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Results for “Quantitative Trait Loci”

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PubMed2006

Quantitative trait loci (QTL) mapping of resistance to strongyles and coccidia in the free-living Soay sheep (Ovis aries).

A genome-wide scan was performed to detect quantitative trait loci (QTL) for resistance to gastrointestinal parasites and ectoparasitic keds segregating in the free-living Soay sheep population on St. Kilda (UK). The mapping panel consisted of a single pedigree of 882 individuals of which 588 were genotyped. The Soay linkage map used for the scans comprised 251 markers covering the whole genome at average spacing of 15cM. The traits here investigated were the strongyle faecal egg count (FEC), the coccidia faecal oocyst count (FOC) and a count of keds (Melophagus ovinus). QTL mapping was performed by means of variance component analysis so that the genetic parameters of the study traits were also estimated and compared with previous studies in Soay and domestic sheep. Strongyle FEC and coccidia FOC showed moderate heritability (h(2)=0.26 and 0.22, respectively) in lambs but low heritability in adults (h(2)<0.10). Ked count appeared to have very low h(2) in both lambs and adults. Genome scans were performed for the traits with moderate heritability and two genomic regions reached the level of suggestive linkage for coccidia FOC in lambs (logarithm of the odds=2.68 and 2.21 on chromosomes 3 and X, respectively). We believe this is the first study to report a QTL search for parasite resistance in a free-living animal population and therefore may represent a useful reference for similar studies aimed at understanding the genetics of host-parasite co-evolution in the wild.

Animals↗

PubMed2024

Complementation testing identifies genes mediating effects at quantitative trait loci underlying fear-related behavior.

Knowing the genes involved in quantitative traits provides an entry point to understanding the biological bases of behavior, but there are very few examples where the pathway from genetic locus to behavioral change is known. To explore the role of specific genes in fear behavior, we mapped three fear-related traits, tested fourteen genes at six quantitative trait loci (QTLs) by quantitative complementation, and identified six genes. Four genes, Lamp, Ptprd, Nptx2, and Sh3gl, have known roles in synapse function; the fifth, Psip1, was not previously implicated in behavior; and the sixth is a long non-coding RNA, 4933413L06Rik, of unknown function. Variation in transcriptome and epigenetic modalities occurred preferentially in excitatory neurons, suggesting that genetic variation is more permissible in excitatory than inhibitory neuronal circuits. Our results relieve a bottleneck in using genetic mapping of QTLs to uncover biology underlying behavior and prompt a reconsideration of expected relationships between genetic and functional variation.

Animals↗

PubMed2004

Multiple quantitative trait loci mapping with cofactors and application of alternative variants of the false discovery rate in an enlarged granddaughter design.

The experimental power of a granddaughter design to detect quantitative trait loci (QTL) in dairy cattle is often limited by the availability of progeny-tested sires, by the ignoring of already identified QTL in the statistical analysis, and by the application of stringent experimentwise significance levels. This study describes an experiment that addressed these points. A large granddaughter design was set up that included sires from two countries (Germany and France), resulting in almost 2000 sires. The animals were genotyped for markers on nine different chromosomes. The QTL analysis was done for six traits separately using a multimarker regression that included putative QTL on other chromosomes as cofactors in the model. Different variants of the false discovery rate (FDR) were applied. Two of them accounted for the proportion of truly null hypotheses, which were estimated to be 0.28 and 0.3, respectively, and were therefore tailored to the experiment. A total of 25 QTL could be mapped when cofactors were included in the model-7 more than without cofactors. Controlling the FDR at 0.05 revealed 31 QTL for the two FDR methods that accounted for the proportion of truly null hypotheses. The relatively high power of this study can be attributed to the size of the experiment, to the QTL analysis with cofactors, and to the application of an appropriate FDR.

Animals↗

PubMed2005

Quantitative trait loci mapped to single-nucleotide resolution in yeast.

Identifying the genetic variation underlying quantitative trait loci remains problematic. Consequently, our molecular understanding of genetically complex, quantitative traits is limited. To address this issue directly, we mapped three quantitative trait loci that control yeast sporulation efficiency to single-nucleotide resolution in a noncoding regulatory region (RME1) and to two missense mutations (TAO3 and MKT1). For each quantitative trait locus, the responsible polymorphism is rare among a diverse set of 13 yeast strains, suggestive of genetic heterogeneity in the control of yeast sporulation. Additionally, under optimal conditions, we reconstituted approximately 92% of the sporulation efficiency difference between the two genetically distinct parents by engineering three nucleotide changes in the appropriate yeast genome. Our results provide the highest resolution to date of the molecular basis of a quantitative trait, showing that the interaction of a few genetic variants can have a profound phenotypic effect.

Adaptor Proteins, Signal Transducing↗

PubMed1987

Molecular-marker-facilitated investigations of quantitative-trait loci in maize. I. Numbers, genomic distribution and types of gene action.

Individual genetic factors which underlie variation in quantitative traits of maize were investigated in each of two F2 populations by examining the mean trait expressions of genotypic classes at each of 17-20 segregating marker loci. It was demonstrated that the trait expression of marker locus classes could be interpreted in terms of genetic behavior at linked quantitative trait loci (QTLs). For each of 82 traits evaluated, QTLs were detected and located to genomic sites. The numbers of detected factors varied according to trait, with the average trait significantly influenced by almost two-thirds of the marked genomic sites. Most of the detected associations between marker loci and quantitative traits were highly significant, and could have been detected with fewer than the 1800-1900 plants evaluated in each population. The cumulative, simple effects of marker-linked regions of the genome explained between 8 and 40% of the phenotypic variation for a subset of 25 traits evaluated. Single marker loci accounted for between 0.3% and 16% of the phenotypic variation of traits. Individual plant heterozygosity, as measured by marker loci, was significantly associated with variation in many traits. The apparent types of gene action at the QTLs varied both among traits and between loci for given traits, although overdominance appeared frequently, especially for yield-related traits. The prevalence of apparent overdominance may reflect the effects of multiple QTLs within individual marker-linked regions, a situation which would tend to result in overestimation of dominance. Digenic epistasis did not appear to be important in determining the expression of the quantitative traits evaluated. Examination of the effects of marked regions on the expression of pairs of traits suggests that genomic regions vary in the direction and magnitudes of their effects on trait correlations, perhaps providing a means of selecting to dissociate some correlated traits. Marker-facilitated investigations appear to provide a powerful means of examining aspects of the genetic control of quantitative traits. Modifications of the methods employed herein will allow examination of the stability of individual gene effects in varying genetic backgrounds and environments.

Chromosome Mapping↗

PubMed2006

Rearranged gene order between pig and human in a quantitative trait loci region on SSC3.

A quantitative trait locus (QTL) for ovulation rate on chromosome 3 that peaks at 36 cM has been identified in a Meishan-White composite resource population with an additive effect of 2.2 corpora lutea. As part of an effort to identify the responsible gene(s), typing of additional genes on the INRA-University of Minnesota porcine radiation hybrid (IMpRH) map of SSC3 and comparative analysis of gene order was conducted. We placed 52 known genes and expressed sequence tags, two BAC-end sequences and one microsatellite (SB42) on a framework map that fills gaps on previous RH maps. Data were analysed for two-point and multipoint linkage with the IMpRH mapping tool and were submitted to the IMpRH database (http://imprh.toulouse.inra.fr/). Gene order was confirmed for 42 loci residing in the QTL region (spanning c. 17 Mb of human sequence) by using the high-resolution IMpRH2 panel. Carthagène (http://www.inra.fr/internet/departments/MIA/T/CarthaGene) was used to estimate multipoint marker distance and order using all public markers on SSC3 in the IMpRH database and those typed in this study. For the high-resolution map, only data for markers typed in both panels were used. Comparative analysis of human and porcine maps identified conservation of gene order for SSC3q and multiple blocks of conserved segments for SSC3p, which included six distinct segments of HSA7 and two segments of HSA16. The results of this study allow significant refinement of the SSC3p region that contains an ovulation rate QTL.

Animals↗

PubMed2004

Quantitative trait Loci for health traits in Finnish Ayrshire cattle.

A whole-genome scan was conducted to search for quantitative trait loci (QTL) affecting health traits in Finnish Ayrshire dairy cattle. The mapping population consisted of 12 bulls and their 491 sons in a granddaughter design. A total of 150 markers were typed covering all 29 autosomes. The traits under study were somatic cell score, mastitis, and a group of other veterinary treatments. Effects of the QTL and positions were estimated with the regression method. When carrying out interval mapping on each chromosome, cofactors were used to adjust for QTL identified at other chromosomes. Empirical P-values were obtained by permutation. Altogether 17 QTL were detected with genomewise significant P-values in the across family analysis. Quantitative trait loci affecting SCS were identified on chromosomes 1, 3, 11, 18, 21, 24, 27, 29, and QTL for mastitis on chromosomes 14, 18. Quantitative trait loci for other veterinary treatments were found on chromosomes 1, 2, 5, 8, 15, 22, and 23. The allele substitution effects were from 0.5 to 1.7 genetic standard deviations. The positions of these health QTL did not overlap with milk QTL detected in previous studies of the same population.

Animals↗

PubMed2004

Mapping quantitative trait loci affecting dairy conformation to chromosome 27 in two Holstein grandsire families.

Preliminary marker association results for quantitative trait loci affecting conformation traits using the granddaughter design and 8 large US Holstein grandsire families revealed strong associations in two families between the predicted transmitting abilities for dairy conformation and marker genotypes on bovine chromosome 27. Those results were based on single marker-trait associations in a genome-scan to identify broad chromosomal regions potentially containing genes affecting traits of interest. Results presented here describe continued study of quantitative trait loci on chromosome 27 for eventual incorporation into a marker-assisted selection program. Tests of marker associations for family 8 (91 sons) indicated an association with a microsatellite marker located near the telomere of chromosome 27. Interval analysis performed using additional marker genotypes generated for family 8 yielded further evidence for a quantitative trait locus in this region. No evidence was found for associations with milk production traits in this family in this region. An association was also detected in family 2 (240 sons) with a microsatellite marker located approximately 21 cM from the centromere of chromosome 27. Interval analysis performed for family 2 yielded evidence for a quantitative trait locus for dairy conformation near BMS689 with evidence of associations with fat percentage in the same region. Identification of quantitative trait loci affecting dairy conformation and fat components supports results reported by other groups, providing additional evidence that genes affecting fat metabolism are located on bovine chromosome 27.

Animals↗

PubMed2006

Quantitative trait loci analysis of primary cell wall composition in Arabidopsis.

Quantitative trait loci (QTL) analysis was used to identify genes underlying natural variation in primary cell wall composition in Arabidopsis (Arabidopsis thaliana). The cell walls of dark-grown seedlings of a Bay-0 x Shahdara recombinant inbred line population were analyzed using three miniaturized global cell wall fingerprinting techniques: monosaccharide composition analysis by gas chromatography, xyloglucan oligosaccharide mass profiling, and whole-wall Fourier-transform infrared microspectroscopy. Heritable variation and transgression were observed for the arabinose-rhamnose ratio, xyloglucan side-chain composition (including O-acetylation levels), and absorbance for a subset of Fourier-transform infrared wavenumbers. In total, 33 QTL, corresponding to at least 11 different loci controlling dark-grown hypocotyl length, pectin composition, and levels of xyloglucan fucosylation and O-acetylation, were identified. One major QTL, accounting for 51% of the variation in the arabinose-rhamnose ratio, affected the number of arabinan side chains presumably attached to the pectic polysaccharide rhamnogalacturonan I, paving the way to positional cloning of the first gene underlying natural variation in pectin structure. Several QTL were found to be colocalized, which may have implications for the regulation of xyloglucan metabolism. These results demonstrate the feasibility of combining fingerprinting techniques, natural variation, and quantitative genetics to gain original insight into the molecular mechanisms underlying the structure and metabolism of cell wall polysaccharides.

Arabidopsis↗

PubMed2000

Linkage disequilibrium analysis of biallelic DNA markers, human quantitative trait loci, and threshold-defined case and control subjects.

Linkage disequilibrium (LD) mapping has been applied to many simple, monogenic, overtly Mendelian human traits, with great success. However, extensions and applications of LD mapping approaches to more complex human quantitative traits have not been straightforward. In this article, we consider the analysis of biallelic DNA marker loci and human quantitative trait loci in settings that involve sampling individuals from opposite ends of the trait distribution. The purpose of this sampling strategy is to enrich samples for individuals likely to possess (and not possess) trait-influencing alleles. Simple statistical models for detecting LD between a trait-influencing allele and neighboring marker alleles are derived that make use of this sampling scheme. The power of the proposed method is investigated analytically for some hypothetical gene-effect scenarios. Our studies indicate that LD mapping of loci influencing human quantitative trait variation should be possible in certain settings. Finally, we consider possible extensions of the proposed methods, as well as areas for further consideration and improvement.

Alleles↗

PubMed1999

High-resolution mapping of quantitative trait loci in outbred mice.

Screening the whole genome of a cross between two inbred animal strains has proved to be a powerful method for detecting genetic loci underlying quantitative behavioural traits, but the level of resolution offered by quantitative trait loci (QTL) mapping is still too coarse to permit molecular cloning of the genetic determinants. To achieve high-resolution mapping, we used an outbred stock of mice for which the entire genealogy is known. The heterogeneous stock (HS) was established 30 years ago from an eight-way cross of C57BL/6, BALB/c, RIII, AKR, DBA/2, I, A/J and C3H inbred mouse strains. At the time of the experiment reported here, the HS mice were at generation 58, theoretically offering at least a 30-fold increase in resolution for QTL mapping compared with a backcross or an F2 intercross. Using the HS mice we have mapped a QTL influencing a psychological trait in mice to a 0.8-cM interval on chromosome 1. This method allows simultaneous fine mapping of multiple QTLs, as shown by our report of a second QTL on chromosome 12. The high resolution possible with this approach makes QTLs accessible to positional cloning.

Animals↗

PubMed2000

Whole genome scan to detect quantitative trait loci for conformation and functional traits in dairy cattle.

A granddaughter design was used to locate quantitative trait loci determining conformation and functional traits in dairy cattle. In this granddaughter design, consisting of 20 Holstein Friesian grandsires and 833 sons, genotypes were determined for 277 microsatellite markers covering the whole genome. Breeding values for 27 traits, regarding conformation (18), fertility (2), birth (4), workability (2), and udder health (1), were evaluated in an across-family analysis using multimarker regression. Significance thresholds were determined using a permutation test. The across-family analysis suggested the presence of 61 quantitative trait loci when 27 (i.e., one for each trait) were expected by chance. The test statistic exceeded the genomewise significance threshold for the following traits and chromosomes: chest width on chromosome 2; gestation length on chromosome 4; stature, body capacity, and size on chromosome 5; dairy character on chromosome 6; angularity on chromosome 12; fore udder attachment on chromosome 13; and fore udder attachment and front teat placement on chromosome 19. The quantitative trait loci for size traits on chromosomes 2, 5, and 6 may also have an effect on calving ease. The quantitative trait loci for udder traits on chromosomes 13 and 19 may also affect somatic cell score and mastitis resistance. If there are no negative effects on other economically important traits, marker assisted selection using markers associated with these quantitative trait loci can be applied.

Animals↗

PubMed2004

Quantitative trait loci affecting plant regeneration from protoplasts of Brassica oleracea.

Quantitative trait loci (QTLs) controlling the plant-regeneration ability of Brassica oleracea protoplasts were mapped in a population of 128 F(2) plants derived from a cross between the high-responding, rapid-cycling line and a low-responding, broccoli breeding line of B. oleracea. A modified bulked segregant analysis with AFLP markers identified two QTLs for plant regeneration. In a multiple regression analysis, the two QTLs explained 83% of the total genetic variation for regeneration recorded 15 weeks after initial transfer of microcalli to regeneration medium. Both QTLs showed additive effects, and the alleles contributing to the high regeneration frequencies were derived from the high-responding, rapid-cycling line. Using microsatellites with known location, the two QTLs were mapped to linkage groups O2 and O9 on the map published by Sebastian et al. [(2000) Theor Appl Genet 100:75-81] or to chromosomes C8 and C7 on the map published by Saal et al. [(2001) Theor Appl Genet 102:695-699]. QTLs for the early flowering trait of the rapid-cycling parent have previously been mapped to the same two linkage groups. Association between flowering time and regeneration ability was, however, not found in the present material, indicating that plant-regeneration ability can be transferred between cultivars independently of the early flowering trait. The detection of two major QTLs for plant regeneration in B. oleracea may provide the initial step towards the identification of markers suitable for marker-assisted selection of regeneration ability.

Brassica↗

PubMed2006

Connected populations for detecting quantitative trait loci and testing for epistasis: an application in maize.

Quantitative trait loci (QTL) detection experiments have often been restricted to large biallelic populations. Use of connected multiparental crosses has been proposed to increase the genetic variability addressed and to test for epistatic interactions between QTL and the genetic background. We present here the results of a QTL detection performed on six connected F(2) populations of 150 F(2:3) families each, derived from four maize inbreds and evaluated for three traits of agronomic interest. The QTL detection was carried out by composite interval mapping on each population separately, then on the global design either by taking into account the connections between populations or not. Epistatic interactions between loci and with the genetic background were tested. Taking into account the connections between populations increased the number of QTL detected and the accuracy of QTL position estimates. We detected many epistatic interactions, particularly for grain yield QTL (R(2) increase of 9.6%). Use of connections for the QTL detection also allowed a global ranking of alleles at each QTL. Allelic relationships and epistasis both contribute to the lack of consistency for QTL positions observed among populations, in addition to the limited power of the tests. The potential benefit of assembling favorable alleles by marker-assisted selection are discussed.

Epistasis, Genetic↗

PubMed2003

Use of a panel of congenic strains to evaluate differentially expressed genes as candidate genes for blood pressure quantitative trait loci.

Candidate gene(s) for multiple blood pressure (BP) quantitative trait loci (QTL) were sought by analysis of differential gene expression patterns in the kidneys of a panel of eight congenic strains, each of which carries a different low-BP QTL allele with a genetic composition that is otherwise similar to that of the hypertensive Dahl salt-sensitive (S) rat strain. First, genes differentially expressed in the kidneys of one-month-old Dahl S and salt-resistant (R) rats were identified. Then, Northern filter hybridization was used to examine the expression patterns of these genes in a panel of congenic strains. Finally, their chromosomal location was determined by radiation hybrid (RH) mapping. Seven out of 37 differentially expressed genes were mapped to congenic regions carrying BP QTLs, but only one of these genes, L-2 hydroxy acid oxidase (Hao2), showed the congenic strain-specific pattern of differential kidney gene expression predicted by its chromosomal location. This data suggests that Hao2 should be examined as a candidate gene for the rat chromosome 2 (RNO2) BP QTL.

Alcohol Oxidoreductases↗

PubMed2026

Identifying Single-Cell Expression Quantitative Trait Loci Using a Bootstrap Penalized Hurdle Model.

BACKGROUND: Expression quantitative trait loci (eQTL) analysis links genetic variants to gene expression levels, helping to uncover how genetic variation contributes to gene regulation. While traditional eQTL analyses rely on bulk RNA-seq data, recent advances in single-cell RNA sequencing (scRNA-seq) have made it possible to detect cell-type-specific eQTLs. However, the inherent sparsity and heterogeneity of scRNA-seq data present major challenges for standard modeling approaches. METHODS: In this paper, we propose a novel statistical framework, Bootstrap Penalized Hurdle regression model (BPHurdle), designed specifically for scRNA-seq data. BPHurdle employs a hurdle modeling framework, where a logistic component accounts for the excess zeros in single-cell expression data, and a Poisson component jointly evaluates the effects of multiple SNPs on positive gene expression levels. RESULTS: Through simulation studies, we show that BPHurdle achieves high accuracy and robustness in identifying regulatory variants. We further demonstrate its utility on a real dataset through a case study focusing on a subset of differentially expressed genes, where it successfully identifies reliable cell-type-specific eQTLs. CONCLUSIONS: Overall, BPHurdle offers an advanced and flexible approach for single-cell eQTL mapping, providing deeper insight into the genetic regulation of gene expression at cellular resolution.

Quantitative Trait Loci↗

PubMed2003

Quantitative trait loci and comparative genomics of cereal cell wall composition.

Quantitative trait loci (QTLs) affecting sugar composition of the cell walls of maize (Zea mays) pericarp were mapped as an approach to the identification of genes involved in cereal wall biosynthesis. Mapping was performed using the IBM (B73 x Mo17) recombinant inbred line population. There were statistically significant differences between B73 and Mo17 in content of xylose (Xyl), arabinose (Ara), galactose (Gal), and glucose. Thirteen QTLs were found, affecting the content of Xyl (two QTLs), Ara (two QTLs), Gal (five QTLs), Glc (two QTLs), Ara + Gal (one QTL), and Xyl + Glc (one QTL). The chromosomal regions corresponding to two of these, affecting Ara + Gal and Ara on maize chromosome 3, could be aligned with a syntenic region on rice (Oryza sativa) chromosome 1, which has been completely sequenced and annotated. The contiguous P1-derived artificial chromosome rice clones covering the QTLs were predicted to encode 117 and 125 proteins, respectively. Two of these genes encode putative glycosyltransferases, displaying similarity to carbohydrate-active enzyme database family GT4 (galactosyltransferases) or to family GT64 (C-terminal domain of animal heparan synthases). The results illustrate the potential of using natural variation, emerging genomic resources, and homeology within the Poaceae to identify candidate genes involved in the essential process of cell wall biosynthesis.

Carbohydrate Metabolism↗

PubMed2006

Further studies on using multiple-cross mapping (MCM) to map quantitative trait loci.

We have completed whole-genome scans for quantitative trait loci (QTLs) associated with acute ethanol-induced activation in the six F(2) intercrosses that can be formed from the C57BL/6J (B6), DBA/2J (D2) , BALB/cJ (C), and LP/J (LP) inbred strains. The goal was to test the hypothesis that given the relatively simple structure of the laboratory mouse genome, the same QTLs will be detected in multiple crosses which in turn will provide support for the strategy of multiple-cross mapping (MCM). QTLs with LOD scores greater than 4 were detected on Chrs 1, 2, 3, 8, 9, 13, 14, and 16. Only for the QTL on distal Chr 1 was there convincing evidence that the same or at least a very similar QTL was detected in multiple crosses. We also mapped the Chr 2 QTL directly in heterogeneous stock (HS) animals derived from the four inbred strains. At G(19) the QTL was mapped to an approximately 3-Mbp interval and this interval was associated with a haplotype block with a largely biallelic structure: B6-L:C-D2. We conclude that mapping in HS animals not only provides significantly greater QTL resolution, at least in some cases it provides significantly more information about the QTL haplotype structure.

Animals↗