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Genetic dissection of quantitative trait loci specifying sedative/hypnotic sensitivity to ethanol: mapping with interval-specific congenic recombinant lines.

BACKGROUND: Linkage studies alone do not produce sufficient resolution to narrow the location of a quantitative trait locus (QTL) to a small-enough chromosomal region for gene identification. One solution to this problem is to use interval-specific congenic recombinant (ISCR) lines to narrow the chromosomal interval known to contain the QTL. In previous work, we mapped four QTLs for differential ethanol sensitivity in the inbred long-sleep (ILS) and inbred short-sleep (ISS) strains and generated reciprocal congenic strains in which each full QTL interval from ILS was bred onto the ISS background and vice versa. METHODS: ISCR lines were derived by identifying mice carrying recombination events in the congenic interval during backcrossing of the ISS.ILS.Lore congenics to ISS. Recombinant mice were backcrossed to ISS, and progeny carrying the ISCR chromosome were identified and tested to determine whether the ISCR region carried the donor Lore QTL. RESULTS: We developed multiple ISCR lines for each Lore QTL, in which the QTL interval was broken into a number of smaller intervals. For all four QTLs, we reduced the size of the interval, in one case to 3.7 cM. CONCLUSIONS: Use of ISCR lines can narrow each Lore candidate region to a few centimorgans. Such an interval size is conducive to brute-force approaches to identify candidate genes, entailing bioinformatics, gene expression, and DNA sequencing strategies.

Alcohol Drinking↗

Fine mapping of three quantitative trait loci for late blight resistance in tomato using near isogenic lines (NILs) and sub-NILs.

An earlier study identified quantitative trait loci (QTLs) lb4, lb5b, and lb11b for quantitative resistance to Phytophthora infestans (late blight) in a backcross population derived from crossing susceptible cultivated tomato ( Lycopersicon esculentum) with resistant L. hirsutum. The QTLs were located in intervals spanning 28-47 cM. Subsequently, near-isogenic lines (NILs) were developed for lb4, lb5b, and lb11b by marker-assisted backcrossing to L. esculentum. Sub-NILs containing overlapping L. hirsutum segments across each QTL region were selected and used to validate the QTL effects, fine-map QTLs, and evaluate potential linkage drag between resistance QTLs and QTLs for horticultural traits. The NILs and sub-NILs were evaluated for disease resistance and eight horticultural traits at three field locations. Resistance QTLs were detected in all three sets of NIL lines, confirming the BC(1) mapping results. Lb4 mapped near TG609, and between TG182 and CT194, on chromosome 4, a 6.9-cM interval; lb5b mapped to an 8.8-cM interval between TG69a and TG413 on chromosome 5, with the most likely position near TG23; and lb11b mapped to a 15.1-cM interval on chromosome 11 between TG194 and TG400, with the peak centered between CT182 and TG147. Most QTLs for horticultural traits were identified in intervals adjacent to those containing the late blight resistance QTLs. Fine mapping of these QTLs permits the use of marker-assisted selection for the precise introgression of L. hirsutum segments containing late blight resistance alleles separately from those containing deleterious alleles at horticulturally important QTLs.

Analysis of Variance↗

Molecular linkage map of Einkorn wheat: mapping of storage-protein and soft-glume genes and bread-making quality QTLs.

Two molecular maps of Triticum monococcum L were produced and integrated. The integrated map includes a total of 477 markers, 32 RFLPs, 438 AFLPs, one morphological (soft glume (Sog)) and six storage-protein markers, and covers 856 cM. The trait Sog with the recessive allele sog maps to linkage group 2S. Probably, this is the T. monococcum homologue of Tg and Tg2 in hexaploid and tetraploid wheats, respectively. Loci coding for seed storage proteins were allocated to chromosomes 1L (HMW GLU1,2 and Glu1), 1S (LMW GLU6,7, LMW GLU1-4, omega GLI1-4, gamma GLI5 and Gli-1) and 6L (alpha/beta GLI7-14). Parameters related to bread-making quality (SDS sedimentation volume, specific sedimentation volume (SSV) and total protein content) were studied in one of the two populations. A QTL that is consistently present across environments was detected for SDS sedimentation volume and for SSV. The position of the QTL on chromosome 1S was in close agreement with the map positions of storage-protein loci. A second QTL was mapped on chromosome 5. For protein content, two significant QTLs were mapped to linkage groups 1 and 5.

Bread↗

A linkage map of an F2 hybrid population of Antirrhinum majus and A. molle.

To increase the utility of Antirrhinum for genetic and evolutionary studies, we constructed a molecular linkage map for an interspecific hybrid A. majus x A. molle. An F(2) population (n = 92) was genotyped at a minimum of 243 individual loci. Although distorted transmission ratios were observed at marker loci throughout the genome, a mapping strategy based on a fixed framework of codominant markers allowed the loci to be placed into eight robust linkage groups consistent with the haploid chromosome number of Antirrhinum. The mapped loci included 164 protein-coding genes and a similar number of unknown sequences mapped as AFLP, RFLP, ISTR, and ISSR markers. Inclusion of sequences from mutant loci allowed provisional alignment of classical and molecular linkage groups. The total map length was 613 cM with an average interval of 2.5 cM, but most of the loci were aggregated into clusters reducing the effective distance between markers. Potential causes of transmission ratio distortion and its effects on map construction were investigated. This first molecular linkage map for Antirrhinum should facilitate further mapping of mutations, major QTL, and other coding sequences in this model genus.

Antirrhinum↗

Mapping of quantitative trait loci for lactation persistency traits in German Holstein dairy cattle.

A whole genome scan to map quantitative trait loci (QTL) for persistency of milk yield (PMY), persistency of fat yield (PFY), persistency of protein yield (PPY) and persistency of milk energy yield (PEY) was performed in a granddaughter design in the German Holstein dairy cattle population. The analysis included 16 paternal half-sib families with a total of 872 bulls. The analysis was carried out for the first lactation and for the first three lactations combined using univariate weighted multimarker regression. Controlling the false discovery rate across traits and data sets at a level of 0.15 and treating the four persistency traits as different traits revealed 27 significant QTL. A total of 12 chromosomes showed significant QTL effects on a chromosomewise basis. The DGAT1 effect was highly significant for PPY and protein yield. A haplotype analysis using results of previous studies of the same design revealed a co-segregation of various persistency QTL and QTL affecting health traits like dystocia and stillbirth and functional traits like non-return rate 90 and somatic cell score.

Animals↗

A genome scan for quantitative trait loci affecting growth-related traits in an F1 family of Asian seabass (Lates calcarifer).

BACKGROUND: Body weight and length are economically important traits in foodfish species influenced by quantitative trait loci (QTL) and environmental factors. It is usually difficult to dissect the genetic and environmental effects. Asian seabass (Lates calcarifer) is an important marine foodfish species with a compact genome (approximately 700 Mb). The recent construction of a first generation linkage map of Asian seabass with 240 microsatellites provides a good opportunity to determine the number and position of QTL, and the magnitude of QTL effects with a genome scan. RESULTS: We conducted a genome scan for QTL affecting body weight, standard length and condition factors in an F1 family containing 380 full-sib individuals from a breeding stock by using 97 microsatellites evenly covering 24 chromosomes. Interval mapping and multiple QTL model mapping detected five significant and 27 suggestive QTL on ten linkage groups (LGs). Among the five significant QTL detected, three (qBW2-a, qTL2-a and qSL2-a) controlling body weight, total and standard length respectively, were mapped on the same region near Lca287 on LG2, and explained 28.8, 58.9 and 59.7% of the phenotypic variance. The other two QTL affecting body weight, qBW2-b and qBW3, were located on LG2 and 3, and accounted for 6.4 and 8.8% of the phenotypic variance. Suggestive QTL associated with condition factors are located on six different LGs. CONCLUSION: This study presents the first example of QTL detection for growth-related traits in an F1 family of a marine foodfish species. The results presented here will enable further fine-mapping of these QTL for marker-assisted selection of the Asian seabass, eventually identifying individual genes responsible for growth-related traits.

Animals↗

A molecular genome scan analysis to identify chromosomal regions influencing economic traits in the pig. II. Meat and muscle composition.

Molecular genetic markers can be used to identify chromosomal regions that contain quantitative trait loci (QTL) that control meat quality and muscle composition traits in farm animals. To study this in pigs, a resource family was generated from a cross between two Berkshire grand sires and nine Yorkshire grand dams. A total of 525 F2 progeny from 65 matings of F1 parents were produced. Phenotypic data on 28 meat quality traits (drip loss, water holding capacity, firmness, color, marbling, percentage cholesterol, ultimate pH, fiber type, and several sensory panel and cooking traits) were collected on the F2 animals. Animals were genotyped for 125 microsatellite markers covering the entire genome. Least squares regression interval mapping was used for QTL detection. Significance thresholds were determined by permutation tests. A total of 60 QTL were detected at the 5% chromosome level for meat quality traits, on Chrs 1, 2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 17, 18, and X, of which 9 and 1 QTL were significant at the 5% and 1% genome-wise levels (on Chrs 1, 5, 12, 15, and 17), respectively.

Adipose Tissue↗

A quick method for computing approximate thresholds for quantitative trait loci detection.

This article proposes a quick method for computing approximate threshold levels that control the genome-wise type I error rate of tests for quantitative trait locus (QTL) detection in interval mapping (IM) and composite interval mapping (CIM). The procedure is completely general, allowing any population structure to be handled, e.g., BC(1), advanced backcross, F(2), and advanced intercross lines. Its main advantage is applicability in complex situations where no closed form approximate thresholds are available. Extensive simulations demonstrate that the method works well over a range of situations. Moreover, the method is computationally inexpensive and may thus be used as an alternative to permutation procedures. For given values of the likelihood-ratio (LR)-profile, computations involve just a few seconds on a Pentium PC. Computations are simple to perform, requiring only the values of the LR statistics (or LOD scores) of a QTL scan across the genome as input. For CIM, the window size and the position of cofactors are also needed. For the approximation to work well, it is suggested that scans be performed with a relatively small step size between 1 and 2 cM.

Biometry↗

Identification of QTL for growth- and grain yield-related traits in rice across nine locations of Asia.

Rice double-haploid (DH) lines of an indica and japonica cross were grown at nine different locations across four countries in Asia. Genotype-by-environment (G x E) interaction analysis for 11 growth- and grain yield-related traits in nine locations was estimated by AMMI analysis. Maximum G x E interaction was exhibited for fertility percentage number of spikelets and grain yield. Plant height was least affected by environment, and the AMMI model explained a total of 76.2% of the interaction effect. Mean environment was computed by averaging the nine environments and subsequently analyzed with other environments to map quantitative trait loci (QTL). QTL controlling the 11 traits were detected by interval analysis using mapmaker/qtl. A threshold LOD of >/=3.20 was used to identify significant QTL. A total of 126 QTL were identified for the 11 traits across nine locations. Thirty-four QTL common in more than one environment were identified on ten chromosomes. A maximum of 44 QTL were detected for panicle length, and the maximum number of common QTL were detected for days to heading detected. A single locus for plant height (RZ730-RG810) had QTL common in all ten environments, confirming AMMI results that QTL for plant height were affected the least by environment, indicating the stability of the trait. Two QTL were detected for grain yield and 19 for thousand-grain weight in all DH lines. The number of QTL per trait per location ranged from zero to four. Clustering of the QTL for different traits at the same marker intervals was observed for plant height, panicle number, panicle length and spikelet number suggesting that pleiotropism and or tight linkage of different traits could be the possible reason for the congruence of several QTL. The many QTL detected by the same marker interval across environments indicate that QTL for most traits are stable and not essentially affected by environmental factors.

Asia↗

A BAC-based physical map of the chicken genome.

A genome-wide physical map constructed with bacterial artificial chromosomes (BACs) is an essential component in linking phenotypic traits to the responsible genetic variation in the genomes of plants and animals. We have constructed a physical map of the chicken genome from 57,091 BACs (7.9-fold haploid genome coverage) by restriction fingerprint analysis using high-resolution polyacrylamide gel electrophoresis. The physical map consists of 2331 overlapping BAC contigs and is estimated to span 1510 Mb in physical length. BAC contigs were verified manually and by screening the BACs with 367 DNA markers. A total of 361 of the contigs have been anchored to the existing chicken genetic map. This map represents the first genome-wide, BAC-based physical map of the chicken genome. It provides a powerful platform for many areas of chicken genomics, including targeted marker development, fine mapping of genes and QTL alleles, positional cloning, analysis of avian genome organization and evolution, chicken-mammalian comparative genomics, and large-scale genome sequencing.

Animals↗

A predicted microsatellite map of the passerine genome based on chicken-passerine sequence similarity.

Abstract We present a predicted passerine genome map consisting of 196 microsatellite markers distributed across 25 chromosomes. The map was constructed by assigning chromosomal locations based on the sequence similarity between 550 publicly available passerine microsatellites and the draft chicken genome sequence published by the International Chicken Genome Sequencing Consortium. We compared this passerine microsatellite map with a recently published great reed warbler (Acrocephalus arundinaceus) linkage map derived from the segregation of marker alleles in a pedigree of a natural population. Twenty-four microsatellite markers were shared between the two maps, distributed across ten chromosomes. Synteny was maintained between the predicted passerine microsatellite map and the great reed warbler linkage map, confirming the validity and accuracy of our approach. Possible applications of the predicted passerine microsatellite map include genome mapping; quantitative trait locus (QTL) discovery; understanding heterozygosity-fitness correlations; investigating avian karyotype evolution; understanding microsatellite mutation processes; and for identifying loci conserved in multiple species, unlinked loci for use in genotyping sets and sex-linked markers.

Animals↗

Fine mapping of the FT1 locus for soybean flowering time using a residual heterozygous line derived from a recombinant inbred line.

Fine-mapping of loci related to complex quantitative traits is essential for map-based cloning. A residual heterozygous line (RHL) of soybean (Glycine max) derived from a recombinant inbred line (RIL) was used for fine-mapping FT1, which is a major quantitative trait locus (QTL) responsible for soybean flowering time. The residual heterozygous line RHL1-156 was selected from the RILs that were derived from two distantly related varieties, Misuzudaizu and Moshidou Gong 503. The genome of RHL1-156 contains a heterozygous segment (approximately 17 cM) surrounding the FT1 locus but is homozygous in other regions, including three other loci affecting flowering time. A large segregating population of 1,006 individuals derived by selfing of RHL1-156 included two homozygous genotypes for the nearest marker of FT1 whose flowering time differed by 26 days. No continuous range of phenotypes was observed, in contrast to the F2 population, suggesting that a single FT1 locus affected the flowering time in the RHL1-156 line. Linkage analysis revealed that the FT1 locus mapped as a single Mendelian factor between two tightly linked DNA markers, Satt365 and GM169, at distances of approximately 0.1 cM and 0.4 cM, respectively. Our results show that a RHL derived from RILs can be used to fine-map a QTL and that RHLs can be an efficient tool for a systematic fine-mapping of QTLs.

Breeding↗

[Initial identification of quantitative trait loci controlling resistance to banded leaf and sheath blight at elongating and heading date in maize].

The genetic linkage map was constructed with 146 SSR markers based on a maize population consisting of 229 F2 individuals from the cross R15 (resistant) x 478 (susceptible), covering 1666cM on a total of ten chromosomes, with an average interval length of 11.4 cM. The disease index from the population of 229 F24 lines were evaluated for BLSB resistance under artificial inoculation at elongating stage and heading date. With the method of composite interval mapping (CIM) described in QTL cartographer v2.0 procedure, 9 QTLs among of 17 QTLs were identified on 1, 2, 3, 4, 5, 6 and 10 chromosomes at Jointing Stage, accounting for 3.72% to 9.26% of the phenotypic variance. The other 10 QTLs were identified on 2, 3, 4, 5, 6, 8 and 9 chromosomes at elongating stage and heading date, accounting for 4.27% to 9.27% of the phenotypic variance. Among of them, two QTLs were detected at two stages, which were located between bnlgl600-umc1818 and umc1006-umc1723 on chromosome 2 and 6, respectively. The result indicated that the significant difference about QTL Controlling Resistance between the two stages had a close connection with Developing Stages, which was showed on the resistance of banded leaf and sheath blight in maize. So this result provided new information to the resistance breeding of maize.

Chromosome Mapping↗

Longevity and metabolism in Drosophila melanogaster: genetic correlations between life span and age-specific metabolic rate in populations artificially selected for long life.

We measured age-specific metabolic rates in 2861 individual Drosophila melanogaster adult males to determine how genetic variation in metabolism is related to life span. Using recombinant inbred (RI) lines derived from populations artificially selected for long life, resting metabolic rates were measured at 5, 16, 29, and 47 days posteclosion, while life spans were measured in the same genotypes in mixed-sex population cages and in single-sex vials. We observed much heritable variation between lines in age-specific metabolic rates, evidence for genotype x age interaction, and moderate to large heritabilities at all ages except the youngest. Four traits exhibit evidence of coordinate genetic control: day 16 and day 29 metabolic rates, life span in population cages, and life span in vials. Quantitative trait loci (QTL) for those traits map to the same locations on three major chromosomes, and additive genetic effects are all positively correlated. In contrast, metabolic rates at the youngest and oldest ages are unrelated to metabolic rates at other ages and to survival. We suggest that artificial selection for long life via delayed reproduction also selects for increased metabolism at intermediate ages. Contrary to predictions of the "rate of living" theory, we find no evidence that metabolic rate varies inversely with survival, at the level of either line means or additive effects of QTL.

Aging↗

Genomic regions determining resistance to leaf stripe (Pyrenophora graminea) in barley.

Leaf stripe is a seed-borne disease of barley (Hordeum vulgare) caused by Pyrenophora graminea. Little is known about the genetics of resistance to this pathogen. In the present work, QTL analysis was applied on two recombinant inbred line (RIL) populations derived from two- and six-rowed barley genotypes with different levels of partial resistance to barley leaf stripe. Quantitative trait loci for partial resistance were identified using the composite interval mapping (CIM) method of PLABQTL software, using the putative QTL markers as cofactors. In the L94 x 'Vada' mapping population, one QTL for resistance was detected on chromosome 2H; the same location as the leaf-stripe resistance gene Rdg1 mapped earlier in 'Alf', where it confers complete resistance to the pathogen. An additional minor-effect QTL was identified by further analyses in this segregating population on chromosome 7H. In L94 x C123, two QTLs for resistance were mapped, one each on chromosomes 7H and 2H.

Analysis of Variance↗

Efficient method for analysis of QTL using F1 progenies in an outcrossing species.

In the present paper, we proposed a statistical procedure based on composite interval mapping for accurate analysis of quantitative trait loci (QTL) for individuals sampled from an outcrossing population with two-generation families consisting of the sampled individuals and F1 progenies obtained by crossing them as parental individuals. In the proposed procedure, haplotypes of markers of parental individuals were reconstructed based on the genotypes of F1 progenies and QTL analyses with composite interval mapping were conducted separately for each of parents as well as jointly for both parents. A least squares method was applied to the composite interval mapping, where some of markers were selected as cofactors to absorb the variation induced by QTL located elsewhere in the genome. The procedure was evaluated for the efficiency in detecting QTL and the precision of estimates of locations and effects of QTL using simulations. It was shown that QTL with interaction between paternal and maternal alleles was effectively detected by joint analysis of both parents, while a QTL segregating only in one parent, closely linked to a QTL segregating only in the other parent, was successfully detected by analyzing separately each of the parents with inclusion of markers of both parents. The proposed procedure can provide detailed genetic information useful for marker assisted breeding in an outcrossing species such as forest trees.

Chromosome Mapping↗

Power of daughter and granddaughter designs for determining linkage between marker loci and quantitative trait loci in dairy cattle.

There is considerable interest in bovine DNA-level polymorphic marker loci as a means of mapping quantitative trait loci (QTL) of economic importance in cattle. Progeny of a sire heterozygous for both a marker locus and a linked QTL, which inherit different alleles for the marker, will have different trait means. Based on this, power to detect QTL, as a function of QTL effect, heritability of the trait, and number of animals tested was determined for 1) daughter design, marker genotype and quantitative trait values assessed on daughters of sires heterozygous for the markers; and 2) granddaughter design, a newly devised alternative design in which marker genotype is determined on sons of heterozygous sires and quantitative trait value measured on daughters of the sons. For equal numbers of assays, power increased with the number of daughters per sire (design 1) and sons per grandsire (design 2). For equal power and heritability less than or equal to .2, design 2 required half as many marker assays as design 1, e.g., with heritability of .2, QTL effect of .2 SD units, and type 1 error of .01, power was .70 if 400 daughters of each of 10 sires were assayed for the markers and .95 if markers were assayed on 100 sons of each of 20 sires with 50 granddaughters per son.

Alleles↗

Detection of quantitative trait loci affecting milk production traits on bovine chromosome 6 in a Chinese Holstein population by the daughter design.

Fourteen microsatellite markers with a coverage of 63.5 cM on bovine chromosome 6 were selected, and 26 sire families with 2,260 daughters were analyzed for mapping quantitative trait loci (QTL) affecting 5 milk production traits in a Chinese Holstein population. In the analyses across 26 families and within the largest significant families with a one-QTL model fitted, a QTL near BMS470 was detected that affected fat yield at the 5% experiment-wide significance level. When a 2-QTL model was fitted in the across-family analysis, it was found that there might exist 2 QTL affecting the 3 yield traits, although the exact or empirical thresholds for the significance testing were unknown. In all analyses, the results for milk yield and protein yield were generally consistent, which might have resulted from the same genetic background for milk and protein yield.

Animals↗