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PubMed1984

An application of a model for a genotype-dependent relationship between a concomitant (age) and a quantitative trait (LDL cholesterol) in pedigree data.

In most genetic studies in humans the variability in a quantitative trait is adjusted for variability in concomitants (age, sex, etc) using a single regression equation prior to analyses of pedigree data. To illustrate an alternative approach, a single locus genetic model was tested. This model incorporates genotypic effects on the level of the trait, the variability in the trait, and the relationship between a concomitant and the trait. In this study, the model was applied to measures of age and low-density lipoprotein (LDL) cholesterol in a large kindred with familial hypercholesterolemia. The application of this model to 322 individuals in four generations provided evidence that genotypic variation at a single locus influences LDL levels early in life, the rate of increase of LDL with age and the phenotypic variance. A model with genotype-dependent slope and variance fit the data significantly better than a model with slope and variance independent of genotype. The inclusion of age-specific genotypic differences contributed to identification of high-risk individuals, to statistical support for a major locus, and to evidence for genetic determination of the tracking of LDL levels. Models that incorporate genotype-specific concomitant effects have the potential to represent more realistically the relationship between genotypic variability and quantitative phenotypic variation than models that assume that these effects do not exist.

Adolescent↗
PubMed1996

Complex interactions of new quantitative trait loci, Sluc1, Sluc2, Sluc3, and Sluc4, that influence the susceptibility to lung cancer in the mouse.

Many complex traits, including susceptibility to lung cancer, are controlled by multiple genes--quantitative trait loci (QTLs). We facilitated the mapping of QTLs by making use of recombinant congenic strains (RCS), a system of mouse inbred strains in which the genetic complexity is reduced, and by applying MQM-mapping (multiple-QTL models or marker-QTL-marker), a multilocus method with an increased power of detecting of individual QTLs and interacting QTLs (epistasis). The mouse strain O20 develops significantly larger N-ethyl-N-nitrosourea induced lung tumours than mice of the RC strain OcB-9 (ref. 5); the latter share approximately 87.5% of their genes with strain O20 and 12.5% with strain B10.O20 (refs 6,7). QTL analysis of 222 (OcB-9 x O20) F2 mice revealed four new loci that influence susceptibility to lung cancer (Sluc genes). They are involved in two significant, partly counteracting interactions which mask their individual main effects: Sluc1 (on chromosome 19) interacts with Sluc2 (chromosome 2), and Sluc3 (chromosome 6) interacts with Sluc4 (chromosome 11). Together with the data of van Wezel et al. in the accompanying report, our results indicate that interactions between tumour susceptibility genes are a common phenomenon which complicates their mapping.

Animals↗
PubMed2006

Use of microsatellite markers to detect quantitative trait loci in Yorkshire pigs.

To identify genetic markers associated with economic traits in pigs, 157 microsatellite markers were examined in Yorkshire pigs. Thirty eight female Yorkshire pigs were initially examined and six of them were selected as progenitors; half were more than 1.5 standard deviations (SD) above the mean for average daily gain (ADG) and backfat thickness (BFT), and the remaining half were more than 1.5 SD below the mean. These pigs were then mated to male Duroc pigs, and 200 F2 pig offspring were examined for the association of specific alleles with ADG and BFT. To confirm the specific markers identified in the initial analysis, associations of significant markers with economic traits were further examined in 228 additional performance-tested purebred pigs. Twenty-five microsatellite markers were significantly associated with either ADG or BFT, and among these, 17 were associated with both traits. The markers with the highest association to ADG were also associated with BFT. Our study reveals that specific markers could be used to predict economic significance, and confirms several quantitative trait loci (QTL) identified in previous studies. However, further analysis with more closely-spaced microsatellite markers is required to refine predictive values for economic traits and positions of QTL that are reliable for actual phenotypic prediction.

Alleles↗
PubMed2005

Molecular mapping of a quantitative trait locus for aluminum tolerance in wheat cultivar Atlas 66.

Genetic improvement of aluminum (Al) tolerance is one of the cost-effective solutions to improve wheat (Triticum aestivum) productivity in acidic soils. The objectives of the present study were to identify quantitative trait loci (QTL) for Al-tolerance and associated PCR-based markers for marker-assisted breeding utilizing cultivar Atlas 66. A population of recombinant inbred lines (RILs) from the cross Atlas 66/Century was screened for Al-tolerance by measuring root-growth rate during Al treatment in hydroponics and root response to hematoxylin stain of Al treatment. After 797 pairs of SSR primers were screened for polymorphisms between the parents, 131 pairs were selected for bulk segregant analysis (BSA). A QTL analysis based on SSR markers revealed one QTL on the distal region of chromosome arm 4DL where a malate transporter gene was mapped. This major QTL accounted for nearly 50% of the phenotypic variation for Al-tolerance. The SSR markers Xgdm125 and Xwmc331 were the flanking markers for the QTL and have the potential to be used for high-throughput, marker-assisted selection in wheat-breeding programs.

Aluminum↗
PubMed2005

Quantitative trait loci affecting delta13C and response to differential water availibility in Arabidopsis thaliana.

Phenotypic plasticity is an important response mechanism of plants to environmental heterogeneity. Here, we explored the genetic basis of plastic responses of Arabidopsis thaliana to water deficit by experimentally mapping quantitative trait loci (QTL) in two recombinant inbred populations (Cvi x Ler and Ler x Col). We detected genetic variation and significant genotype-by-environment interactions for many traits related to water use. We also mapped 26 QTL, including six for carbon isotope composition (delta13C). Negative genetic correlations between fruit length and fruit production as well as between flowering time and branch production were corroborated by QTL colocalization, suggesting these correlations are due to pleiotropy or physical linkage. Water-limited plants were more apically dominant with greater root:shoot ratios and higher delta13C (higher water-use efficiency) when compared to well-watered plants. Many of the QTL effects for these traits interacted significantly with the irrigation treatment, suggesting that the observed phenotypic plasticity is genetically based. We specifically searched for epistatic (QTL-QTL) interactions using a two-dimensional genome scan, which allowed us to detect epistasis regardless of additive genetic effects. We found several significant QTL-QTL interactions including three that exhibited environmental dependence. These results provide preliminary evidence for proposed genetic mechanisms underlying phenotypic plasticity.

Arabidopsis↗
PubMed2001

Genetic mapping of quantitative trait loci governing longevity of Caenorhabditis elegans in recombinant-inbred progeny of a Bergerac-BO x RC301 interstrain cross.

Recombinant-inbred populations, generated from a cross between Caenorhabditis elegans strains Bergerac-BO and RC301, were used to identify quantitative trait loci (QTL) affecting nematode longevity. Genotypes of young controls and longevity-selected worms (the last-surviving 1% from a synchronously aged population) were assessed at dimorphic transposon-specific markers by multiplex polymerase chain reaction. The power of genetic mapping was enhanced, in a novel experimental design, through map expansion by accrual of recombinations over several generations, internally controlled longevity selection from a genetically heterogeneous, homozygous population, and selective genotyping of extremely long-lived worms. Analysis of individual markers indicated seven life-span QTL, situated near markers on chromosomes I (tcbn2), III (stP127), IV (stP13), V (stP6, stP23, and stP128), and X (stP41). These loci were corroborated, and mapped with increased precision, by nonparametric interval mapping-which supported all loci implicated by single-marker analysis. In addition, a life-span QTL on chromosome II (stP100-stP196), was significant only by interval mapping. Congenic lines were constructed for the longevity QTL on chromosomes III and X, by backcrossing the Bergerac-BO QTL allele into an RC301 background with selection for flanking markers. Survival data for these lines demonstrated consistent and significant effects of each QTL on life span.

Alleles↗
PubMed2004

A simulation study concerning the effect of varying the residual phenotypic correlation on the power of bivariate quantitative trait loci linkage analysis.

The power of bivariate variance components (VC) linkage analysis is affected by the size and source of the phenotypic correlation between variables. In particular, several authors have suggested that the power to detect linkage is greatest when the quantitative trait locus (QTL) and residual sources of variation induce phenotypic covariation in opposite directions, and that this increase in power is greatest when unique environmental sources of variation induce covariation in the direction opposite to the QTL. The purpose of the present study was to investigate further the effect of varying the residual correlation between variables on the power to detect linkage in a bivariate variance components linkage analysis. Data were simulated for a biallelic QTL that pleiotropically influenced two variables. The power to detect linkage was calculated under a variety of situations in which the proportion of phenotypic covariance resulting from shared sources of variation and from unique sources of variation was varied. These simulations were performed for the case in which the QTL affected the two variables equally and also for the case in which the QTL made unequal contributions to each variable. Our results confirm that the power to detect QTLs in a bivariate test for linkage depends upon the size and source of the residual correlation between variables, being greatest when the QTL and unique environmental sources of variation induce phenotypic covariation in opposite directions. We also found that when the QTL affected the two variables unequally, the power to detect linkage increased markedly as the correlation between unique environmental sources of variation increased from 0.6 to 0.9. Similar results were obtained under a variety of genetic models, including when there were unequal allele frequencies and dominance at the QTL. We suggest that a promising strategy to increase the power to detect QTLs might be to collect data from variables where there is either good observational evidence (e.g., from multivariate structural equation modeling of twin data) or a sound theoretical argument that the QTL and environmental factors induce covariation in opposite directions.

Alleles↗
PubMed2001

Quantitative trait linkage analysis of the liability underlying a common oligogenic disease.

We utilized pedigree discriminant and factor analytic approaches to combine multivariate phenotypic information into a single liability phenotype in the isolate and general populations. We applied two-stage relative-pair quantitative trait linkage analysis to detect genetic contributions to variation in the resulting liability phenotypes. Linkage analysis revealed several regions of suggestive linkage in both the general and isolate populations, the majority of which appear in retrospect to be false positives. A likely explanation is an overall lack of power given that we tested hypotheses in data from only one replicate. However, it may be possible that a construct that ignores affection status when using liability-associated characteristics as indicators of this construct is not the most effective approach in modeling the liability underlying a complex phenotype.

Chromosome Mapping↗
PubMed2005

Haplotype-sharing analysis for alcohol dependence based on quantitative traits and the Mantel statistic.

Haplotype-based methods have become increasingly popular in the last decade because shared lengths in haplotypes can be used for disease localization. In this contribution, we propose a novel linkage-based haplotype-sharing approach for quantitative traits based on the class of Mantel statistics which is closely related to the weighted pair-wise correlation statistic. Because these statistics are known to be liberal, we propose a permutation test to evaluate significance. We applied the Mantel statistic to the autosomal data from the genome-wide scan of the Collaborative Study on the Genetics of Alcoholism with the Affymetrix Genotype 10 K array that was provided for the Genetic Analysis Workshop 14. Four regions on chromosome 4, 8, 16, and 20 showed p-values less than 0.005 with a minimum p-value of < 0.0001 on chromosome 16 (tsc0520638 at 72.8 cM). Three of these four regions located on chromosome 4, 16, and 20 have been reported previously in the Genetic Analysis Workshop 11.

Alcoholism↗
PubMed2004

A Leishmania major response locus identified by interval-specific congenic mapping of a T helper type 2 cell bias-controlling quantitative trait locus.

The propensity of naive CD4 T cells to become T helper (Th) type 2 cells correlates with susceptibility to infection by the protozoal parasite Leishmania major. Using genetic linkage analysis, we earlier identified Dice1 as a Th2 cell bias-controlling quantitative trait locus on chromosome 16. Using interval-specific congenic mapping, we now resolve Dice1 into two independent genetic loci, Dice1.1 and Dice1.2, which control Il4 expression from naive Th cells and thereby indirectly control Th2 cell bias. Interestingly, only one of the two congenic intervals containing Dice1.1 and Dice1.2, respectively, also contained an L. major response locus, indicating that L. major responsiveness can be insensitive to determinants that influence Th2 cell bias by controlling naive T cell Il4 expression. These results lay the groundwork for identifying the Dice1.1 and Dice1.2 genes controlling naive T cell Il4 expression and L. major responses, and for testing whether these control other Th2 cell-dependent processes such as worm expulsion, allergic asthma, and dermatitis.

Animals↗
PubMed2002

Quantitative trait loci on chromosomes 10 and 11 influencing mandible size of SMXA RI mouse strains.

Predicting the mandible size before the termination of growth of the maxillofacial bones is essential in pedodontics as well as for the predictions needed for genetic analysis. Here, Quantitative Trait Locus (QTL) analysis was used to detect the chromosomal regions responsible for the mandible length between the menton and gonion in an SMXA recombinant inbred strain of mice. Around the region 60 cM from the centromere in chromosome 10, the logarithm of the odds score showed a higher than suggestive level. Around the regions 13 cM and 16 cM in chromosome 11, two significant QTLs were detected. Analysis of genotypes from loci corresponding to those QTLs revealed a large mandible when the region between the markers Hba and D11Mit163 and D10Mit70 and D10Mit136 indicated the genotype from the A/J and SM/J alleles, respectively. These results suggest that the major gene(s) responsible for mandible length are located in these regions.

Animals↗
PubMed2004

Quantitative trait loci for obesity- and diabetes-related traits and their dietary responses to high-fat feeding in LGXSM recombinant inbred mouse strains.

Genetic variation in response to high-fat diets is important in understanding the recent secular trends that have led to increases in obesity and type 2 diabetes. The examination of quantitative trait loci (QTLs) for both obesity- and diabetes-related traits and their responses to a high-fat diet can be effectively addressed in mouse model systems, including LGXSM recombinant inbred (RI) mouse strains. A wide range of obesity- and diabetes-related traits were measured in animals from 16 RI strains with 8 animals of each sex fed a high- or low-fat diet from each strain. Marker associations were measured at 506 microsatellite markers spread throughout the mouse genome using a nested ANOVA. Locations with significant effects on the traits themselves and/or trait dietary responses were identified after correction for multiple comparisons by limiting the false detection rate. Nonsyntenic associations of marker genotypes were common at QTL locations so that the significant results were limited to loci still significant in multiple QTL models. We discovered 91 QTLs at 39 locations. Many of these locations (n = 31) also showed genetic effects on dietary response, typically because the loci produced significantly larger effects on the high-fat diet. Fat depot weights, leptin levels, and body weight at necropsy tended to map to the same locations and were responsible for a majority of the dietary response QTLs. Basal glucose levels and the response to glucose challenge mapped together in locations distinct from those affecting obesity. These QTL locations form a panel for further research and fine mapping of loci affecting obesity- and diabetes-related traits and their responses to high-fat feeding.

Animals↗
PubMed2001

Quantitative trait loci for femoral and lumbar vertebral bone mineral density in C57BL/6J and C3H/HeJ inbred strains of mice.

Significant differences in vertebral (9%) and femoral (50%) adult bone mineral density (BMD) between the C57BL/6J (B6) and C3H/HeJ (C3H) inbred strains of mice have been subjected to genetic analyses for quantitative trait loci (QTL). Nine hundred eighty-six B6C3F2 females were analyzed to gain insight into the number of genes that regulate peak BMD and their locations. Femurs and lumbar vertebrae were isolated from 4-month-old B6C3F2 females at skeletal maturity and then BMD was determined by peripheral quantitative computed tomography (pQCT). Estimates of BMD heritability were 83% for femurs and 72% for vertebrae. Genomic DNA from F2 progeny was screened for 107 polymerase chain reaction (PCR)-based markers discriminating B6 and C3H alleles on all 19 autosomes. The regression analyses of markers on BMD revealed ten chromosomes (1, 2, 4, 6, 11, 12, 13, 14, 16, and 18) carrying QTLs for femurs and seven chromosomes (1, 4, 7, 9, 11, 14, and 18) carrying QTLs for vertebrae, each with log10 of the odds ratio (LOD) scores of 2.8 or better. The QTLs on chromosomes (Chrs) 2, 6, 12, 13, and 16 were unique to femurs, whereas the QTLs on Chrs 7 and 9 were unique to vertebrae. When the two bone sites had a QTL on the same chromosome, the same marker had the highest, although different, LOD score. A pairwise comparison by analysis of variance (ANOVA) did not reveal significant gene x gene interactions between QTLs for either bone site. BMD variance accounted for by individual QTLs ranged from 1% to 10%. Collectively, the BMD QTLs for femurs accounted for 35.1% and for vertebrae accounted for 23.7 % of the F2 population variances in these bones. When mice were homozygous c3/c3 in the QTL region, 8 of the 10 QTLs increased, while the remaining two QTLs on Chrs 6 and 12 decreased, femoral BMD. Similarly, when mice were homozygous c3/c3 in the QTL region for the vertebrae, five of the seven QTLs increased, while two QTLs on Chrs 7 and 9 decreased, BMD. These findings show the genetic complexity of BMD with multiple genes participating in its regulation. Although 5 of the 12 QTLs are considered to be skeleton-wide loci and commonly affect both femurs and vertebrae, each of the bone sites also exhibited unique QTLs. Thus, the BMD phenotype can be partitioned into its genetic components and the effects of these loci on normal bone biology can be determined. Importantly, the BMD QTLs that we have identified are in regions of the mouse genome that have known human homology, and the QTLs will become useful experimental tools for mechanistic and therapeutic analyses of bone regulatory genes.

Alleles↗
PubMed2004

Comparative genetic analysis of quantitative traits in sunflower (Helianthus annuus L.) 3. Characterisation of QTL involved in resistance to Sclerotinia sclerotiorum and Phoma macdonaldi.

One hundred and fifty F(2)-F(3) families from a cross between two inbred sunflower lines FU and PAZ2 were used to map quantitative trait loci (QTL) for resistance to white rot (Sclerotinia sclerotiorum) attacks of terminal buds and capitula, and black stem ( Phoma macdonaldii). A genetic linkage map of 18 linkage groups with 216 molecular markers spanning 1,937 cM was constructed. Disease resistances were measured in field experiments for S. sclerotiorum and under controlled conditions for P. macdonaldii. For resistance to S. sclerotiorum terminal bud attack, seven QTL were identified, each explaining less than 10% of phenotypic variance. For capitulum attack by this parasite, there were four QTL (each explaining up to 20% of variation) and for P. macdonaldii resistance, four QTL were identified, each having effects of up to 16%. The S. sclerotiorum capitulum resistance QTL were compared with those reported previously and it was concluded that resistance to this disease is governed by a considerable number of QTL, located on almost all the sunflower linkage groups.

Ascomycota↗
PubMed2001

Quantitative trait loci affecting clinical mastitis and somatic cell count in dairy cattle.

Norway has a field recording system for dairy cattle that includes recording of all veterinary treatments on an individual animal basis from 1978 onwards. Application of these data in a genome search for quantitative trait loci (QTL) verified genome-wise significant QTL affecting clinical mastitis on Chromosome (Chr) 6. Additional putative QTL for clinical mastitis were localized to Chrs. 3, 4, 14, and 27. The comprehensive field recording system includes information on somatic cell count as well. This trait is often used in selection against mastitis when direct information on clinical mastitis is not available. The absence of common QTL positions for the two traits in our study indicates that the use of somatic cell count data in QTL studies aimed for reducing the incidence of mastitis should be carefully evaluated.

Animals↗
PubMedDate not supplied

Identification of chromosomes associated with dental caries susceptibility using quantitative trait locus analysis in mice.

Dental caries is a multifactorial, infectious disease with little known about the host genetic factors influencing susceptibility. This study aimed to identify the major candidate chromosomes for dental caries susceptibility and to detect the relevant regions within these. Quantitative trait locus (QTL) analysis was performed on genetic crosses of C3H/HeJ (caries-resistant) and C57BL/6J (caries-susceptible) mice inoculated with Streptococcus mutans serotype C. In a genomewide scan, three suggestive QTLs were detected on chromosomes 1, 2, and 7, one significant QTL was found on chromosome 2, and one highly significant QTL was detected on chromosome 8. The likelihood ratio statistic (LRS) was raised around the marker D1Mit21 in the middle region of chromosome 1, between D2Mit255 and D2Mit311 in the distal region of chromosome 2, and the region distal to D7Mit31 on chromosome 7. A significant QTL was located between the markers D2Mit237 and D2Mit101 on chromosome 2. The LRS was highly significantly raised between markers D8Mit208 and D8Mit280 on chromosome 8, and exceeded a highly significant level between markers D8Mit211 and D8Mit280. These results suggest that major gene(s) responsible for dental caries susceptibility or resistance are located in one or more of these regions.

Animals↗
PubMed2006

Quantitative trait loci analysis of mice administered the methionine-choline deficient dietary model of experimental steatohepatitis.

BACKGROUND: Nonalcoholic steatohepatitis (NASH) is a common disease with a poorly understood etiology, and the methionine-choline-deficient (MCD) diet is a nutritional model of NASH. Quantitative trait loci (QTL) analysis is a standard method for chromosomal mapping of polygenic disease traits. The purpose of this study is to administer mice an MCD diet in order to determine the strain-specific susceptibility for developing steatohepatitis, and to apply a computational methodology of QTL analysis to identify associated chromosomal susceptibility loci. METHODS: Inbred mice were fed an MCD diet and alanine aminotransferase (ALT), hepatic triglycerides, liver weight, and weight loss were measured as phenotypic markers of steatohepatitis. RESULTS: A/J mice developed the highest ALT and hepatic triglyceride levels. Using linear regression analysis, gene loci affecting serum ALT levels were identified on four chromosomes, and four loci that affect liver weight were also identified. In contrast, no QTLs for hepatic triglycerides or body weight were identified. Of note, loci for ALT and liver weight co-localized to proximal segments of chromosomes 2 and 15, in regions previously identified as QTLs for liver fibrosis. CONCLUSIONS: These data indicate that experimental steatohepatitis is a polygenic disease with genes determining ALT, liver weight, and liver fibrosis.

Animals↗
PubMed2000

Identification of quantitative trait loci for serum cholesterol levels in stroke-prone spontaneously hypertensive rats.

The stroke-prone spontaneously hypertensive rat (SHRSP) has been reported to show significantly lower levels of serum total cholesterol than the normotensive control strain Wistar-Kyoto rat (WKY). Because selective inbreeding was conducted for stroke proneness, this concomitantly inherited characteristic of SHRSP may play some pathophysiological role in stroke. We evaluated the genetic determinants of the cholesterol trait by estimating heritability and subsequently by undertaking a genome-wide screen with 161 genetic markers in F(2) progeny involving SHRSP and WKY (104 male and 106 female rats). Three quantitative trait loci (QTLs) were detected on rat chromosomes 5, 7, and 15. Markers from the linked region on chromosome 15 indicated significant evidence of linkage with a maximal log of the odds (LOD) score of 7.7, whereas those on chromosomes 5 and 7 cosegregated with the trait in a sex-specific manner (the QTL close to genetic marker D5 Mit5 reached an LOD score of 7.3 in males, and that close to D7 Mit10 reached an LOD score of 3.2 in females). The male-specific QTL on chromosome 5 appeared to overlap with previously reported QTLs for stroke-associated phenotypes, but an identical gene (or genes) appeared unlikely to control these and the cholesterol traits simultaneously. In the present study, serum cholesterol levels were shown to be highly genetically determined in SHRSP (the heritability estimates are 76% in males and 83% in females), and 3 QTLs with substantial effects were identified. Further work, however, is required to clarify whether the cholesterol trait is related to the etiology of stroke or has been retained by chance through the inbreeding process in SHRSP.

Alleles↗