTL associated with quality traits. Furthermore, 64 wheat orthologues of 30 known rice genes were detected in 44 MQTL regions. Markers flanking the MQTL identified in the present study can be used for marker-assisted breeding and as fixed effects in the genomic selection models for improving the prediction accuracy during quality breeding. Wheat orthologues of rice genes and other CGs available from MQTLs can be promising targets for further functional validation and to better understand the molecular mechanism underlying the quality traits in wheat.

Major Genomic Regions for Wheat Grain Weight as Revealed by QTL Linkage Mapping and Meta-Analysis.

Grain weight is a key determinant for grain yield potential in wheat, which is highly governed by a type of quantitative genetic basis. The identification of major quantitative trait locus (QTL) and functional genes are urgently required for molecular improvements in wheat grain yield. In this study, major genomic regions and putative candidate genes for thousand grain weight (TGW) were revealed by integrative approaches with QTL linkage mapping, meta-analysis and transcriptome evaluation. Forty-five TGW QTLs were detected using a set of recombinant inbred lines, explaining 1.76-12.87% of the phenotypic variation. Of these, ten stable QTLs were identified across more than four environments. Meta-QTL (MQTL) analysis were performed on 394 initial TGW QTLs available from previous studies and the present study, where 274 loci were finally refined into 67 MQTLs. The average confidence interval of these MQTLs was 3.73-fold less than that of initial QTLs. A total of 134 putative candidate genes were mined within MQTL regions by combined analysis of transcriptomic and omics data. Some key putative candidate genes similar to those reported early for grain development and grain weight formation were further discussed. This finding will provide a better understanding of the genetic determinants of TGW and will be useful for marker-assisted selection of high yield in wheat breeding.

Fine Mapping of a New Major QTL-qGLS8 for Gray Leaf Spot Resistance in Maize.

Gray leaf spot (GLS), caused by different species of Cercospora, is a fungal, non-soil-borne disease that causes serious reductions in maize yield worldwide. The identification of major quantitative trait loci (QTLs) for GLS resistance in maize is essential for developing marker-assisted selection strategies in maize breeding. Previous research found a significant difference (P < 0.01) in GLS resistance between T32 (highly resistant) and J51 (highly susceptible) genotypes of maize. Initial QTL analysis was conducted in an F2 : 3 population of 189 individuals utilizing genetic maps that were constructed using 181 simple sequence repeat (SSR) markers. One QTL (qGLS8) was detected, defined by the markers umc1130 and umc2354 in three environments. The qGLS8 QTL detected in the initial analysis was located in a 51.96-Mb genomic region of chromosome 8 and explained 7.89–14.71% of the phenotypic variation in GLS resistance in different environments. We also developed a near isogenic line (NIL) BC3F2 population with 1,468 individuals and a BC3F2-Micro population with 180 individuals for fine mapping. High-resolution genetic and physical maps were constructed using six newly developed SSRs. The QTL-qGLS8 was narrowed down to a 124-kb region flanked by the markers ym20 and ym51 and explained up to 17.46% of the phenotypic variation in GLS resistance. The QTL-qGLS8 contained seven candidate genes, such as an MYB-related transcription factor 24 and a C3H transcription factor 347), and long intergenic non-coding RNAs (lincRNAs). The present study aimed to provide a foundation for the identification of candidate genes for GLS resistance in maize.

Meta-Analysis of Qtl Associated With Starch Pasting Viscosity in Rice (Oryza Sativa L.)

Rapid Visco Analyzer (RVA) profiles are important traits for indicating rice quality, which are controlled by quantitative trait loci (QTL). In the past two decades, hundreds of QTL for rice RVA profiles have been detected, but without an integration study. Consistent QTL regions associated with rice RVA profiles by QTL meta-analysis with genetic information of 592 QTL for eight RVA profiles were studied. A total of 82 meta-QTL for RVA profiles were detected on 12 chromosomes. MQTL possessed narrower confidence intervals than the initial QTL and candidate genes were identified within each MQTL. These MQTL and candidate genes are beneficial for future marker-assisted selection gene cloning.&#x0D; Bangladesh J. Bot. 50(2): 269-276, 2021 (June)

QGLS1.02, a novel major locus for resistance to gray leaf spot in maize

Gray leaf spot (GLS), caused by the fungal pathogen Cercospora zeae-maydis, is one of the most serious foliar diseases of maize worldwide. In the present study, a population of recombinant inbred lines (RILs) derived from a gray leaf spot–resistant inbred line Qi319 and a susceptible line Ye478 was used to map quantitative trait loci (QTL) for GLS resistance. For initial QTL analysis, genetic maps containing 199 simple sequence repeat (SSR) markers were constructed. One major QTL, qGLS1.02, was consistently detected between SSR markers umc2614 and bnlg1803, and explained 6.86–36.24% of the phenotypic variation in GLS resistance. The chromosome segment substitution lines harboring the qGLS1.02 from Qi319 were more resistant to GLS (P < 0.01). Using a recombinant-derived progeny test strategy, the interval containing qGLS1.02 was narrowed to ~ 314 kb according to the B73 (RefGen_v4 genome) including 12 candidate genes; only six of them have been annotated functionally.

Consensus map integration and QTL meta-analysis narrowed a locus for yield traits to 0.7\u2009cM and refined a region for late leaf spot resistance traits to 0.38\u2009cM on linkage group A05 in peanut (Arachis hypogaea L.)

BackgroundMany large-effect quantitative trait loci (QTLs) for yield and disease resistance related traits have been identified in different mapping populations of peanut (Arachis hypogaea L.) under multiple environments. However, only a limited number of QTLs have been used in marker-assisted selection (MAS) because of unfavorable epistatic interactions between QTLs in different genetic backgrounds. Thus, it is essential to identify consensus QTLs across different environments and genetic backgrounds for use in MAS. Here, we used QTL meta-analysis to identify a set of consensus QTLs for yield and disease resistance related traits in peanut.ResultsA new integrated consensus genetic map with 5874 loci was constructed. The map comprised 20 linkage groups (LGs) and was up to a total length of 2918.62 cM with average marker density of 2.01 loci per centimorgan (cM). A total of 292 initial QTLs were projected on the new consensus map, and 40 meta-QTLs (MQTLs) for yield and disease resistance related traits were detected on four LGs. The genetic intervals of these consensus MQTLs varied from 0.20 cM to 7.4 cM, which is narrower than the genetic intervals of the initial QTLs, meaning they may be suitable for use in MAS. Importantly, a region of the map that previously co-localized multiple major QTLs for pod traits was narrowed from 3.7 cM to 0.7 cM using an overlap region of four MQTLs for yield related traits on LG A05, which corresponds to a physical region of about 630.3 kb on the A05 pseudomolecule of peanut, including 38 annotated candidate genes (54 transcripts) related to catalytic activity and metabolic process. Additionally, one major MQTL for late leaf spot (LLS) was identified in a region of about 0.38 cM. BLAST searches identified 26 candidate genes (30 different transcripts) in this region, some of which were annotated as related to regulation of disease resistance in different plant species.ConclusionsCombined with the high-density marker consensus map, all the detected MQTLs could be useful in MAS. The biological functions of the 64 candidate genes should be validated to unravel the molecular mechanisms of yield and disease resistance in peanut.

QTL Mapping in Three Connected Populations Reveals a Set of Consensus Genomic Regions for Low Temperature Germination Ability in Zea mays L.

Improving seed vigor in response to cold stress is an important breeding objective in maize that allows early sowing. Using two cold tolerant inbred lines 220 and P9-10 and two susceptible lines Y1518 and PH4CV, three connected F2:3 populations were generated for detecting quantitative trait locus (QTL) related to seed low-temperature germination ability. At 10°C, two germination traits (emergence rate and germination index) were collected from a sand bed and three seedling traits (seedling root length, shoot length, and total length) were extracted from paper rolls. Significant correlations were found among all traits in all populations. Via single-population analysis, 43 QTL were detected with explained phenotypic variance of 0.62%∼39.44%. Seventeen QTL explained more than 10% phenotypic variance; of them sixteen (94.12%) inherited favorable alleles from the tolerant lines. After constructing a consensus map, three meta-QTL (mQTL) were identified to include at least two initial QTL from different populations. mQTL1-1 included seven initial QTL for both germination and seedling traits; with three explaining more than 30% phenotypic variance. mQTL2-1 and mQTL9-1 covered two to three initial QTL. The favorable alleles of the QTL within these three mQTL regions were all inherited from the tolerant line 220 and P9-10. These results provided a basis for cloning of genes underlying the mQTL regions to uncover the molecular mechanisms of maize cold tolerance during germination.

Genome-wide QTL mapping results for regional DXA body composition and bone mineral density traits in pigs

Abstract. In a previous study, genome-wide mapping of quantitative trait loci (QTL) for five body composition traits, three bone mineral traits and live weight was performed using whole-body dual-energy X-ray absorptiometry (DXA) data. Since QTL for bone mineral traits were rare, the current study aimed to clarify whether the mapping results were influenced by the analysed body regions. Thus, the same material (551 pigs) and methods as in the whole-body QTL mapping study were used. However, for evaluation of the DXA scans, we manually defined two body regions: (i) from the last ribs to the pelvis (A) and (ii) including the pelvis and the hind limbs (P). Since live weight was not affected by the regional analysis, it was omitted from the QTL mapping design. Our results show an overall high consistency of mapping results especially for body composition traits. Two thirds of the initial whole-body QTL are significant for both A and P. Possible causes for the still low number of bone mineral QTL and the lower consistency found for these traits are discussed. For body composition traits, the data presented here show high genome-wide Pearson correlations between mapping results that are based on DXA scans with the time-saving whole-body standard setting and mapping results for DXA data that were obtained by time-consuming manual definition of the regions of interest. However, our results also suggest that whole-body or regional DXA scans might generally be less suitable for mapping of bone mineral traits in pigs. An analysis of single reference bones could be more useful.

A molecular genome scan to identify DNA segments associated with live weight in Japanese quail.

Japanese quail is an animal model in biological studies and also a commercial bird for eggs and meat production. This study was conducted to map quantitative trait loci (QTL) affecting live weight in Japanese quail. An F2 mapping population was developed by crossing two diverse lines (meat type and egg layer) of Japanese quail. A total number of 34 F1 and 422 F2 progeny were produced by reciprocal crossing of eight pairs of parental birds. All the birds from three generations were genotyped for SSR markers that were spread across all the autosomal linkage groups. The studied traits were hatching weight and live weights at 1-5weeks of age. QTL analysis was conducted by the regression interval mapping. Significant QTL were detected on chromosomes 1, 2, 3 (chromosome-wide significant) and 5 (genome-wide significant, P<0.05) for body weight. Although the additive effect of the detected QTL on chromosome 5 was significant, the dominance and imprinting effects were not significant. This finding is the first report of a genome-wide significant QTL associated with live weight in Japanese quail. Our results point out to candidate DNA regions affecting live weight, a trait of great economic relevance to the Japanese quail breeding. Although these results enhance our current knowledge about the genetic control of live weight in the Japanese quail, it should be noted that the initial QTL results from the experimental designs such as backcross or F2 cannot be applied directly to the breeding programs and require further validation within the commercial lines.

Genetic analysis of leaf morphology underlying the plant density response by QTL mapping in maize (Zea mays L.)

Maize yield increase has been strongly linked to plant population densities over time with changes in plant architecture, but the genetic basis for the plant architecture response to plant density is unknown, as is its stability across environments. To elucidate the genetic basis of the plant architecture response to density in maize, we mapped quantitative trait loci (QTLs) for leaf morphology-related traits in four sets of recombinant inbred line (RIL) populations under two plant density conditions. Forty-five QTLs for six traits were detected in both high and low plant density conditions. Thirty-seven QTLs were only detected when grown under high plant density, and 34 QTLs were only detected when grown under low plant density. Twenty-two meta-QTLs (mQTLs) were identified by meta-analysis, and mQTL1-1, mQTL3-2 and mQTL8 were identified when grown under high and low plant densities, with R 2 of some initial QTLs > 10 %, suggesting the mQTLs might be hot spots of the important QTLs for the related traits under planting density stress conditions. The results presented here provide useful information for further research and the marker-assisted selection of varieties targeting increased plant density and will help to reveal the molecular mechanisms related to leaf morphology in response to density.

Dissection of the genetic architecture for grain quality‐related traits in three RIL populations of maize (Zea mays L.)

AbstractTo manipulate the composition of the maize kernel to meet future needs, an understanding of the molecular regulation of kernel quality‐related traits is required. In this study, the quantitative trait loci (QTL) for the concentrations of grain protein, starch and oil were identified using three sets of RIL populations in three environments. The genetic maps and the initial QTL were integrated using meta‐analyses. A total of 38 QTL were identified, including 15 in population 1, 12 in population 2 and 11 in population 3. The individual effects ranged from 2.87% to 13.11% of the phenotypic variation, with seven QTL each contributing over 10%. One common QTL was found for the concentrations of grain protein and starch in bin 3.09 in the three environments and the three RIL populations. Of the 38 initial QTL, 22 were integrated into eight mQTL by meta‐analysis. mQTL3 and mQTL8 of the key mQTL in which the initial QTL displayed R2 &gt; 10% included six and three initial QTL for grain protein and starch concentrations from two or three populations, respectively. These results will provide useful information for marker‐assisted selection to improve the quality of the maize kernel.