Use of trial clustering to study QTL × environment effects for grain yield and related traits in maize

A population of 300 F_3:4 lines derived from the cross between maize inbred lines F2 and F252 was evaluated for testcross value in a large range of environmental conditions (11 different locations in 2 years: 1995 and 1996) in order to study (1) the magnitude of genotype × environment and (2) the stability of quantitative trait loci (QTL) effects. Several agronomic traits were measured: dry grain yield (DGY), kernel weight, average number of kernels per plant, silking date (SD) and grain moisture at harvest. A large genotype × environment interaction was found, particularly for DGY. A hierarchical classification of trials and an additive main effects and multiplicative interaction (AMMI) model were carried out. Both methods led to the conclusion that trials could be partitioned into three groups consistent with (1) the year of experiment and (2) the water availability (irrigated vs non-irrigated) for the trials sown in 1995. QTL detection was carried out for all the traits in the different groups of trials. Between 9 and 15 QTL were detected for each trait. QTL × group and QTL × trial effects were tested and proved significant for a large proportion of QTL. QTL detection was also performed on coordinates on the first two principal components (PC) of the AMMI model. PC QTL were generally detected in areas where QTL × group and QTL × trial interactions were significant. A region located on chromosome 8 near an SD QTL seemed to play a key role in DGY stability. Our results confirm the key role of water availability and flowering earliness on grain yield stability in maize.     

QTL for yield in bioenergy Populus: identifying G×E interactions from growth at three contrasting sites

Populus is a genus of fast growing trees that may be suitable as a bioenergy crop grown in short rotation, but understanding the genetic nature of yield and genotype interactions with the environment is critical in developing new high-yield genotypes for wide-scale planting. In the present study, 210 genotypes from an F₂ population (Family 331; POP1) derived from a cross between Populus trichocarpa 93-968 and P. deltoides ILL-129 were grown in southern UK, central France and northern Italy. The performance of POP1, based upon first- and second-year main stem traits and biomass production, improved from northern to southern Europe. Trees at the Italian site produced the highest mean biomass ranging from 0.77 to 18.06 oven-dried tonnes (ODT) ha⁻¹ year⁻¹, and the UK site produced the lowest mean biomass ranging from 0.18 to 10.31 ODT ha⁻¹ year⁻¹. Significant genotype × environment interactions were seen despite heritability values across sites being moderate to high. Using a pseudo-testcross analysis, 37 quantitative trait loci (QTL) were identified for the maternal parent and 45 for the paternal parent for eight stem and biomass traits across the three sites. High genetic correlations between traits suggested that collocating QTL could be inferred as a single pleiotropic QTL, reducing the number of unique QTL to 23 and 24 for the maternal and paternal parent, respectively. Additive genetic effects were seen to differ significantly for eight QTL on the maternal map and 20 on the paternal map across sites. An additive main effects and multiplicative interaction analysis was carried out to obtain stability parameters for each trait. These parameters were mapped as QTL, and collocation to trait QTL was accessed. Two of the eight stability QTL collocate to trait QTL on the maternal map, and 8 of the 20 stability QTL collocate to trait QTL on the paternal map, suggesting that a regulatory gene model is prevalent over an allele sensitivity model for stem trait stability across these environments.     

Analysis of QTL×environment interaction for yield components and plant height in rice

 An F₂ and two equivalent F₃ populations of an indica-indica cross of rice, Tesanai 2/CB, were constructed and grown in different environments. The identification of quantitative trait loci (QTL) for yield components and plant height and an analysis of QTL×environment interaction were conducted for three trials. Interval mapping of QTL for eight traits was employed with a threshold of LOD=2 using the computer package MAPMAKER/QTL. A total of 44 QTL were detected in 18 intervals of nine chromosomes, including 3 for the number of panicles (NP), 5 for the number of filled grains (NFG), 6 for total number of spikelets (TNS), 3 for spikelet fertility (SF), 7 for 1000-grain weight (TGWT), 5 for grain weight per plant (GWT), 8 for plant height (PH) and 7 for panicle length (PL). The numbers of QTL detected in two or three trials were 1 for NP, 1 for NFG, 1 for TNS, none for SF, 4 for TGWT, 3 for GWT, 2 for PH and 5 for PL, making a total of 17. When a QTL was detected in more than one trial the direction and magnitude of its additive effect, the dominance effect and the degree of dominance were generally in good agreement. In all three trials, QTL were frequently detected for related traits in the same intervals. The directions of additive effect of QTL for related traits in a given interval were in agreement with few exceptions, no matter whether they were detected in the same trial or not. This result suggested that pleiotropism rather than close linkage of different QTL was the major reason why QTL for different traits were frequently detected in the same intervals. When gene pleiotropism was considered, 23 of the 29 QTL for yield and its components and 9 of the 15 QTL for plant stature were detected in more than one trial. This indicated that the detection of chromosomal segments harboring QTL was hardly affected by environmental factors. Received: 30 January 1997 / Accepted: 21 March 1997     

QTL mapping for grain filling rate and yield-related traits in RILs of the Chinese winter wheat population Heshangmai × Yu8679

A set of 142 winter wheat recombinant inbred lines (RILs) deriving from the cross Heshangmai x Yu8679 were tried in four ecological environments during the seasons 2006 and 2007. Nine agronomic traits comprising mean grain filling rate (GFR(mean)), maximum grain filling rate (GFR(max)), grain filling duration (GFD), grain number per ear (GNE), grain weight per ear (GWE), flowering time (FT), maturation time (MT), plant height (PHT) and thousand grain weight (TGW) were evaluated in Beijing (2006 and 2007), Chengdu (2007) and Hefei (2007). A genetic map comprising 173 SSR markers and two EST markers was generated. Based on the genetic map and phenotypic data, quantitative trait loci (QTL) were mapped for these agronomic traits. A total of 99 putative QTLs were identified for the nine traits over four environments except GFD, PHT and MT, measured in two environments (BJ07 and CD07), respectively. Of the QTL detected, 17 for GFR(mean), 16 for GFR(max), 21 for TGW and 10 for GWE involving the chromosomes 1A, 1B, 2A, 2D, 3A, 3B, 3D, 4A, 4D, 5A, 5B, 6D and 7D were identified. Moreover, 13 genomic regions showing pleiotropic effects were detected in chromosomes 1A, 1B, 1D, 2A, 2B, 2D, 3A, 3B, 4B, 4D, 5B, 6D and 7D; these QTL revealing pleiotropic effects may be informative for a better understanding of the genetic basis of grain filling rate and other yield-related traits, and represent potential targets for multi-trait marker aided selection in wheat.     

Mapping QTL associated with photoperiod sensitivity and assessing the importance of QTL×environment interaction for flowering time in maize

Background

An understanding of the genetic determinism of photoperiod response of flowering is a prerequisite for the successful exchange of germplasm across different latitudes. In order to contribute to resolve the genetic basis of photoperiod sensitivity in maize, a set of 201 recombinant inbred lines (RIL), derived from a temperate and tropical inbred line cross were evaluated in 5 field trials spread in short- and long-day environments.

Methodology/Principal Findings

Firstly, QTL analyses for flowering time and photoperiod sensitivity in maize were conducted in individual photoperiod environments separately, and then, the total genetic effect was partitioned into additive effect (A) and additive-by-environment interaction effect (AE) by using a mixed-model-based composite interval mapping (MCIM) method.

Conclusions/Significance

Seven putative QTL were found associated with DPS thermal time based on the data estimated in individual environments. Nine putative QTL were found associated with DPS thermal time across environments and six of them showed significant QTL×enviroment (QE) interactions. Three QTL for photoperiod sensitivity were identified on chromosome 4, 9 and 10, which had the similar position to QTL for DPS thermal time in the two long-day environment. The major photoperiod sensitive loci qDPS10 responded to both short and long-day photoperiod environments and had opposite effects in different photoperiod environment. The QTL qDPS3, which had the greatest additive effect exclusively in the short-day environment, were photoperiod independent and should be classified in autonomous promotion pathway.     

QTL for fibre-related traits in grain × sweet sorghum as a tool for the enhancement of sorghum as a biomass crop

Compared to maize and temperate grasses, sorghum has received less attention in terms of improving cell wall components. The objectives of this study were to identify quantitative trait loci (QTL) with main effects, epistatic and pleiotropic effects along with QTL × environment (QE) interactions controlling fibre-related traits in sorghum. Neutral detergent fibre (NDF), acid detergent fibre (ADF), acid detergent lignin (ADL), cellulose, hemicellulose, fresh leaf mass, stripped stalk mass, dry stalk mass, fresh biomass and dry biomass were analysed from a population of 188 grain × sweet sorghum recombinant inbred lines. A genetic map consisting of 157 DNA markers was constructed, and QTL were detected using composite interval mapping (CIM). CIM detected more than 5 additive QTL per trait explaining 7.1–24.7% of the phenotypic variation. Abundant co-localization of these QTL was observed across all chromosomes, and the highest cluster was identified on chromosome 6. Searching for candidate genes using the confidence interval of our QTL clusters reveals that these clusters might comprise a set of genes that are tightly linked. Some QTL showed multiple effects; however, the allele for each trait was favouring the parent with the increasing effect. QE interactions were observed for QTL showing multiple effects. Additive × additive interaction was observed for 7 out of 10 traits, indicating the importance of epistatic analysis. However, the phenotypic variation explained by digenic interactions was lower compared to the individual QTL. Our results indicate that various genetic components contribute to fibre-related traits and should be considered during the enhancement of sorghum for lignocellulosic biomass.     

Mapping of QTL for Fusarium head blight resistance and morphological and developmental traits in three backcross populations derived from Triticum dicoccum?×?Triticum durum

Breeding for resistance to Fusarium head blight (FHB) in durum wheat continues to be hindered by the lack of effective resistance sources. Only limited information is available on resistance QTL for FHB in tetraploid wheat. In this study, resistance to FHB of a Triticum dicoccum line in the background of three Austrian T. durum cultivars was genetically characterized. Three populations of BC₁F₄-derived RILs were developed from crosses between the resistant donor line T. dicoccum-161 and the Austrian T. durum recipient varieties DS-131621, Floradur and Helidur. About 130 BC₁F₄-derived lines per population were evaluated for FHB response using artificial spray inoculation in four field experiments during two seasons. Lines were genetically fingerprinted using SSR and AFLP markers. Genomic regions on chromosomes 3B, 4B, 6A, 6B and 7B were significantly associated with FHB severity. FHB resistance QTL on 6B and 7B were identified in two populations and a resistance QTL on 4B appeared in three populations. The alleles that enhanced FHB resistance were derived from the T. dicoccum parent, except for the QTL on chromosome 3B. All QTL except the QTL on 6A mapped to genomic regions where QTL for FHB have previously been reported in hexaploid wheat. QTL on 3B and 6B coincided with Fhb1 and Fhb2, respectively. This implies that tetraploid and hexaploid wheat share common genomic regions associated with FHB resistance. QTL for FHB resistance on 4B co-located with a major QTL for plant height and mapped at the position of the Rht-B1 gene, while QTL on 7B overlapped with QTL for flowering time.     

Behaviour of a ‘sticky-desynaptic’ mutant in pearl millet

In the progeny after selfing of a normally open pollinated variety (L.S. 326-3) of pearl millet (Pennisetum americanum (L.) Leeke, n=7) one plant exhibited desynapsis, chromosome stickiness and high sterility. Meiosis was normal until diplotene. Thereafter, it was characterized by dissociation of bivalents into univalents and formation of nonspecific congregations of chromosomes at diakinesis, shrinkage of cytoplasm and occurrence of unoriented sticky chromatin masses at metaphase I, relaxation of stickiness, unbalanced chromosome numbers at the poles and laggards at anaphase I, and presence of other irregularities in subsequent stages. Meiosis was completed. Male and female sterility was high. This meiotic mutant thus has multiple effects and is inherited as a monogenic recessive and designated as st.     

Mapping QTLs with epistatic effects and QTL×environment interactions for plant height using a doubled haploid population in cultivated wheat

Quantitative trait loci (QTLs) for plant height in wheat (Triticum aestivum L.) were studied using a set of 168 doubled haploid (DH) lines, which were derived from the cross Huapei 3/Yumai 57. A genetic linkage map was constructed using 283 SSR and 22 EST-SSR markers. The DH population and the parents were evaluated for wheat plant height in 2005 and 2006 in Tai'an and 2006 in Suzhou. QTL analyses were performed using the software of QTLNetwork version 2.0 based on the mixed linear model. Four additive QTLs and five pairs of epistatic effects were detected, which were distributed on chromosomes 3A, 4B, 4D, 5A, 6A, 7B, and 7D. Among them, three additive QTLs and three pairs of epistatic QTLs showed QTLxenvironment interactions (QEs). Two major QTLs, QphAB and Qph4D, which accounted for 14.51 % and 20.22% of the phenotypic variation, were located similar to the reported locations of the dwarfing genes Rhtl and Rht2, respectively. The Qph3A-2 with additive effect was not reported in previous linkage mapping studies. The total QTL ef fects detected for the plant height explained 85.04% of the phenotypic variation, with additive effects 46.07%, epistatic effects 19.89%, and QEs 19.09%. The results showed that both additive effects and epistatic effects were important genetic bases of wheat plant height, which were subjected to environmental modifications, and caused dramatic changes in phenotypic effects. The information obtained in this study will be useful for manipulating the QTLs for wheat plant height by molecular marker-assisted selection (MAS).     

Mapping QTLs with epistatic effects and QTL×environment interactions for plant height using a doubled haploid population in cultivated wheat

Quantitative trait loci (QTLs) for plant height in wheat (Triticum aestivum L.) were studied using a set of 168 doubled haploid (DH) lines, which were derived from the cross Huapei 3/Yumai 57. A genetic linkage map was constructed using 283 SSR and 22 EST-SSR markers. The DH population and the parents were evaluated for wheat plant height in 2005 and 2006 in Tai’an and 2006 in Suzhou. QTL analyses were performed using the software of QTLNetwork version 2.0 based on the mixed linear model. Four additive QTLs and five pairs of epistatic effects were detected, which were distributed on chromosomes 3A, 4B, 4D, 5A, 6A, 7B, and 7D. Among them, three additive QTLs and three pairs of epistatic QTLs showed QTL×environment interactions (QEs). Two major QTLs, Qph4B and Qph4D, which accounted for 14.51% and 20.22% of the phenotypic variation, were located similar to the reported locations of the dwarfing genes Rht1 and Rht2, respectively. The Qph3A-2 with additive effect was not reported in previous linkage mapping studies. The total QTL effects detected for the plant height explained 85.04% of the phenotypic variation, with additive effects 46.07%, epistatic effects 19.89%, and QEs 19.09%. The results showed that both additive effects and epistatic effects were important genetic bases of wheat plant height, which were subjected to environmental modifications, and caused dramatic changes in phenotypic effects. The information obtained in this study will be useful for manipulating the QTLs for wheat plant height by molecular marker-assisted selection (MAS).     

Mapping Isoflavone QTL with Main,Epistatic and QTL × Environment Effects in Recombinant Inbred Lines of Soybean

Soybean (Glycine max (L.) Merr.) isoflavone is important for human health and plant defense system. To identify novel quantitative trait loci (QTL) and epistatic QTL underlying isoflavone content in soybean, F_5:6, F_5:7 and F_5:8 populations of 130 recombinant inbred (RI) lines, derived from the cross of soybean cultivar ‘Zhong Dou 27′ (high isoflavone) and ‘Jiu Nong 20′ (low isoflavone), were analyzed with 95 new SSR markers. A new linkage map including 194 SSR markers and covering 2,312 cM with mean distance of about 12 cM between markers was constructed. Thirty four QTL for both individual and total seed isoflavone contents of soybean were identified. Six, seven, ten and eleven QTL were associated with daidzein (DZ), glycitein (GC), genistein (GT) and total isoflavone (TI), respectively. Of them 23 QTL were newly identified. The qTIF_1 between Satt423 and Satt569 shared the same marker Satt569 with qDZF_2, qGTF_1 and qTIF_2. The qGTD2_1 between Satt186 and Satt226 was detected in four environments and explained 3.41%-10.98% of the phenotypic variation. The qGTA2_1, overlapped with qGCA2_1 and detected in four environments, was close to the previously identified major QTL for GT, which were responsible for large a effects. QTL (qDZF_2, qGTF_1 and qTIF_2) between Satt144-Satt569 were either clustered or pleiotropic. The qGCM_1, qGTM_1 and qTIM_1 between Satt540-Sat_244 explained 2.02%–9.12% of the phenotypic variation over six environments. Moreover, the qGCE_1 overlapped with qGTE_1 and qTIE_1, the qTIH_2 overlapped with qGTH_1, qGCI_1 overlapped with qDZI_1, qTIL_1 overlapped with qGTL_1, and qTIO_1 overlapped with qGTO_1. In this study, some of unstable QTL were detected in different environments, which were due to weak expression of QTL, QTL by environment interaction in the opposite direction to a effects, and/or epistasis. The markers identified in multi-environments in this study could be applied in the selection of soybean cultivars for higher isoflavone content and in the map-based gene cloning.     

Mapping QTLs with epistatic effects and QTL×environment interactions by mixed linear model approaches

A new methodology based on mixed linear models was developed for mapping QTLs with digenic epistasis and QTL×environment (QE) interactions. Reliable estimates of QTL main effects (additive and epistasis effects) can be obtained by the maximum-likelihood estimation method, while QE interaction effects (additive×environment interaction and epistasis×environment interaction) can be predicted by the-best-linear-unbiased-prediction (BLUP) method. Likelihood ratio and t statistics were combined for testing hypotheses about QTL effects and QE interactions. Monte Carlo simulations were conducted for evaluating the unbiasedness, accuracy, and power for parameter estimation in QTL mapping. The results indicated that the mixed-model approaches could provide unbiased estimates for both positions and effects of QTLs, as well as unbiased predicted values for QE interactions. Additionally, the mixed-model approaches also showed high accuracy and power in mapping QTLs with epistatic effects and QE interactions. Based on the models and the methodology, a computer software program (QTLMapper version 1.0) was developed, which is suitable for interval mapping of QTLs with additive, additive×additive epistasis, and their environment interactions. Received: 23 October 1998 / Accepted: 11 May 1999     

Multiple-line cross QTL mapping for biomass yield and plant height in triticale (× Triticosecale Wittmack)

Key message

QTL mapping in multiple families identifies trait-specific and pleiotropic QTL for biomass yield and plant height in triticale.

Abstract

Triticale shows a broad genetic variation for biomass yield which is of interest for a range of purposes, including bioenergy. Plant height is a major contributor to biomass yield and in this study, we investigated the genetic architecture underlying biomass yield and plant height by multiple-line cross QTL mapping. We employed 647 doubled haploid lines from four mapping populations that have been evaluated in four environments and genotyped with 1710 DArT markers. Twelve QTL were identified for plant height and nine for biomass yield which cross-validated explained 59.6 and 38.2 % of the genotypic variance, respectively. A major QTL for both traits was identified on chromosome 5R which likely corresponds to the dominant dwarfing gene Ddw1. In addition, we detected epistatic QTL for plant height and biomass yield which, however, contributed only little to the genetic architecture of the traits. In conclusion, our results demonstrate the potential of genomic approaches for a knowledge-based improvement of biomass yield in triticale.     

Quantitative trait loci for yield and related traits in the wheat population Ning7840 × Clark

Grain yield and associated agronomic traits are important factors in wheat (Triticum aestivum L.) improvement. Knowledge regarding the number, genomic location, and effect of quantitative trait loci (QTL) would facilitate marker-assisted selection and the development of cultivars with desirable characteristics. Our objectives were to identify QTLs directly and indirectly affecting grain yield expression. A population of 132 F₁₂ recombinant inbred lines (RILs) was derived by single-seed descent from a cross between the Chinese facultative wheat Ning7840 and the US soft red winter wheat Clark. Phenotypic data were collected for 15 yield and other agronomic traits in the RILs and parental lines from three locations in Oklahoma from 2001 to 2003. Twenty-nine linkage groups, consisting of 363 AFLP and 47 SSR markers, were identified. Using composite interval mapping (CIM) analysis, 10, 16, 30, and 14 QTLs were detected for yield, yield components, plant adaptation (shattering and lodging resistance, heading date, and plant height), and spike morphology traits, respectively. The QTL effects ranged from 7 to 23%. Marker alleles from Clark were associated with a positive effect for the majority of QTLs for yield and yield components, but gene dispersion was the rule rather than the exception for this RIL population. Often, QTLs were detected in proximal positions for different traits. Consistent, co-localized QTLs were identified in linkage groups 1AL, 1B, 4B, 5A, 6A, and 7A, and less consistent but unique QTLs were identified on 2BL, 2BS, 2DL, and 6B. Results of this study provide a benchmark for future efforts on QTL identification for yield traits.     

Comparative mapping in F2∶3 and F6∶7 generations of quantitative trait loci for grain yield and yield components in maize

This study was conducted to compare maize quantitative trait loci (QTL) detection for grain yield and yield components in F₂₃ and F₆₇ recombinant inbred (RI) lines from the same population. One hundred and eighty-six F₆₇ RIs from a Mo17×H99 population were grown in a replicated field experiment and analyzed at 101 loci detected by restriction fragment length polymorphisms (RFLPs). Single-factor analysis of variance was conducted for each locus-trait combination to identify QTL. For grain yield, 6 QTL were detected accounting for 22% of the phenotypic variation. A total of 63 QTL were identified for the seven grain yield components with alleles from both parents contributing to increased trait values. Several genetic regions were associated with more than one trait, indicating possible linked and/or pleiotropic effects. In a comparison with 150 F₂₃ lines from the same population, the same genetic regions and parental effects were detected across generations despite being evaluated under diverse environmental conditions. Some of the QTL detected in the F₂₃ seem to be dissected into multiple, linked QTL in the F₆₇ generation, indicating better genetic resolution for QTL detection with RIs. Also, genetic effects at QTL are smaller in the F₆₇ generation for all traits.Abbreviations RFLPs Restriction fragment length polymorphisms - QTL quantitative trait loci - RIs recombinant inbreds Journal Paper no. J-16261 of the Iowa Agric and Home Economics Exp Stn Project no. 3134     

Association mapping, patterns of linkage disequilibrium and selection in the vicinity of the PHYTOCHROME C gene in pearl millet

Key message

Linkage analysis confirmed the association in the region of PHYC in pearl millet. The comparison of genes found in this region suggests that PHYC is the best candidate.

Abstract

Major efforts are currently underway to dissect the phenotype–genotype relationship in plants and animals using existing populations. This method exploits historical recombinations accumulated in these populations. However, linkage disequilibrium sometimes extends over a relatively long distance, particularly in genomic regions containing polymorphisms that have been targets for selection. In this case, many genes in the region could be statistically associated with the trait shaped by the selected polymorphism. Statistical analyses could help in identifying the best candidate genes into such a region where an association is found. In a previous study, we proposed that a fragment of the PHYTOCHROME C gene (PHYC) is associated with flowering time and morphological variations in pearl millet. In the present study, we first performed linkage analyses using three pearl millet F₂ families to confirm the presence of a QTL in the vicinity of PHYC. We then analyzed a wider genomic region of ~100 kb around PHYC to pinpoint the gene that best explains the association with the trait in this region. A panel of 90 pearl millet inbred lines was used to assess the association. We used a Markov chain Monte Carlo approach to compare 75 markers distributed along this 100-kb region. We found the best candidate markers on the PHYC gene. Signatures of selection in this region were assessed in an independent data set and pointed to the same gene. These results foster confidence in the likely role of PHYC in phenotypic variation and encourage the development of functional studies.     

Assessment of ICCV 2?×?JG 62 chickpea progenies shows sensitivity of reproduction to salt stress and reveals QTL for seed yield and yield components

Salinity is a complex abiotic stress and understanding the physiological and genetic basis of salinity tolerance is a prerequisite for improving existing crop cultivars. Experiments were undertaken using 126 recombinant inbred lines from a cross between JG 62 (tolerant) and ICCV 2 (sensitive) to characterize traits related to seed yield differences under saline conditions and to map quantitative trait loci (QTL). The population segregated for flowering time and entries were separated into ??early?? and ??late?? phenology groups to undertake the analysis. In both groups seed yield varied under salinity, with seed number being the most closely related trait to yield. In contrast, seed yield was not related to 100-seed weight or flowering time. Shoot dry weight was positively correlated with seed yield in the early entries only, but had no significant relationship with seed number. The higher sensitivity to salinity of the early entries was related both to a smaller biomass and lesser seed number under saline conditions. A QTL for seed yield under saline conditions was found in linkage group 3 in the late group, and a cluster of QTL for seed yield components in linkage group 6, including a QTL for seed number which explained 37% of the variation. In contrast, no QTL for seed yield was found in the early group, but a QTL for seed number under saline conditions was found. These data indicate that salinity tolerance traits are linked to the degree of earliness in chickpea. Tolerance is determined by the success of reproductive sites in both early and late entries, which relates in part to constitutive traits, and by the capacity of maintaining growth in early-flowering lines only. This is the first report of QTL for seed yield and seed number in chickpea exposed to salinity.     

Results of a whole genome scan targeting QTL for growth and carcass traits in a Piétrain × Large White intercross

We herein report the results of a whole genome scan performed in a Piétrain × Large White intercross counting 525 offspring to map QTL influencing economically important growth and carcass traits. We report experiment-wide significant lod scores (> 4.6 for meatiness and fat deposition on chromosome SSC2, and for average daily gain and carcass length on chromosome SSC7. Additional suggestive lod scores (> 3.3) for fat deposition are reported on chromosomes SSC1, SSC7 and SSC13. A significant dominance deviation was found for the QTL on SSC1, while the hypothesis of an additive QTL could not be rejected for the QTL on SSC7 and SSC13. No evidence for imprinted QTL could be found for QTL other than the one previously reported on SSC2.