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Bayes factors for detection of quantitative trait loci.

A fundamental issue in quantitative trait locus (QTL) mapping is to determine the plausibility of the presence of a QTL at a given genome location. Bayesian analysis offers an attractive way of testing alternative models (here, QTL vs. no-QTL) via the Bayes factor. There have been several numerical approaches to computing the Bayes factor, mostly based on Markov Chain Monte Carlo (MCMC), but these strategies are subject to numerical or stability problems. We propose a simple and stable approach to calculating the Bayes factor between nested models. The procedure is based on a reparameterization of a variance component model in terms of intra-class correlation. The Bayes factor can then be easily calculated from the output of a MCMC scheme by averaging conditional densities at the null intra-class correlation. We studied the performance of the method using simulation. We applied this approach to QTL analysis in an outbred population. We also compared it with the Likelihood Ratio Test and we analyzed its stability. Simulation results were very similar to the simulated parameters. The posterior probability of the QTL model increases as the QTL effect does. The location of the QTL was also correctly obtained. The use of meta-analysis is suggested from the properties of the Bayes factor.

Algorithms↗

PubMed2005

A genome-wide scan for quantitative trait loci linked to obesity phenotypes among West Africans.

OBJECTIVE: To identify quantitative trait loci (QTL) for three obesity phenotypes: body mass index (BMI), fat mass (FM) and percent body fat (PBF) in West Africans with type 2 diabetes (T2DM). DESIGN: An affected sibling pair (ASP) design, in which both siblings had T2DM. Obesity was analyzed as a quantitative trait using a variance components approach. SUBJECTS: Sib-pairs affected with T2DM from the Africa America Diabetes Mellitus (AADM) study, comprising 321 sibling pairs and 36 half-sibling pairs. MEASUREMENTS: Weight was measured on an electronic scale to the nearest 0.1 kg, and height was measured with a stadiometer to the nearest 0.1 cm. Body composition was estimated using bioelectric impedance analysis (BIA). Genotyping was carried out at the Center for Inherited Disease Research (CIDR) with a panel of 390 trinucleotide and tetranucleotide repeats. RESULTS: The obesity-related phenotype showing the strongest linkage evidence was PBF on chromosome 2 (LOD 3.30 at 72.6 cM, marker D2S739). Suggestive linkage to FM was found on chromosomes 2 (LOD 2.56 at 80.4 cM) and 5 (LOD 2.25 at 98 cM, marker D5S1725). The highest LOD score for BMI was 1.68 (chromosome 4, 113.8 cM). The areas of linkage for the three phenotypes showed some clustering as all three phenotypes were linked to the same regions of 2p13 and 5q14, and our study replicated linkage evidence for several regions previously reported in other studies. CONCLUSION: We obtained evidence for several QTLs on chromosome 2, 4 and 5 to three obesity phenotypes. This study provides data on the genetics of obesity in populations that are currently under represented in the global effort directed at understanding the pathophysiology of excess adiposity in free living individuals.

Adipose Tissue↗

PubMed2003

Genome-wide searches for blood pressure quantitative trait loci in the stroke-prone spontaneously hypertensive rat of a Japanese colony.

OBJECTIVES: Although several quantitative trait loci for blood pressure have been reported in stroke-prone spontaneously hypertensive rats (SHRSP), the results are not always concordant among different crosses. To evaluate potential confounding factors in linkage analysis, we performed genome-wide screens in F2 populations derived from SHRSP and Wistar-Kyoto rats of a Japanese colony. METHODS: Two F cohorts were independently produced: F2-1 (110 male and 110 female rats), and F2-2 (174 male and 184 female rats). Blood pressure was measured longitudinally (from 2 to 5 months of age and 1 month after salt-loading) in F2-1, while it was measured at 13 weeks of age in F2-2. Subsequent to an initial screen with 251 markers in F2-1 male progeny, 170 markers were selected and characterized in the remaining populations. RESULTS: When 578 rats were analyzed together, markers from five chromosomal regions showed significant linkage to blood pressure at 13 weeks of age. The strongest and the most consistent linkage was found on rat chromosome 1 (a maximal log of the odds score reached 8.3). In the other regions, the degree of linkage was more prominent in either of sexes. Some evidence of age-specific and sex-specific linkage was detected in five additional regions in the F2-1 cohort. In the Japanese colony, however, there was no significant linkage to several chromosomal regions previously reported in other SHRSP colonies. CONCLUSIONS: Our data provide solid evidence of a chromosome-1 linkage and demonstrate the importance of aging, sex, and dietary manipulation in linkage analysis. Also, the combination of parental rat strains seems to be critical when searching for blood pressure quantitative trait loci.

Aging↗

PubMed1999

Confirmed quantitative trait loci for fatness and growth on pig chromosome 4.

Quantitative trait loci (QTLs) with large effects on fatness and growth have been identified previously on pig chromosome 4 in an intercross between the European wild pig and Large White domestic pigs. Two F2 sows, heterozygous for the actual chromosome region, were backcrossed to a Large White boar, and two backcross (BC1) boars were in turn backcrossed to Large White/Landrace sows. One of the boars was heterozygous for an intact wild pig-derived QTL region, whereas the other carried a recombinant haplotype. A total of 85 BC2 animals were produced. Phenotypical measurements included daily weight gain, ultrasonic measurements of fat depth at 70 and 90 kg and several carcass traits. QTL segregation was deduced using 15 markers previously assigned to chromosome 4. Highly significant QTL effects were observed on all fatness traits and on the length of the carcass. A small but significant effect on growth was also observed. The results confirm the presence of one or more QTLs on chromosome 4 affecting fatness and growth. There was a good agreement between the estimates of QTL effects in the F2 and BC2 generations. The results from the recombinant sire family allowed us to map the major QTL effect distal to the recombination breakpoint. We propose that this confirmed QTL with a major effect on fatness is designated FAT1.

Adipose Tissue↗

PubMed2000

A primary screen of the bovine genome for quantitative trait loci affecting twinning rate.

An autosomal genome scan for quantitative trait loci (QTL) affecting twinning rate was carried out in the Norwegian Cattle population. Suggestive QTL were detected on Chromosomes (Chr) 5, 7, 12, and 23. Among these, the QTL positions on both Chr 5 and Chr 23 are strongly supported by literature in the field. Our results also confirm previous mapping of a QTL for twinning to Chr 7, but definitely suggest a different location of the QTL on this chromosome. The most convincing QTL peak was observed for a region in the middle part of Chr 5 close to the insulin-like growth factor 1 (IGF1) gene. Since IGF1 plays an important role in the regulation of folliculogenesis, a mutation search was performed by sequencing more than 3.5 kb of the gene in actual families. The sequencing revealed three polymorphisms in noncoding regions of the gene that will be important in fine structure mapping and characterization of the QTL.

Animals↗

PubMed2000

A transmission/disequilibrium test approach to screen for quantitative trait loci in two selected lines of large white pigs.

Pedigree and marker data from a multiple-generation pig selection experiment have been analysed to screen for loci affecting quantitative traits (QTL). Pigs from a base population were selected either for low backfat thickness at fixed live weight (L-line) or high live weight at fixed age (F-line). Selection was based on single-trait own performance and DNA was available on selected individuals only. Genotypes for three marker loci with known positions on chromosome 4 were available. The transmission/disequilibrium test (TDT) was originally described in human genetics to test for linkage between a genetic marker and a disease-susceptibility locus, in the presence of association. Here, we adapt the TDT to test for linkage between a marker and QTL favoured by selection, and for linkage disequilibrium between them in the base population. The a priori unknown distribution of the test statistic under the null hypothesis, no linkage, was obtained via Monte Carlo simulation. Significant TDT statistics were found for markers AFABP and SW818 in the F-line, indicating the presence of a closely linked QTL affecting growth performance. In the L-line, none of the markers studied showed significance. This study emphasizes the potential of the TDT as a quick and simple approach to screen for QTL in situations where marker genotypes are available on selected individuals. The results suggest that previously identified QTL in crosses of genetically diverse breeds may also segregate in commercial selection lines.

Animals↗

PubMed2004

Mapping multiple Quantitative Trait Loci by Bayesian classification.

We developed a classification approach to multiple quantitative trait loci (QTL) mapping built upon a Bayesian framework that incorporates the important prior information that most genotypic markers are not cotransmitted with a QTL or their QTL effects are negligible. The genetic effect of each marker is modeled using a three-component mixture prior with a class for markers having negligible effects and separate classes for markers having positive or negative effects on the trait. The posterior probability of a marker's classification provides a natural statistic for evaluating credibility of identified QTL. This approach performs well, especially with a large number of markers but a relatively small sample size. A heat map to visualize the results is proposed so as to allow investigators to be more or less conservative when identifying QTL. We validated the method using a well-characterized data set for barley heading values from the North American Barley Genome Mapping Project. Application of the method to a new data set revealed sex-specific QTL underlying differences in glucose-6-phosphate dehydrogenase enzyme activity between two Drosophila species. A simulation study demonstrated the power of this approach across levels of trait heritability and when marker data were sparse.

Animals↗

PubMed2002

Mapping quantitative trait loci controlling seed longevity in rice ( Oryza sativa L.).

Quantitative trait loci (QTLs) controlling seed longevity in rice were identified using 98 backcross inbred lines (BILs) derived from a cross between a japonica variety Nipponbare and an indica variety Kasalath. Seeds of each BIL were kept for 12 months at 30 degrees C in dry conditions to promote loss of viability. To measure seed longevity, we performed an additional aging-processing treatment for 2 months at 30 degrees C maintaining seeds at 15% moisture content. We measured the germination percent of these treated seeds at 25 degrees C for 7 days as the degree of seed longevity. The germination of BILs ranged from 0 to 100% with continuous variation. Three putative QTLs for seed longevity, qLG-2, qLG-4 and qLG-9, were detected on chromosome 2, 4 and 9, respectively. Kasalath alleles increased the seed longevity at these QTLs. The QTL with the largest effect, qLG-9, explained 59.5% of total phenotypic variation in BILs. The other two QTLs, qLG-2 and qLG-4, explained 13.4 and 11.6% of the total phenotypic variation, respectively. We also verified the effect of the Kasalath allele of qLG-9 using chromosome segment substitution lines. Furthermore, QTLs for seed dormancy were identified on chromosomes 1, 3, 5, 7 and 11. Based on the comparison of the chromosomal location of QTLs for seed longevity and seed dormancy, these traits seem to be controlled by different genetic factors.

Journal Article↗

PubMed2001

Mapping quantitative trait loci in oligogenic models.

We discuss strategies for mapping quantitative trait loci with emphasis on certain issues of study design that have recently received attention: e.g. genotyping only selected pedigrees and the comparative value of large pedigrees versus sib pairs. We use a standard variance components model and a parametrization of the genetic effects in which the 'segregation' parameters are locally orthogonal to the 'linkage' parameters. This permits simple explicit expressions for the expectation of the score statistic, which we use to compare the power of different strategies. We also discuss robustness of the score statistic.

Journal Article↗

PubMed1994

Precision mapping of quantitative trait loci.

Adequate separation of effects of possible multiple linked quantitative trait loci (QTLs) on mapping QTLs is the key to increasing the precision of QTL mapping. A new method of QTL mapping is proposed and analyzed in this paper by combining interval mapping with multiple regression. The basis of the proposed method is an interval test in which the test statistic on a marker interval is made to be unaffected by QTLs located outside a defined interval. This is achieved by fitting other genetic markers in the statistical model as a control when performing interval mapping. Compared with the current QTL mapping method (i.e., the interval mapping method which uses a pair or two pairs of markers for mapping QTLs), this method has several advantages. (1) By confining the test to one region at a time, it reduces a multiple dimensional search problem (for multiple QTLs) to a one dimensional search problem. (2) By conditioning linked markers in the test, the sensitivity of the test statistic to the position of individual QTLs is increased, and the precision of QTL mapping can be improved. (3) By selectively and simultaneously using other markers in the analysis, the efficiency of QTL mapping can be also improved. The behavior of the test statistic under the null hypothesis and appropriate critical value of the test statistic for an overall test in a genome are discussed and analyzed. A simulation study of QTL mapping is also presented which illustrates the utility, properties, advantages and disadvantages of the method.

Chromosome Mapping↗

PubMed2006

A genome-wide linkage scan for quantitative trait loci underlying obesity related phenotypes in 434 Caucasian families.

To identify quantitative trait loci (QTLs) that contribute to obesity, we performed a large-scale whole genome linkage scan (WGS) involving 4,102 individuals from 434 Caucasian families. The most pronounced linkage evidence was found at the genomic region 20p11-12 for fat mass (LOD = 3.31) and percentage fat mass (PFM) (LOD = 2.92). We also identified several regions showing suggestive linkage signals (threshold LOD = 1.9) for obesity phenotypes, including 5q35, 8q13, 10p12, and 17q11.

Family↗

PubMed2003

Functional mapping for quantitative trait loci governing growth rates: a parametric model.

Are there-specific quantitative trait loci (QTL) governing growth rates in biology? This is emerging as an exciting but challenging question for contemporary developmental biology, evolutionary biology, and plant and animal breeding. In this article, we present a new statistical model for mapping QTL underlying age-specific growth rates. This model is based on the mechanistic relationship between growth rates and ages established by a variety of mathematical functions. A maximum likelihood approach, implemented with the EM algorithm, is developed to provide the estimates of QTL position, growth parameters characterized by QTL effects, and residual variances and covariances. Based on our model, a number of biologically important hypotheses can be formulated concerning the genetic basis of growth. We use forest trees as an example to demonstrate the power of our model, in which a QTL for stem growth diameter growth rates is successfully mapped to a linkage group constructed from polymorphic markers. The implications of the new model are discussed.

Age Factors↗

PubMed2006

Mapping temporally varying quantitative trait loci in time-to-failure experiments.

Existing methods for mapping quantitative trait loci (QTL) in time-to-failure experiments assume that the QTL effect is constant over the course of the study. This assumption may be violated when the gene(s) underlying the QTL are up- or downregulated on a biologically meaningful timescale. In such situations, models that assume a constant effect can fail to detect QTL in a whole-genome scan. To investigate this possibility, we utilize an extension of the Cox model (EC model) within an interval-mapping framework. In its simplest form, this model assumes that the QTL effect changes at some time point t0 and follows a linear function before and after this change point. The approximate time point at which this change occurs is estimated. Using simulated and real data, we compare the mapping performance of the EC model to the Cox proportional hazards (CPH) model, which explicitly assumes a constant effect. The results show that the EC model detects time-dependent QTL, which the CPH model fails to detect. At the same time, the EC model recovers all of the QTL the CPH model detects. We conclude that potentially important QTL may be missed if their time-dependent effects are not accounted for.

Chromosome Mapping↗

PubMed2006

An empirical Bayes method for updating inferences in analysis of quantitative trait loci using information from related genome scans.

Individual genome scans for quantitative trait loci (QTL) mapping often suffer from low statistical power and imprecise estimates of QTL location and effect. This lack of precision yields large confidence intervals for QTL location, which are problematic for subsequent fine mapping and positional cloning. In prioritizing areas for follow-up after an initial genome scan and in evaluating the credibility of apparent linkage signals, investigators typically examine the results of other genome scans of the same phenotype and informally update their beliefs about which linkage signals in their scan most merit confidence and follow-up via a subjective-intuitive integration approach. A method that acknowledges the wisdom of this general paradigm but formally borrows information from other scans to increase confidence in objectivity would be a benefit. We developed an empirical Bayes analytic method to integrate information from multiple genome scans. The linkage statistic obtained from a single genome scan study is updated by incorporating statistics from other genome scans as prior information. This technique does not require that all studies have an identical marker map or a common estimated QTL effect. The updated linkage statistic can then be used for the estimation of QTL location and effect. We evaluate the performance of our method by using extensive simulations based on actual marker spacing and allele frequencies from available data. Results indicate that the empirical Bayes method can account for between-study heterogeneity, estimate the QTL location and effect more precisely, and provide narrower confidence intervals than results from any single individual study. We also compared the empirical Bayes method with a method originally developed for meta-analysis (a closely related but distinct purpose). In the face of marked heterogeneity among studies, the empirical Bayes method outperforms the comparator.

Chromosome Mapping↗

PubMed2006

Mapping quantitative trait loci in noninbred mosquito crosses.

The identification of genes that affect quantitative traits has been of great interest to geneticists for many decades, and many statistical methods have been developed to map quantitative trait loci (QTL). Most QTL mapping studies in experimental organisms use purely inbred lines, where the two homologous chromosomes in each individual are identical. As a result, many existing QTL mapping methods developed for experimental organisms are applicable only to genetic crosses between inbred lines. However, it may be difficult to obtain inbred lines for certain organisms, e.g., mosquitoes. Although statistical methods for QTL mapping in outbred populations, e.g., humans, can be applied for such crosses, these methods may not fully take advantage of the uniqueness of these crosses. For example, we can generally assume that the two grandparental lines are homozygous at the QTL of interest, but such information is not be utilized through methods developed for outbred populations. In addition, mating types and phases can be relatively easy to establish through the analysis of adjacent markers due to the large number of offspring that can be collected, substantially simplifying the computational need. In this article, motivated by a mosquito intercross experiment involving two selected lines that are not genetically homozygous across the genome, we develop statistical methods for QTL mapping for genetic crosses involving noninbred lines. In our procedure, we first infer parental mating types and use likelihood-based methods to infer phases in each parent on the basis of genotypes of offspring and one parent. A hidden Markov model is then employed to estimate the number of high-risk alleles at marker positions and putative QTL positions between markers in each offspring, and QTL mapping is finally conducted through the inferred QTL configuration across all offspring in all crosses. The performance of the proposed methods is assessed through simulation studies, and the usefulness of this method is demonstrated through its application to a mosquito data set.

Alleles↗

PubMed1994

Quantitative trait loci mapping of three loci controlling morphine preference using inbred mouse strains.

Quantitative trait loci mapping was used to identify the chromosomal location of genes which contribute to oral morphine preference (in a two-bottle choice paradigm) of C57BL/6J mice, compared to DBA/2J mice. An F2 intercross of these two strains (606 mice) was phenotyped for morphine preference and those mice demonstrating extreme values for morphine consumption (the highest and lowest 7.7%) were genotyped for 157 murine microsatellite polymorphisms. Maximum likelihood methods revealed three loci on murine chromosomes 1, 6 and 10 which are responsible for nearly 85% of the genetic variance observed between the two parental strains.

Animals↗

PubMed2003

Molecular mapping of capsaicinoid biosynthesis genes and quantitative trait loci analysis for capsaicinoid content in Capsicum.

Quantitative variation in the accumulation of two major capsaicinoids responsible for pungency in the fruit of chile peppers, capsaicin and dihydrocapsaicin, was analyzed in a cross between the non-pungent Capsicum annuum parent cv. Maor and a pungent Capsicum frutescens parent, accession BG 2816. In order to identify quantitative trait loci (QTLs) for capsaicinoid content, we employed the bulked segregant analysis method and screened bulked DNA from F2 individuals at the extremes of the distribution of capsaicinoid content with RAPD primers. Screening with 400 primers allowed the identification of three loci that were polymorphic between the bulks. These RAPD markers were converted to SCARs and subsequently mapped with additional RFLP markers to chromosome 7 of pepper. QTL interval analysis for individual and total capsaicinoid content identified a major QTL, termed cap, which explained 34-38% of the phenotypic variation for this trait in two growing environments. For all measurements, the allele of the pungent parent BG 2816 at cap contributed to the increased level of pungency. To determine whether known structural genes in the pathway could define a candidate for this QTL, 12 clones obtained from differentially expressed transcripts from placental tissue in pungent peppers were also mapped. None of them had a significant effect on this trait, nor did the allelic state at the locus C, the on/off switch for pungency in pepper, located on chromosome 2. The identity of cap and its effect on capsaicin content in other backgrounds will be addressed in future studies.

Alleles↗

PubMed2006

Heritability and tissue specificity of expression quantitative trait loci.

Variation in gene expression is heritable and has been mapped to the genome in humans and model organisms as expression quantitative trait loci (eQTLs). We applied integrated genome-wide expression profiling and linkage analysis to the regulation of gene expression in fat, kidney, adrenal, and heart tissues using the BXH/HXB panel of rat recombinant inbred strains. Here, we report the influence of heritability and allelic effect of the quantitative trait locus on detection of cis- and trans-acting eQTLs and discuss how these factors operate in a tissue-specific context. We identified several hundred major eQTLs in each tissue and found that cis-acting eQTLs are highly heritable and easier to detect than trans-eQTLs. The proportion of heritable expression traits was similar in all tissues; however, heritability alone was not a reliable predictor of whether an eQTL will be detected. We empirically show how the use of heritability as a filter reduces the ability to discover trans-eQTLs, particularly for eQTLs with small effects. Only 3% of cis- and trans-eQTLs exhibited large allelic effects, explaining more than 40% of the phenotypic variance, suggestive of a highly polygenic control of gene expression. Power calculations indicated that, across tissues, minor differences in genetic effects are expected to have a significant impact on detection of trans-eQTLs. Trans-eQTLs generally show smaller effects than cis-eQTLs and have a higher false discovery rate, particularly in more heterogeneous tissues, suggesting that small biological variability, likely relating to tissue composition, may influence detection of trans-eQTLs in this system. We delineate the effects of genetic architecture on variation in gene expression and show the sensitivity of this experimental design to tissue sampling variability in large-scale eQTL studies.

Alleles↗