Dissecting of the FHB resistance QTL on the short arm of wheat chromosome 2D using a comparative genomic approach: from QTL to candidate gene

Colinearity in gene content and order between rice and closely related cereal crops has been a powerful tool for gene identification. Using a comparative genomic approach, we have identified the rice genomic region syntenous to the region of the short arm of wheat chromosome 2D, on which quantitative trait loci (QTLs) for Fusarium head blight (FHB) resistance and for controlling accumulation of the mycotoxin deoxynivalenol (DON) are closely located. Utilizing markers known to reside near the FHB resistance QTL and data from several wheat genetic maps, we have limited the syntenous region to 6.8 Mb of the short arm of rice chromosome 4. From the 6.8-Mb sequence of rice chromosome 4, we found three putative rice genes that could have a role in detoxification of mycotoxins. DNA sequences of these putative rice genes were used in BLAST searches to identify wheat expressed sequence tags (ESTs) exhibiting significant similarity. Combined data from expression analysis and gene mapping of wheat homologues and results of analysis of DON accumulation using doubled haploid populations revealed that a putative gene for multidrug resistance-associated protein (MRP) is a possible candidate for the FHB resistance and/or DON accumulation controlling QTLs on wheat chromosome 2DS and can be used as a molecular marker to eliminate the susceptible allele when the Chinese wheat variety Sumai 3 is used as a resistance source. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Analysis of the benefits to wheat yield from assimilates stored prior to grain filling in a range of environments*

Grain yields of rainfed agriculture in Australia are often low and vary substantially from season to season. Assimilates stored prior to grain filling have been identified as important contributors to grain yield in such environments, but quantifying their benefit has been hampered by inadequate methods and large seasonal variability. APSIM-Nwheat is a crop system simulation model, consisting of modules that incorporate aspects of soil water, nitrogen (N), crop residues, crop growth and development. Model outputs were compared with detailed measurements of N fertilizer experiments on loamy soils at three locations in southern New South Wales, Australia. The field measurements allowed the routine for remobilization of assimilates stored prior to grain filling in the model to be tested for the first time and simulations showed close agreement with observed data. Analysing system components indicated that with increasing yield, both the observed and simulated absolute amount of remobilization generally increased while the relative contribution to grain yield decreased. The simulated relative contribution of assimilates stored prior to grain filling to grain yield also decreased with increasing availability of water after anthesis. The model, linked to long-term historical weather records was used to analyse yield benefits from assimilates stored prior to grain filling under rainfed conditions at a range of locations in the main wheat growing areas of Australia. Simulation results highlighted that in each of these locations assimilates stored prior to grain filling often contributed a significant proportion to grain yield. The simulated contribution of assimilates stored prior to grain filling to grain yield can amount to several tonnes per hectare, however, it varied substantially from 5–90% of grain yield depending on seasonal rainfall amount and distribution, N supply, crop growth and seasonal water use. High N application often reduced the proportion of water available after anthesis and decreased the relative contribution of remobilization to grain yield as long as grain yields increased, particularly on soils with greater water-holding capacity. Increasing the capacity or potential to accumulate pre-grain filling assimilates for later remobilization by 20% increased yields by a maximum of 12% in moderate seasons with terminal droughts, but had little effect in poor or very good seasons in which factors that affect the amount of carbohydrates stored rather than the storage capacity itself appeared to limit grain yield. These factors were, little growth due to water or N deficit in the weeks prior to and shortly after anthesis (when most of the assimilates accumulate for later remobilization), poor sink demand of grains due to low grain number as a result of little pre-anthesis growth or high photosynthetic rate during grain filling. Increasing the potential storage capacity for remobilization is expected to increase grain yield especially under conditions of terminal drought.     

QTL and candidate genes for fecundity in sows

Fecundity in pigs is a trait of major economic interest but low heritability. For the improvement of fecundity, genetic markers for selection are desirable and therefore, several searches for genetic variation influencing fecundity have been performed. The aim of this review is to compare and to evaluate all published QTL analyses and candidate gene approaches concerning reproductive traits in sows. For this purpose, we present a comprehensive cytogenetic map comprising 54 QTL and 11 candidate genes with influence on reproductive traits. The evaluation and comparison of the results showed similarities, but also marked differences among studies. Reasons for different results are multicausal and are due to differences between resource populations, number of evaluated animals, mating systems, measured phenotypical traits and environmental influences. We could show that chromosome 8 and to a lower extend chromosome 7 are the most important chromosomes with regard to reproductive traits in pigs. For further research, fine mapping of the identified QTL regions is necessary in order to confirm and to narrow the most likely chromosomal intervals. Although difficult to perform, an advance would be a standardization of the experimental setup in particular, in respect to the collection of phenotypic data. Furthermore, we suggest to publish the information on further identified QTL and candidate genes as comprehensive and accurate as possible in order to allow a more transparent comparison and collation of the results.     

Markers associated with a QTL for grain yield in wheat under drought

Drought is a major abiotic stress that adversely affects wheat production in many regions of the world. The objective of this study was to identify quantitative trait loci (QTL) controlling grain yield and yield components under reduced moisture. A cross between common wheat cultivars ‘Dharwar Dry’ (drought tolerant) and ‘Sitta’ was the source of one hundred twenty-seven recombinant inbred lines evaluated for two-seasons in a field under differing soil moisture regimes in Ciudad Obregon, Sonora, Mexico. An SSR/EST-STS marker map was constructed and a grain yield QTL on the proximal region of chromosome 4AL was found to have a significant impact on performance under reduced moisture. This region was associated with QTL for grain yield, grain fill rate, spike density, grains m⁻², biomass production, biomass production rate, and drought susceptibility index (DSI). Molecular markers associated with these traits explained 20, 33, 15, 23, 30, 26, and 41% of phenotypic variation, respectively on chromosome 4A. Microsatellite locus Xwmc89 was associated with all significant QTL covering a 7.7 centiMorgans (cM) region and generally explained the greatest proportion of phenotypic variation. The alleles associated with enhanced performance under drought stress were contributed by Dharwar Dry. Microsatellite marker wmc89 may be useful for marker assisted selection to enhance drought tolerance.     

Integration of QTL and bioinformatic tools to identify candidate genes for triglycerides in mice

To identify genetic loci influencing lipid levels, we performed quantitative trait loci (QTL) analysis between inbred mouse strains MRL/MpJ and SM/J, measuring triglyceride levels at 8 weeks of age in F2 mice fed a chow diet. We identified one significant QTL on chromosome (Chr) 15 and three suggestive QTL on Chrs 2, 7, and 17. We also carried out microarray analysis on the livers of parental strains of 282 F2 mice and used these data to find cis-regulated expression QTL. We then narrowed the list of candidate genes under significant QTL using a "toolbox" of bioinformatic resources, including haplotype analysis; parental strain comparison for gene expression differences and nonsynonymous coding single nucleotide polymorphisms (SNP); cis-regulated eQTL in livers of F2 mice; correlation between gene expression and phenotype; and conditioning of expression on the phenotype. We suggest Slc25a7 as a candidate gene for the Chr 7 QTL and, based on expression differences, five genes (Polr3 h, Cyp2d22, Cyp2d26, Tspo, and Ttll12) as candidate genes for Chr 15 QTL. This study shows how bioinformatics can be used effectively to reduce candidate gene lists for QTL related to complex traits.     

Genomic regions for yield and yield parameters in Chinese winter wheat (Triticum aestivum L.) genotypes tested under varying environments correspond to QTL in widely different wheat materials

Field trials with a population of 108 doubled haploid (DH) lines of bread wheat (Triticum aestivum L.) derived from a cross between the Chinese winter wheat cultivars CA9613 and H1488 were carried out at Beijing (China) in 2000/2001 and 2001/2002. In addition, a field trial and a pot experiment were carried out at the experimental field stations of Giessen University (Germany) in the vegetation periods 2004/2005 and 2006/2007. Phenotypic data for major agronomic yield-related traits, i.e. grain weight per ear (GWE), grain number per ear (GNE), plant height and thousand-grain weight (TGW), were recorded in all experiments. In addition, biomass weight per tiller and ear weight were evaluated in the two field trials at Beijing. Based on the phenotypic data and a genetic map comprising 168 SSR markers, an analysis of quantitative trait loci (QTL) was carried out for yield and yield parameters using the composite interval mapping (CIM) approach. A total of 30 QTL were detected for these traits across four environments. Five of these QTL located on chromosomes 1A, 1B, 2B, 2D and 7D exhibited pleiotropic effects. Such pleiotropic gene loci will be very useful for understanding the homologous/homeologous relationships among QTL and designing an appropriate marker-assisted breeding programme including multi-trait selection in order to accumulate (“pyramide”) favorable alleles at different genetic loci.     

Identification of candidate genes encoding an LDL-C QTL in baboons

Cardiovascular disease (CVD) is the leading cause of death in developed countries, and dyslipidemia is a major risk factor for CVD. We previously identified a cluster of quantitative trait loci (QTL) on baboon chromosome 11 for multiple, related quantitative traits for serum LDL-cholesterol (LDL-C). Here we report differentially regulated hepatic genes encoding an LDL-C QTL that influences LDL-C levels in baboons. We performed hepatic whole-genome expression profiling for LDL-C-discordant baboons fed a high-cholesterol, high-fat (HCHF) diet for seven weeks. We detected expression of 117 genes within the QTL 2-LOD support interval. Three genes were differentially expressed in low LDL-C responders and 8 in high LDL-C responders in response to a HCHF diet. Seven genes (ACVR1B, CALCOCO1, DGKA, ERBB3, KRT73, MYL6B, TENC1) showed discordant expression between low and high LDL-C responders. To prioritize candidate genes, we integrated miRNA and mRNA expression profiles using network tools and found that four candidates (ACVR1B, DGKA, ERBB3, TENC1) were miRNA targets and that the miRNAs were inversely expressed to the target genes. Candidate gene expression was validated using QRT-PCR and Western blotting. This study reveals candidate genes that influence variation in LDL-C in baboons and potential genetic mechanisms for further investigation.     

QTL mapping for yield and yield contributing traits in two mapping populations of bread wheat

In bread wheat, single-locus and two-locus QTL analyses were conducted for seven yield and yield contributing traits using two different mapping populations (P I and P II). Single-locus QTL analyses involved composite interval mapping (CIM) for individual traits and multiple-trait composite interval mapping (MCIM) for correlated yield traits to detect the pleiotropic QTLs. Two-locus analyses were conducted to detect main effect QTLs (M-QTLs), epistatic QTLs (E-QTLs) and QTL × environment interactions (QE and QQE). Only a solitary QTL for spikelets per spike was common between the above two populations. HomoeoQTLs were also detected, suggesting the presence of triplicate QTLs in bread wheat. Relatively fewer QTLs were detected in P I than in P II. This may be partly due to low density of marker loci on P I framework map (173) than in P II (521) and partly due to more divergent parents used for developing P II. Six QTLs were important which were pleiotropic/coincident involving more than one trait and were also consistent over environments. These QTLs could be utilized efficiently for marker assisted selection (MAS).     

Tests of candidate genes in breed cross populations for QTL mapping in livestock

In recent years, several F₂ crosses between outbred lines of livestock have been developed to identify quantitative trait loci (QTL). These populations are valuable for further genetic analysis, including positional candidate gene loci (CGL). Analysis of CGL in F₂ populations is, however, hindered by extensive between-breed linkage disequilibrium (LD). The objectives here were to develop and evaluate three tests for CGL in simulated F₂ breed-cross populations. 1) A standard association test, based on the fixed effect of CGL genotype. This test was significant for CGL at considerable distances from the QTL. 2) A marker-assisted association test, based on a test at the CGL of the fixed effect of CGL genotype in a breed-cross QTL interval mapping model. This removed the impact of between-breed LD, but was not powerful in detecting CGL closely linked to the QTL, unless the CGL was the QTL. 3) An F-drop test, comparing F ratios for a QTL at the CGL with and without the CGL included as fixed effect. It had low power to distinguish close from distant CGL. Power to distinguish two CGL within 10 cM from the QTL was limited and little improved by including QTL effects associated with markers to remove between-breed LD, although the power was greater when one of the CGL was the causative mutation. Therefore, while we conclude that candidate gene tests in QTL mapping populations must be interpreted with caution, we now have a clearer picture of the value of candidate gene tests in these populations.     

A intervarietal genetic map and QTL analysis for yield traits in wheat

A new genetic linkage map was constructed based on recombinant inbred lines (RILs) derived from the cross between the Chinese winter wheat (Triticum aestivum L.) varieties, Chuang 35050 and Shannong 483 (ChSh). The map included 381 loci on all the wheat chromosomes, which were composed of 167 SSR, 94 EST-SSR, 76 ISSR, 26 SRAP, 15 TRAP, and 3 Glu loci. This map covered 3636.7 cM with 1327.7 cM (36.5%), 1485.5 cM (40.9%), and 823.5 cM (22.6%) for A, B, and D genome, respectively, and contained 13 linkage gaps. Using the RILs and the map, we detected 46 putative QTLs on 12 chromosomes for grain yield (GY) per m², thousand-kernel weight (TKW), spike number (SN) per m², kernel number per spike (KNS), sterile spikelet number per spike (SSS), fertile spikelet number per spike (FSS), and total spikelet number per spike (TSS) in four environments. Each QTL explained 4.42–70.25% phenotypic variation. Four QTL cluster regions were detected on chromosomes 1D, 2A, 6B, and 7D. The most important QTL cluster was located on chromosome 7D near the markers of Xwmc31, Xgdm67, and Xgwm428, in which 8 QTLs for TKW, SN, SSS and FSS were observed with very high contributions (27.53–67.63%).     

BioMercator: integrating genetic maps and QTL towards discovery of candidate genes

SUMMARY: Breeding programs face the challenge of integrating information from genomics and from quantitative trait loci (QTL) analysis in order to identify genomic sequences controlling the variation of important traits. Despite the development of integrative databases, building a consensus map of genes, QTL and other loci gathered from multiple maps remains a manual and tedious task. Nevertheless, this is a critical step to reveal co-locations between genes and QTL. Another important matter is to determine whether QTL linked to same traits or related ones is detected in independent experiments and located in the same region, and represents a single locus or not. Statistical tools such as meta-analysis can be used to answer this question. BioMercator has been developed to automate map compilation and QTL meta-analysis, and to visualize co-locations between genes and QTL through a graphical interface. AVAILABILITY: Available upon request (http://moulon/~bioinfo/BioMercator/). Free of charge for academic use.     

Identification of candidate genes and mutations in QTL regions for immune responses in chicken

There are two categories of immune responses – innate and adaptive immunity – both having polygenic backgrounds and a significant environmental component. In our study, adaptive immunity was represented by the specific antibody response toward keyhole limpet hemocyanin (KLH); innate immunity was represented by natural antibodies toward lipopolysaccharide (LPS) and lipoteichoic acid (LTA). Defining genetic bases of immune responses leads from defining quantitative trait loci (QTL) toward a single mutation responsible for variation in the phenotypic trait. The goal of the reported study was to define candidate genes and mutations for the immune traits of interest in chicken by performing an association study of SNPs located in candidate genes defined in QTL regions. Candidate genes and SNPs in QTL regions were selected in silico. SNP association was based on a custom SNP panel, GoldenGate genotyping assay (Illumina) and two statistical models: random mixed model and CAR score. The most significant SNP for immune response toward KLH was located in the JMJD6 gene located on GGA18. Four SNPs in candidate genes FOXJ1 (GGA18), EPHB1 (GGA9), PTGER4 (GGAZ) and PRKCB (GGA14) showed association with natural antibodies for LPS. A single SNP in ITGB4 (GGA18) was associated with natural antibodies for LTA. All associated SNPs mentioned above showed additive effects.     

Detection of two major grain yield QTL in bread wheat (Triticum aestivum L.) under heat, drought and high yield potential environments

A large proportion of the worlds’ wheat growing regions suffers water and/or heat stress at some stage during the crop growth cycle. With few exceptions, there has been no utilisation of managed environments to screen mapping populations under repeatable abiotic stress conditions, such as the facilities developed by the International Wheat and Maize Improvement Centre (CIMMYT). Through careful management of irrigation and sowing date over three consecutive seasons, repeatable heat, drought and high yield potential conditions were imposed on the RAC875/Kukri doubled haploid population to identify genetic loci for grain yield, yield components and key morpho-physiological traits under these conditions. Two of the detected quantitative trait loci (QTL) were located on chromosome 3B and had a large effect on canopy temperature and grain yield, accounting for up to 22?% of the variance for these traits. The locus on chromosome arm 3BL was detected under all three treatments but had its largest effect under the heat stress conditions, with the RAC875 allele increasing grain yield by 131?kg?ha^?1 (or phenotypically, 7?% of treatment average). Only two of the eight yield QTL detected in the current study (including linkage groups 3A, 3D, 4D 5B and 7A) were previously detected in the RAC875/Kukri doubled haploid population; and there were also different yield components driving grain yield. A number of discussion points are raised to understand differences between the Mexican and southern Australian production environments and explain the lack of correlation between the datasets. The two key QTL detected on chromosome 3B in the present study are candidates for further genetic dissection and development of molecular markers.     

Mapping QTL for grain yield, yield components, and spike features in a doubled haploid population of bread wheat

A doubled haploid (DH) population derived from a cross between the Japanese cultivar 'Fukuho-kumogi' and the Israeli wheat line 'Oligoculm' was used to map genome regions involved in the expression of grain yield, yield components, and spike features in wheat (Triticum aestivum L). A total of 371 markers (RAPD, SSR, RFLP, AFLP, and two morphological traits) were used to construct the linkage map that covered 4190 cM of wheat genome including 28 linkage groups. The results of composite interval mapping for all studied traits showed that some of the quantitative trait loci (QTL) were stable over experiments conducted in 2004 and 2005. The major QTL located in the Hair-Xpsp2999 interval on chromosome 1A controlled the expression of grains/spike (R(2) = 12.9% in 2004 and 22.4% in 2005), grain weight/spike (R(2) = 21.4% in 2004 and 15.8% in 2005), and spike number (R(2) = 15.6% in 2004 and 5.4% in 2005). The QTL for grain yield located on chromosomes 6A, 6B, and 6D totally accounted for 27.2% and 31.7% of total variation in this trait in 2004 and 2005, respectively. Alleles inherited from 'Oligoculm' increased the length of spikes and had decreasing effects on spike number. According to the data obtained in 2005, locus Xgwm261 was associated with a highly significant spike length QTL (R(2) = 42.33%) and also the major QTL for spikelet compactness (R(2) = 26.1%).     

QTL study reveals candidate genes underlying host resistance in a Red Queen model system

Specific interactions of host and parasite genotypes can lead to balancing selection, maintaining genetic diversity within populations. In order to understand the drivers of such specific coevolution, it is necessary to identify the molecular underpinnings of these genotypic interactions. Here, we investigate the genetic basis of resistance in the crustacean host, Daphnia magna, to attachment and subsequent infection by the bacterial parasite, Pasteuria ramosa. We discover a single locus with Mendelian segregation (3:1 ratio) with resistance being dominant, which we call the F locus. We use QTL analysis and fine mapping to localize the F locus to a 28.8-kb region in the host genome, adjacent to a known resistance supergene. We compare the 28.8-kb region in the two QTL parents to identify differences between host genotypes that are resistant versus susceptible to attachment and infection by the parasite. We identify 13 genes in the region, from which we highlight eight biological candidates for the F locus, based on presence/absence polymorphisms and differential gene expression. The top candidates include a fucosyltransferase gene that is only present in one of the two QTL parents, as well as several Cladoceran-specific genes belonging to a large family that is represented in multiple locations of the host genome. Fucosyltransferases have been linked to resistance in previous studies of Daphnia–Pasteuria and other host–parasite systems, suggesting that P. ramosa spore attachment could be mediated by changes in glycan structures on D. magna cuticle proteins. The Cladoceran-specific candidate genes suggest a resistance strategy that relies on gene duplication. Our results add a new locus to a growing genetic model of resistance in the D. magna–P. ramosa system. The identified candidate genes will be used in future functional genetic studies, with the ultimate aim to test for cycles of allele frequencies in natural populations.     

Genome wide QTL mapping to identify candidate genes for carcass traits in Hanwoo (Korean Cattle)

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.     

A functional genomics approach to evaluate candidate genes located in a QTL interval for milk production traits on BTA6

The potential genetic and economic advantage of marker-assisted selection for enhanced production in dairy cattle has provided an impetus to conduct numerous genome scans in order to identify associations between DNA markers and future productive potential. One area of focus has been a quantitative trait locus on bovine chromosome 6 (BTA6) found to be associated with milk yield, milk protein and fat percentage, which has been subsequently fine-mapped to six positional candidate genes. Subsequent investigations have yet to resolve which of the potential positional candidate genes is responsible for the observed associations with productive performance. In this study, we analysed candidate gene expression and the effects of gene knockdown on expression of β- and κ-casein mRNA in a small interfering RNA transfected bovine in vitro mammosphere model. From our expression studies in vivo , we observed that four of the six candidates ( ABCG2 , SPP1 , PKD2 and LAP3 ) exhibited differential expression in bovine mammary tissue over the lactation cycle, but in vitro functional studies indicate that inhibition of only one gene, SPP1 , had a significant impact on milk protein gene expression. These data suggest that the gene product of SPP1 (also known as osteopontin) has a significant role in the modulation of milk protein gene expression. While these findings do not exclude other positional candidates from influencing lactation, they support the hypothesis that the gene product of SPP1 is a significant lactational regulatory molecule.     

Dissection of novel candidate genes for grain texture in Russian wheat varieties

Plant Molecular Biology - Wheat grain texture is a complex trait. The crucial role of puroindoline genes in grain texture determination was confirmed. New loci, highly associated with the trait,...