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PubMed2003

Mapping quantitative trait loci for yield, yield components and morphological traits in an advanced backcross population between Oryza rufipogon and the Oryza sativa cultivar Jefferson.

An advanced backcross population between an accession of Oryza rufipogon (IRGC 105491) and the U.S. cultivar Jefferson (Oryza sativa ssp. japonica) was developed to identify quantitative trait loci (QTLs) for yield, yield components and morphological traits. The genetic linkage map generated for this population consisted of 153 SSR and RFLP markers with an average interval size of 10.3 cM. Thirteen traits were examined, nine of which were measured in multiple environments. Seventy-six QTLs above an experiment-wise significance threshold of P<0.01 (corresponding to an interval mapping LOD>3.6 or a composite interval mapping LOD>3.9) were identified. For the traits measured in multiple environments, 47% of the QTLs were detected in at least two environments. The O. rufipogon allele was favorable for 53% of the yield and yield component QTLs, including loci for yield, grains per panicle, panicle length, and grain weight. Morphological traits related to the domestication process and/or weedy characteristics, including plant height, shattering, tiller type and awns, were found clustered on chromosomes 1 and 4. Comparisons to previous studies involving wild x cultivated crosses revealed O. rufipogon alleles with stable effects in multiple genetic backgrounds and environments, several of which have not been detected in studies between Oryza sativa cultivars, indicating potentially novel alleles from O. rufipogon. Some O. rufipogon-derived QTLs, however, were in similar regions as previously reported QTLs from Oryza sativa cultivars, providing evidence for conservation of these QTLs across the Oryza genus. In addition, several QTLs for grain weight, plant height, and flowering time were localized to putative homeologous regions in maize where QTLs for these traits have been previously reported, supporting the hypothesis of functional conservation of QTLs across the grasses.

Chromosome Mapping↗
PubMedDate not supplied

Identification of quantitative trait loci associated with bone traits and body weight in an F2 resource population of chickens.

Bone fractures at the end of lay are a significant problem in egg-laying strains of hens. The objective of the current study was to identify quantitative trait loci (QTL) associated with bone mineralization and strength in a chicken resource population. Layer (White Leghorn hens) and broiler (Cobb-Cobb roosters) lines were crossed to generate an F2 population of 508 hens over seven hatches, and 26 traits related to bone integrity, including bone mineral density (BMD) and content (BMC), were measured. Genotypes of 120 microsatellite markers on 28 autosomal groups were determined, and interval mapping was conducted to identify QTL regions. Twenty-three tests representing three chromosomal regions (chromosomes 4, 10 and 27) contained significant QTL that surpassed the 5% genome-wise threshold, and 47 tests representing 15 chromosomes identified suggestive QTL that surpassed the 5% chromosome-wise threshold. Although no significant QTL influencing BMD and BMC were detected after adjusting for variation in body weight and egg production, multiple suggestive QTL were found. These results support previous experiments demonstrating an important genetic regulation of bone strength in chickens, but suggest the regulation may be due to the effects of multiple genes that each account for relatively small amounts of variation in bone strength.

Animals↗
PubMed2026

eQTM (expression quantitative trait methylation) Atlas: a comprehensive resource of over 11 million DNA methylation-gene expression associations through across 11 tissues and 4 diseases.

MOTIVATION: Epigenome-wide association studies (EWAS) have identified numerous DNA methylation (DNAm) CpG sites associated with complex traits and diseases, but interpretation of those CpG sites remains challenging because in EWAS, CpGs are mostly linked to nearby genes based only on genomic proximity. Expression quantitative trait methylation (eQTM) analyses connect DNAm CpGs with statistically associated gene expression levels. However, a comprehensive, searchable resource integrating eQTMs across diverse tissues and disease contexts has been lacking. RESULTS: We developed the eQTM Atlas, a web-based resource that manually curates more than 11 million DNAm-gene expression associations from eight cohorts, covering 11 tissue types, four broad disease contexts, 173,886 unique CpG probes and 20,231 unique genes. The Atlas supports gene- or CpG- searches by tissue or disease type and finding associated CpG or genes, visualization of cis- and trans-eQTMs through genome browser, heatmap interfaces across various tissues, and cohort-level data downloads. By integrating eQTM results with EWAS resources, the eQTM Atlas enables users to connect disease- or trait-associated CpGs to statistically associated genes rather than relying solely on proximity-based gene annotation, supporting functional interpretation of EWAS findings and generation of disease-specific regulatory hypotheses. AVAILABILITY AND IMPLEMENTATION: The eQTM Atlas is freely available at https://shiny.crc.pitt.edu/eqtm_browser/. The web interface is implemented in R Shiny and hosted through the University of Pittsburgh Center for Research Computing (CRC). Source code is available at https://github.com/ads303/eQTM-Atlas.

DNA methylation↗
PubMed2004

Detection and mapping of quantitative trait loci that determine responsiveness of mice to nitrous oxide antinociception.

Exposure to 70% N(2)O evokes a robust antinociceptive effect in C57BL/6 (B6) but not in DBA/2 (D2) inbred mice. This study was conducted to identify quantitative trait loci (QTL) in the mouse genome that might determine responsiveness to N(2)O. Offspring from the F(2) generation bred from B6 and D2 progenitors exhibited a broad range of responsiveness to N(2)O antinociception as determined by the acetic acid-induced abdominal constriction test. QTL analysis was then used to dissect this continuous trait distribution into component loci, and to map them to broad chromosomal regions. To this end, 24 spleens were collected from each of the following four groups: male and female F(2) mice responding to 70% N(2)O in oxygen with 100% response (high-responders); and male and female F(2) mice responding with 0% response (low-responders). Genomic DNA was extracted from the spleens and genotyped with simple sequence length polymorphism MapPairs markers. Findings were combined with findings from the earlier QTL analysis from BXD recombinant inbred mice [Brain Res 725 (1996) 23]. Combined results revealed two significant QTL that influence responsiveness to nitrous oxide on proximal chromosome 2 and distal chromosome 5, and one suggestive QTL on midchromosome 18. The chromosome 2 QTL was evident only in males. A significant interaction was found between a locus on chromosome 6 and another on chromosome 13 with a substantial effect on N(2)O antinociception.

Animals↗
PubMed2002

Efficient algorithms for quantitative trait loci mapping problems.

Rapid advances in molecular genetics push the need for efficient data analysis. Advanced algorithms are necessary for extracting all possible information from large experimental data sets. We present a general linear algebra framework for quantitative trait loci (QTL) mapping, using both linear regression and maximum likelihood estimation. The formulation simplifies future comparisons between and theoretical analyses of the methods. We show how the common structure of QTL analysis models can be used to improve the kernel algorithms, drastically reducing the computational effort while retaining the original analysis results. We have evaluated our new algorithms on data sets originating from two large F(2) populations of domestic animals. Using an updating approach, we show that 1-3 orders of magnitude reduction in computational demand can be achieved for matrix factorizations. For interval-mapping/composite-interval-mapping settings using a maximum likelihood model, we also show how to use the original EM algorithm instead of the ECM approximation, significantly improving the convergence and further reducing the computational time. The algorithmic improvements makes it feasible to perform analyses which have previously been deemed impractical or even impossible. For example, using the new algorithms, it is reasonable to perform permutation testing using exhaustive search on populations of 200 individuals using an epistatic two-QTL model.

Algorithms↗
PubMed2004

Quantitative trait locus mapping of genetic modifiers of metabolic syndrome and atherosclerosis in low-density lipoprotein receptor-deficient mice: identification of a locus for metabolic syndrome and increased atherosclerosis on chromosome 4.

OBJECTIVE: The purpose of this study was to examine genetic factors responsible for metabolic syndrome and atherosclerosis in a setting of low-density lipoprotein (LDL) receptor deficiency in a cross between C57BL/6J (B6) and PERA/Ei (PERA) inbred mouse strains. METHODS AND RESULTS: Comparison of metabolic phenotypes in B6 and PERA strains revealed the PERA genetic background to be dramatically more susceptible to hyperleptinemia, hyperglycemia, hypertriglyceridemia, elevated insulin levels, and body fat increase than the B6 background. To facilitate genetic analysis, metabolic syndrome-related traits and atherosclerotic lesion area were measured in 167 [(PERAxB6.129S7-Ldlr(tm1Her))xB6.129S7-Ldlr(tm1Her)]N2 male and female backcross mice that were homozygous for the Ldlr null allele. Quantitative trait locus analysis was performed using 153 polymorphic microsatellite markers spanning the genome. On chromosome 4, we identified a locus influencing plasma triglyceride, insulin, and leptin concentrations, body weight, and atherosclerosis. Several other genetic loci were identified with separate effects on plasma insulin, body weight, high-density lipoprotein cholesterol, and atherosclerosis. CONCLUSIONS: The PERA strain is highly susceptible to the development of metabolic syndrome after feeding a Western-type diet. This susceptibility is due, in part, to a locus on murine chromosome 4 in which PERA alleles predispose to adiposity, increased insulin, and accelerated atherogenesis in the absence of marked hyperlipidemia.

Animals↗
PubMed2006

Estimation of genetic parameters for quantitative trait loci for dairy traits in the French Holstein population.

A marker-assisted selection program (MAS) has been implemented in dairy cattle in France. The efficiency of such a selection program depends on the use of correct genetic parameters for the marked quantitative trait loci (QTL). Therefore, the objective of this study was to estimate the proportion of genetic variance explained by 4 QTL described in previous studies (these QTL are segregating on chromosomes 6, 14, 20, and 26). Genotypes for 11 markers were available for 3,974 bulls grouped within 54 sire families of the French Holstein population undergoing MAS. The parameters were estimated for 4 QTL and 5 dairy traits: milk, fat and protein yields, and fat and protein percentages. The proportion of genetic variance explained by the QTL ranged from as low as 0.03 to 0.36%. Both lack of marker informativity and poor monitoring of QTL transmission might limit the accuracy of estimation. The QTL explained a larger proportion of genetic variance for milk composition traits. The QTL on chromosome 14 and chromosomes 6 and 20 have their largest influence on fat and protein percentages, respectively. The overall proportions of genetic variance explained by the QTL were 27.0, 30.7, 24.1, 48.2, and 33.6% for milk, fat and protein yields, and fat and protein percentages, respectively. These results clearly indicated that a large part of the genetic variance is explained by a small number of QTL and that their use in MAS might be beneficial for dairy cattle breeding programs.

Animals↗
PubMed1999

A quantitative-trait locus on chromosome 6p influences different aspects of developmental dyslexia.

Recent application of nonparametric-linkage analysis to reading disability has implicated a putative quantitative-trait locus (QTL) on the short arm of chromosome 6. In the present study, we use QTL methods to evaluate linkage to the 6p25-21.3 region in a sample of 181 sib pairs from 82 nuclear families that were selected on the basis of a dyslexic proband. We have assessed linkage directly for several quantitative measures that should correlate with different components of the phenotype, rather than using a single composite measure or employing categorical definitions of subtypes. Our measures include the traditional IQ/reading discrepancy score, as well as tests of word recognition, irregular-word reading, and nonword reading. Pointwise analysis by means of sib-pair trait differences suggests the presence, in 6p21.3, of a QTL influencing multiple components of dyslexia, in particular the reading of irregular words (P=.0016) and nonwords (P=.0024). A complementary statistical approach involving estimation of variance components supports these findings (irregular words, P=.007; nonwords, P=.0004). Multipoint analyses place the QTL within the D6S422-D6S291 interval, with a peak around markers D6S276 and D6S105 consistently identified by approaches based on trait differences (irregular words, P=.00035; nonwords, P=.0035) and variance components (irregular words, P=.007; nonwords, P=.0038). Our findings indicate that the QTL affects both phonological and orthographic skills and is not specific to phoneme awareness, as has been previously suggested. Further studies will be necessary to obtain a more precise localization of this QTL, which may lead to the isolation of one of the genes involved in developmental dyslexia.

Adolescent↗
PubMed1999

Identification of quantitative trait loci influencing traits related to energy balance in selection and inbred lines of mice.

Energy balance is a complex trait with relevance to the study of human obesity and maintenance energy requirements of livestock. The objective of this study was to identify, using unique mouse models, quantitative trait loci (QTL) influencing traits that contribute to variation in energy balance. Two F2 resource populations were created from lines of mice differing in heat loss measured by direct calorimetry as an indicator of energy expenditure. The HB F2 resource population originated from a cross between a noninbred line selected for high heat loss and an inbred line with low heat loss. Evidence for significant QTL influencing heat loss was found on chromosomes 1, 2, 3, and 7. Significant QTL influencing body weight and percentage gonadal fat, brown fat, liver, and heart were also identified. The LH F2 resource population originated from noninbred lines of mice that had undergone divergent selection for heat loss. Chromosomes 1 and 3 were evaluated. The QTL for heat loss identified on chromosome 1 in the HB population was confirmed in the LH population, although the effect was smaller. The presence of a QTL influencing 6-wk weight was also confirmed. Suggestive evidence for additional QTL influencing heat loss, percentage subcutaneous fat, and percentage heart was found for chromosome 1.

Adipose Tissue↗
PubMed2002

Bias in estimates of quantitative-trait-locus effect in genome scans: demonstration of the phenomenon and a method-of-moments procedure for reducing bias.

An attractive feature of variance-components methods (including the Haseman-Elston tests) for the detection of quantitative-trait loci (QTL) is that these methods provide estimates of the QTL effect. However, estimates that are obtained by commonly used methods can be biased for several reasons. Perhaps the largest source of bias is the selection process. Generally, QTL effects are reported only at locations where statistically significant results are obtained. This conditional reporting can lead to a marked upward bias. In this article, we demonstrate this bias and show that its magnitude can be large. We then present a simple method-of-moments (MOM)-based procedure to obtain more-accurate estimates, and we demonstrate its validity via Monte Carlo simulation. Finally, limitations of the MOM approach are noted, and we discuss some alternative procedures that may also reduce bias.

Bias↗
PubMed2006

A statistical framework for genome-wide scanning and testing of imprinted quantitative trait loci.

Non-equivalent expression of alleles at a locus results in genomic imprinting. In this article, a statistical framework for genome-wide scanning and testing of imprinted quantitative trait loci (iQTL) underlying complex traits is developed based on experimental crosses of inbred line species in backcross populations. The joint likelihood function is composed of four component likelihood functions with each of them derived from one of four backcross families. The proposed approach models genomic imprinting effect as a probability measure with which one can test the degree of imprinting. Simulation results show that the model is robust for identifying iQTL with various degree of imprinting ranging from no imprinting, partial imprinting to complete imprinting. Under various simulation scenarios, the proposed model shows consistent parameter estimation with reasonable precision and high power in testing iQTL. When a QTL shows Mendelian effect, the proposed model also outperforms traditional Mendelian model. Extension to incorporate maternal effect is also given. The developed model, built within the maximum likelihood framework and implemented with the EM algorithm, provides a quantitative framework for testing and estimating iQTL involved in the genetic control of complex traits.

Algorithms↗
PubMed2006

Quantitative trait analysis of seed yield and other complex traits in hybrid spring rapeseed (Brassica napus L.): 1. Identification of genomic regions from winter germplasm.

The introgression of winter germplasm into spring canola (Brassica napus L.) represents a novel approach to improve seed yield of hybrid spring canola. In this study, quantitative trait loci (QTL) for seed yield and other traits were genetically mapped to determine the effects of genomic regions introgressed from winter germplasm into spring canola. Plant materials used comprised of two populations of doubled haploid (DH) lines having winter germplasm introgression from two related French winter cultivars and their testcrosses with a spring line used in commercial hybrids. These populations were evaluated for 2 years at two locations (Wisconsin, USA and Saskatchewan, Canada). Genetic linkage maps based on RFLP loci were constructed for each DH population. Six QTL were detected in the testcross populations for which the winter alleles increased seed yield. One of these QTL explained 11 and 19% of the phenotypic variation in the two Canadian environments. The winter allele for another QTL that increased seed yield was linked in coupling to a QTL allele for high glucosinolate content, suggesting that the transition of rapeseed into canola could have resulted in the loss of favorable seed yield alleles. Most QTL for which the introgressed allele decreased seed yield of hybrids mapped to genomic regions having homoeologous non-reciprocal transpositions. This suggests that allelic configurations created by these rearrangements might make an important contribution to genetic variation for complex traits in oilseed B. napus and could account for a portion of the heterotic effects in hybrids.

Alleles↗
PubMed2001

Possible causal relationships between cerebellar patterns of foliation and hindlimb coordination in laboratory mice: a quantitative trait locus analysis.

The cerebellum is involved in a large set of integrative functions including memory, affect, and motricity. The cerebellar patterns of foliation and their causal relationships with motricity were investigated via a wide genome scan approach and quantitative trait locus (QTL) strategy. QTLs were mapped in an F2 population derived from NZB/B1NJ and C57BL/6By inbred strains of mice for cerebellar fissures in the four vermal lobules (intraculminate, uvula, declival, and intracentral) and for hindpaw slips in a bar crossing test. No linkage was detected for uvula and intracentral fissures. We found five QTLs linked to declival fissure: Cpfd-1q and Cpfd-2q (chromosome 1), Cpfd-3q (chromosome 5), Cpfd-4q (chromosome 9), and Cpfd-5q (chromosome 13). Two QTLs were also mapped for intraculminate fissure Cpfi-1q (chromosome 4) and Cpfi-2q (chromosome 1). Most of the confidence intervals of these QTLs included genes that were previously identified for their implication in the physiological mechanisms underlying cerebellar patterns of foliation. Only one significant QTL was found for the measure of hindpaw coordination (Tne-1q). It was linked with Cpfd-1q and Cpfd-2q on the telomeric part of chromosome 1.

Animals↗
PubMedDate not supplied

Rats with inherited stress-induced arterial hypertension (ISIAH strain) display specific quantitative trait loci for blood pressure and for body and kidney weight on chromosome 1.

1. The aim of the present study was to scan chromosome 1 in the hypertensive 'inherited stress-induced arterial hypertension' (ISIAH) rat strain for the quantitative trait loci (QTL) that control basal and stress-induced arterial blood pressure (ABP) levels and weight traits. 2. Two F(2) populations of 3-4- and 6-month-old male rats derived from a cross between the normotensive Wistar albino Glaxo (WAG) and hypertensive ISIAH rats were used in the search for the QTL. To identify the QTL for blood pressure (basal and under stress) and weight traits (bodyweight, as well as the weight of the adrenals, kidney and heart), 12 polymorphic markers covering a span of 234.6 Mb on chromosome 1 were analysed. 3. In 3-4-month-old rats, QTL were found for bodyweight in the vicinity of the D1Rat76 marker (230.6 Mb; P = 0.0019; logarithm of odds (LOD) score 3.23) and for relative kidney weight in the vicinity of the D1Rat117 marker (219.3 Mb; P = 0.000992; LOD score 3.41). No QTL for blood pressure were detected on chromosome 1 in the 3-4-month-old population. 4. In 6-month-old rats, a QTL for basal ABP in the region spanning 168.0-250.4 Mb, with two peaks around the markers D1Rat168 (204.8 Mb; P = 0.00087; LOD score 3.42) and D1Rat76 (P = 0.0006; LOD score 3.34), was described. A novel QTL was found in the D1Rat54-D1Rat168 region for stress-induced blood pressure (P = 0.0014; LOD score 3.08). 5. The results provide support for the existence of age-dependent differences in the genetic control of ABP and weight traits. Chromosome 1 was characterized by four QTL: for bodyweight and relative kidney weight in 3-4-month-old F(2) (ISIAH yen WAG) rats and basal ABP and ABP under emotional (restraint) stress conditions in 6-month-old F(2) rats. The QTL for stress-induced ABP seems to be novel and specific to the ISIAH rat strain.

Age Factors↗
PubMed1997

Mapping quantitative trait loci with dominant and missing markers in various crosses from two inbred lines.

Dominant phenotype of a genetic marker provides incomplete information about the marker genotype of an individual. A consequence of using this incomplete information for mapping quantitative trait loci (QTL) is that the inference of the genotype of a putative QTL flanked by a marker with dominant phenotype will depend on the genotype or phenotype of the next marker. This dependence can be extended further until a marker genotype is fully observed. A general algorithm is derived to calculate the probability distribution of the genotype of a putative QTL at a given genomic position, conditional on all observed marker phenotypes in the region with dominant and missing marker information for an individual. The algorithm is implemented for various populations stemming from two inbred lines in the context of mapping QTL. Simulation results show that if only a proportion of markers contain missing or dominant phenotypes, QTL mapping can be almost as efficient as if there were no missing information in the data. The efficiency of the analysis, however, may decrease substantially when a very large proportion of markers contain missing or dominant phenotypes and a genetic map has to be reconstructed first on the same data as well. So it is important to combine dominant markers with codominant markers in a QTL mapping study.

Algorithms↗
PubMed1998

Effects of quantitative trait loci for lipid phenotypes in the rat are influenced by age.

1. Previous study on the backcross hybrids derived from a cross of the spontaneously hypertensive rat (SHR/Mol) and the spontaneously diabetic BB/OK rat demonstrated the existence of quantitative trait loci (QTL) affecting lipid phenotypes on chromosome 4 and suggestive linkage of lipid phenotypes with markers on chromosome 1. Because the previous study was performed with backcross hybrids at 12 weeks of age and it is known that lipid phenotypes can show age-related differences, in the present study, the effect of QTL (chromosome 1 and 4) on serum triglycerides and cholesterol was longitudinally analysed between 20 and 32 weeks of age in backcross hybrids. 2. The results of the present study show that the effect of QTL on chromosome 4 (between It-6 and D4Mit9) for serum triglycerides was maximal at 20 weeks of age, but disappeared at 32 weeks of age. In contrast, the effect of QTL on serum total cholesterol on chromosome 4 (Npy-Spr) was maximal at 32 weeks of age. In contrast with the first study (12 weeks), the longitudinal investigation showed significant linkage of DIMit14 marker with lipid phenotypes on chromosome 1. 3. The results of the present study indicate the necessity of considering the role of age in QTL analysis of lipid phenotypes.

Aging↗
PubMed2005

Time-related mapping of quantitative trait loci controlling grain-filling in rice (Oryza sativa L.).

Grain-filling is a crucial process that determines final grain yield in rice (Oryza sativa L.). To understand the genetic basis of dynamics of grain-filling, quantitative trait locus (QTL) analysis was conducted using time-related phenotypic data on grain-filling collected from a population of 155 recombinant inbred lines (F12), derived from a cross between Milyang 23 and Akihikari. Two QTLs detected on chromosomes 8 and 12 were strongly associated with increased filling percentage per panicle. These QTLs were not linked with those controlling spikelet numbers per panicle. This result confers the possibility of improving grain-filling together with an enlargement of sink size. The QTL for filling percentage per panicle on chromosome 8 exactly overlapped that for non-structural carbohydrate (NSC) content in the culm and leaf sheaths during grain-filling, and the Milyang 23 allele associated with increased grain-filling percentage per panicle was associated with decreased NSC content. Therefore, this QTL may be directly involved in NSC translocation from the culm and leaf sheaths to panicle. In addition, the Milyang 23 alleles of QTLs associated with greater spikelet number per panicle on chromosomes 1 and 6 were also related with a reduction in NSC content in the culm and leaf sheaths during grain-filling. These results indicate that NSC dynamics during grain-filling is partly dependent on sink size. NSC accumulation in the culm and leaf sheaths at the heading stage was mainly controlled by different genetic regulations from NSC dynamics during grain-filling. Nitrogen dynamics during grain-filling may also be involved in carbohydrate dynamics.

Chromosome Mapping↗
PubMed1998

Quantitative trait loci on chromosomes 1 and 4 affect lipid phenotypes in the rat.

The spontaneously hypertensive rat (SHR/Mol) and the spontaneously diabetic BB/OK rat were crossed, and the F1 hybrids were backcrossed onto the BB/OK rat in order to search for quantitative trait loci (QTL) affecting serum total cholesterol and triglycerides on chromosomes 1, 3, 4, 10, 13, 18, and X. On chromosome 4 a QTL for triglyceride levels (lod score 3.3) was found within the region flanked by the D4Mit9 and Il-6 markers. Suggestive linkage (lod score 1.9) was found for total cholesterol on chromosome 4 at the Spr locus. Also, on chromosome 1 suggestive linkage for both investigated traits was found at marker D1Mit14 (lod score 1.9 for triglycerides, 2.1 for total cholesterol). The results of the study could contribute to the explanation of the genetic basis of lipid abnormalities, which are a common feature of pathological disorders such as coronary heart disease, hypertension, or non-insulin-dependent diabetes.

Animals↗