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PubMed2005

Costly resistance to parasitism: evidence from simultaneous quantitative trait loci mapping for resistance and fitness in Tribolium castaneum.

Information on the molecular basis of resistance and the evolution of resistance is crucial to an understanding of the appearance, spread, and distribution of resistance genes and of the mechanisms of host adaptation in natural populations. One potential important genetic constraint for the evolution of resistance is fitness cost associated with resistance. To determine whether host resistance to parasite infection is associated with fitness costs, we conducted simultaneous quantitative trait loci (QTL) mapping of resistance to parasite infection and fitness traits using the red flour beetle (Tribolium castaneum) and the tapeworm parasite (Hymenolepis diminuta) system in two independent segregating populations. A genome-wide QTL scan using amplified fragment length polymorphism (AFLP) markers revealed three QTL for beetle resistance to tapeworm infection. These three QTL account for 44-58% variance in beetle infection intensity. We identified five QTL for fecundity and five QTL for egg-to-adult viability, which accounted for 36-57% and 36-49%, respectively, of the phenotypic variance in fecundity and egg-to-adult viability. The three QTL conferring resistance were colocalized with the QTL affecting beetle fitness. The genome regions that contain the QTL for parasite resistance explained the majority of the variance in fecundity and egg-to-adult viability in the mapping populations. Colocalization of QTL conferring resistance to parasite infection and beetle fitness may result from the pleiotropic effects of the resistance genes on host fitness or from tight linkages between resistance genes and adverse deleterious mutations. Therefore, our results provide evidence that the genome regions conferring resistance to tapeworm infection are partially responsible for fitness costs in the resistant beetle populations.

Animals↗
PubMed2004

Admixture in the Hispanics of the San Luis Valley, Colorado, and its implications for complex trait gene mapping.

Hispanic populations are a valuable resource that can and should facilitate the identification of complex trait genes by means of admixture mapping (AM). In this paper we focus on a particular Hispanic population living in the San Luis Valley (SLV) in Southern Colorado. We used a set of 22 Ancestry Informative Markers (AIMs) to describe the admixture process and dynamics in this population. AIMs are defined as genetic markers that exhibit allele frequency differences between parental populations >or=30%, and are more informative for studying admixed populations than random markers. The ancestral proportions of the SLV Hispanic population are estimated as 62.7 +/- 2.1% European, 34.1 +/- 1.9% Native American and 3.2 +/- 1.5% West African. We also estimated the ancestral proportions of individuals using these AIMs. Population structure was demonstrated by the excess association of unlinked markers, the correlation between estimates of admixture based on unlinked marker sets, and by a highly significant correlation between individual Native American ancestry and skin pigmentation (R2= 0.082, p < 0.001). We discuss the implications of these findings in disease gene mapping efforts.

Adult↗
PubMed2003

FXR and ABCG5/ABCG8 as determinants of cholesterol gallstone formation from quantitative trait locus mapping in mice.

BACKGROUND & AIMS: Cholesterol gallstone formation is a complex genetic trait. To identify additional cholesterol gallstone susceptibility loci, we performed a quantitative trait locus analysis using an intercross of PERA/Ei and I/LnJ inbred strains of mice. METHODS: Mice of both sexes were examined for gallstone weight and evaluated according to a scoring system for the physical chemistry of cholelithiasis during feeding of a lithogenic diet. Intercross offspring were genotyped, and linkage analysis was performed by interval mapping. Differences in messenger RNA expression of positional candidate genes were determined using reverse-transcription and real-time polymerase chain reaction. RESULTS: We identified significant loci associated with gallstone weight on chromosomes 10 and 4, named Lith7 and Lith8, respectively (both susceptibility alleles conferred by strain I/LnJ). Positional candidate genes with higher expression in I/LnJ mice are Fxr (official symbol, Nr1h4), encoding the nuclear bile salt receptor, on chromosome 10 and Shp1 (official symbol, Nr0b2), encoding the small heterodimer partner 1, on chromosome 4. A significant locus associated with gallstone score on chromosome 17, named Lith9 (susceptibility allele conferred by strain PERA/Ei), colocalizes with the genes Abcg5 and Abcg8 that encode the canalicular cholesterol transporter. Higher hepatic messenger RNA expression of Abcg5 and Abcg8 in strain PERA/Ei correlates positively with higher biliary cholesterol levels. CONCLUSIONS: Our findings suggest a primary role of the nuclear bile salt receptor FXR and the canalicular cholesterol transporter ABCG5/ABCG8 in the genetic susceptibility and pathogenesis of cholesterol cholelithiasis in these strains of inbred mice.

ATP Binding Cassette Transporter, Subfamily G, Mem↗
PubMed1997

Mapping quantitative trait loci for open-field behavior in mice.

By performing a whole genome screen in an F2 intercross of two strains of mice (A/J and C57BL/6J), which differ markedly in their behavioral response to a brightly lit open field (O-F), we have mapped several quantitative trait loci (QTL) for this complex behavioral phenotype. QTL on chromosomes 1 and 10 were identified that affect both initial ambulation in the O-F (initial "response to novelty" ambulation) (lod of 7.1 and 8.8, respectively) and vertical rearings (lod of 4.5 and 8.5, respectively). For habituated O-F behavior, QTL were identified on chromosomes 3 and 10 for ambulation (lod of 4.1 and 14.7, respectively) and on chromosomes 1, 10, and 19 for vertical rearings (lod of 5.8, 6.0, and 4.7, respectively). The QTL on chromosome 1 (near D1Mit116; 101 cM) was specific for initial O-F ambulation behavior, whereas the QTL on chromosome 10 (near D10Mit237; 74 cM) affected both initial and habituated rearing behavior. Additional suggestive QTL (lod, > 2.8) were mapped to chromosomes 1, 8, 11, 15, and 19. The QTL on chromosomes 1, 10, and 19 individually explain from 3.2 to 12.7%. Collectively, the multiple independent QTL explain from 16.3 to 24.1% of the F2 population's phenotypic variance, depending on the trait. These identified QTL should prove useful for dissecting the genetic and behavioral dimensions of O-F behavior, fostering an understanding of individual differences.

Animals↗
PubMed2006

Mapping quantitative trait loci by an extension of the Haley-Knott regression method using estimating equations.

The Haley-Knott (HK) regression method continues to be a popular approximation to standard interval mapping (IM) of quantitative trait loci (QTL) in experimental crosses. The HK method is favored for its dramatic reduction in computation time compared to the IM method, something that is particularly important in simultaneous searches for multiple interacting QTL. While the HK method often approximates the IM method well in estimating QTL effects and in power to detect QTL, it may perform poorly if, for example, there is strong epistasis between QTL or if QTL are linked. Also, it is well known that the estimation of the residual variance by the HK method is biased. Here, we present an extension of the HK method that uses estimating equations based on both means and variances. For normally distributed phenotypes this estimating equation (EE) method is more efficient than the HK method. Furthermore, computer simulations show that the EE method performs well for very different genetic models and data set structures, including nonnormal phenotype distributions, nonrandom missing data patterns, varying degrees of epistasis, and varying degrees of linkage between QTL. The EE method retains key qualities of the HK method such as computational speed and robustness against nonnormal phenotype distributions, while approximating the IM method better in terms of accuracy and precision of parameter estimates and power to detect QTL.

Chromosome Mapping↗
PubMed2005

On normality, ethnicity, and missing values in quantitative trait locus mapping.

BACKGROUND: This paper deals with the detection of significant linkage for quantitative traits using a variance components approach. Microsatellite markers were obtained for the Genetic Analysis Workshop 14 Collaborative Study on the Genetics of Alcoholism data. Ethnic heterogeneity, highly skewed quantitative measures, and a high rate of missing values are all present in this dataset and well known to impact upon linkage analysis. This makes it a good candidate for investigation. RESULTS: As expected, we observed a number of changes in LOD scores, especially for chromosomes 1, 7, and 18, along with the three factors studied. A dramatic example of such changes can be found in chromosome 7. Highly significant linkage to one of the quantitative traits became insignificant when a proper normalizing transformation of the trait was used and when analysis was carried out on an ethnically homogeneous subset of the original pedigrees. CONCLUSION: In agreement with existing literature, transforming a trait to ensure normality using a Box-Cox transformation is highly recommended in order to avoid false-positive linkages. Furthermore, pedigrees should be sorted by ethnic groups and analyses should be carried out separately. Finally, one should be aware that the inclusion of covariates with a high rate of missing values reduces considerably the number of subjects included in the model. In such a case, the loss in power may be large. Imputation methods are then recommended.

Chromosome Mapping↗
PubMed1997

Mapping quantitative trait loci for body weight on the X chromosome in mice. II. Analysis of congenic backcrosses.

In a QTL mapping study with an F2 population of mice, we have shown that one or more sex-linked factors account for a large part of the divergence between mouse lines selected for high and low body weight. Here, we describe a study undertaken to map the putative X-linked quantitative trait loci (QTLs) by backcrossing segments of chromosome from the high line onto an inbred line derived from the low line, thereby removing possible contributions from the autosomes and linked segments of the X chromosome. Sublines containing a regional at the proximal end of the X chromosome were found to be associated with large differences in body weight, and to account for almost all the difference between the lines. A Markov chain Monte Carlo based multipoint linkage analysis incorporating the available marker and phenotypic information from the backcross pedigree was used to map the QTL to a region of about 6 cM. There was no evidence for QTLs elsewhere on the chromosome. The estimated QTL effect is approximately 20% of mean body weight in males and females at 10 weeks. From results obtained from this study and the accompanying F2 analysis, we conclude the presence of a single factor for body weight localizing to about position (+/- SE) 26.4 +/- 1.2 cM on the X chromosome, which increases body weight by approximately 18% at 10 weeks. A strategy to positionally clone the QTL is discussed.

Animals↗
PubMed2004

Mapping quantitative trait loci in F2 incorporating phenotypes of F3 progeny.

In plants and laboratory animals, QTL mapping is commonly performed using F(2) or BC individuals derived from the cross of two inbred lines. Typical QTL mapping statistics assume that each F(2) individual is genotyped for the markers and phenotyped for the trait. For plant traits with low heritability, it has been suggested to use the average phenotypic values of F(3) progeny derived from selfing F(2) plants in place of the F(2) phenotype itself. All F(3) progeny derived from the same F(2) plant belong to the same F(2:3) family, denoted by F(2:3). If the size of each F(2:3) family (the number of F(3) progeny) is sufficiently large, the average value of the family will represent the genotypic value of the F(2) plant, and thus the power of QTL mapping may be significantly increased. The strategy of using F(2) marker genotypes and F(3) average phenotypes for QTL mapping in plants is quite similar to the daughter design of QTL mapping in dairy cattle. We study the fundamental principle of the plant version of the daughter design and develop a new statistical method to map QTL under this F(2:3) strategy. We also propose to combine both the F(2) phenotypes and the F(2:3) average phenotypes to further increase the power of QTL mapping. The statistical method developed in this study differs from published ones in that the new method fully takes advantage of the mixture distribution for F(2:3) families of heterozygous F(2) plants. Incorporation of this new information has significantly increased the statistical power of QTL detection relative to the classical F(2) design, even if only a single F(3) progeny is collected from each F(2:3) family. The mixture model is developed on the basis of a single-QTL model and implemented via the EM algorithm. Substantial computer simulation was conducted to demonstrate the improved efficiency of the mixture model. Extension of the mixture model to multiple QTL analysis is developed using a Bayesian approach. The computer program performing the Bayesian analysis of the simulated data is available to users for real data analysis.

Algorithms↗
PubMed1997

Mapping quantitative trait loci for fear-like behaviors in mice.

Two mouse models developed for screening anxiolytic drugs were selected for genetic analysis, namely "wall-seeking" tendency in an open field ("thigmotaxis") and the light-to-dark transition (LD) paradigm, a conflict test. These tests measure differences in naturalistic tendencies of mice to explore a novel environment and to avoid a bright light or the center of an open field. In an F2 intercross of two strains of mice (A/J and C57BL/6J) that differ markedly in these behaviors, we estimated a broad sense heritability ranging from 0.3 to 0.59. With this intercross (n = 518), we have mapped several quantitative trait loci (QTL) for these behaviors by performing a genome-wide search. A significant QTL on chromosome 10 (near D10Mit237; LOD of 9.3) that affects LD behavior was identified, and suggestive QTL (LOD > 2.8) were mapped to chromosomes 6, 15, 19, and X. For center time behaviors, QTL were identified on chromosome 1 (LOD of 7.7 and 4.0 for the initial 5-min epoch and the first trial average of the next two 5-min epochs, respectively), and suggestive QTL (LOD > 2.8) were mapped to chromosomes 6 and 14. These QTL individually explain from 2.3 to 8.4% of the phenotypic variance. Collectively, the multiple independent QTL explain from 3.5 to 26.5% of the F2 population's phenotypic variance, depending on the trait. The complexity and heterogeneity of the genetic factors underlying these fear-like behaviors are illustrated by the lack of shared QTL between paradigms and by mapping different QTL for repeated trials of behavior. The identification of QTL affecting individual differences in fear-like behavior may lead to the identification of new gene products and pathways that modulate behavior, providing targets for rational drug design.

Animals↗
PubMed1995

Detection of genome similarity as an exploratory tool for mapping complex traits.

For one- and two-trait-locus models, we show that the lod score based on affected relative pairs or trios is a monotonically increasing function of the genome similarity measured by the proportion of alleles shared identical by descent (IBD) conditional on observed marker data. These results can be generalized to multitrait-locus models. Thus, we can use conditional probability of genes shared IBD as a tool to reveal chromosomal segments that are likely to harbor the genes underlying the complex traits.

Alleles↗
PubMed2002

Mapping quantitative trait loci controlling early growth in a (longleaf pine x slash pine) x slash pine BC(1) family.

Random amplified polymorphic DNA (RAPD) markers were employed to map the genome and quantitative trait loci controlling the early growth of a pine hybrid F(1) tree ( Pinus palustris Mill. x P. elliottii Engl.) and a recurrent slash pine tree ( P. elliottii Engl.) in a (longleaf pine x slash pine) x slash pine BC(1) family consisting of 258 progeny. Of the 150 hybrid F(1) parent-specific RAPD markers, 133 were mapped into 17 linkage groups covering a genetic distance of 1,338.2 cM. Of the 116 slash pine parent-specific RAPD markers, 83 were mapped into 19 linkage groups covering a genetic distance of 994.6 cM. A total of 11 different marker intervals were found to be significantly associated with 13 of the 20 traits on height and diameter growth using MAPMAKER/QTL. Nine of the eleven marker intervals were unique to the hybrid parent 488 genome, and two were unique to the recurrent parent 18-27 genome. The amount of phenotypic variance explained by the putative QTLs ranged from 3.6% to 11.0%. Different QTLs were detected at different ages. Two marker intervals from the hybrid parent 488 were found to have QTL by environment interactions.

Journal Article↗
PubMed2005

Quantitative trait loci mapping for intracellular calcium in spontaneously hypertensive rats.

BACKGROUND: Increased intracellular calcium ([Ca2+]i) in platelets is also proposed as an intermediate phenotype for hypertension in spontaneously hypertensive rats (SHR). Increased [Ca2+]i in platelets is hypothesized to contribute to atherothrombotic events. Platelet hyperactivity is frequently associated with cardiovascular disease. METHODS: In a genome scan, we performed the quantitative trait loci (QTL) mapping for [Ca2+]i in back-crossed rats derived from SHR and normotensive Fischer 344 rats, which demonstrated a single major QTL for hypertension on chromosome 1. Thrombin-stimulated [Ca2+]i in Ca2+-free and in Ca2+-containing buffers was measured in platelets using the Fura-2 method. RESULTS: Among the parental strains, systolic blood pressure and thrombin-stimulated [Ca2+]i were significantly greater in SHR than in Fischer 344 and F1 rats. The sarco(endo)plasmic reticulum Ca2+-dependent ATPase II gene locus (Serca2) between D12Mgh5 and D12Mgh6 showed the significant linkage for thrombin-stimulated [Ca2+]i in Ca2+-free and Ca2+-containing buffers. The peak logarithm of the odds scores were 3.6 and 3.3, respectively. These QTL explained 19.8% and 17.4% of the total variances, respectively. D3Mit13 and DXMgh1 showed suggestive linkage for thrombin-stimulated [Ca2+]i in Ca2+-free and in Ca2+-containing buffers, respectively. The peak logarithm of the odds scores were 2.6 and 2.1, respectively. CONCLUSIONS: A significant QTL for [Ca2+]i was mapped near Serca2 on chromosome 12, and suggestive QTL were identified near D3Mit13 and DXMgh1 in a genome scan. Genetic abnormalites in platelet [Ca2+]i may contribute to cardiovascular disease via platelet hyperactivity, independent of blood pressure elevation.

Animals↗
PubMed1999

Preweanling sensorial and motor development in laboratory mice: quantitative trait loci mapping.

Chromosomal mapping of genes linked with 19 measures of sensorial, motor, and body weight development were investigated. Chromosomal mapping is the first step towards gene identification. When a genomic region is shown to be linked to a trait, it is possible to select a reduced number of candidate genes that have been previously mapped on this region. The involvement of every gene can be individually tested either by molecular (transgenesis, homologous recombination) or traditional methods (congenicity). Mapping was performed using 389 males and females from two inbred strains of laboratory mice C57BL/6By and NZB/BlNJ, their reciprocal F1s and F2s. Thirty-six Quantitative Trait Loci (QTL) were mapped, 12 reached the 3.13 lod score, being thus considered as confirmed. These QTL were tentatively labeled: Cliff Drop Aversion (Cliff Qtl), Geotaxia (Geot Qtl), Vertical Clinging (VertCling Qtl), Bar Holding with the 4 paws (BH4P Qtl), Age at Eyelid Opening (Aeyo Qtl), Visual Placing (Vispl Qtl), Startle Response (Start Qtl1, Start Qtl2), Body Weight at Day 10 in Males pooled with Females (Bwefmd10 Qtl), and Body Weight at Day 30 in males (Bwemd30 Qtl). For the majority of the developmental measures, the QTL that were mapped contributed little to the phenotypic variance, even when mitochondrial DNA contribution was included: Righting Response (12.7%), Cliff Drop Aversion (10%), Crossed Extensor Response (18.1%), Geotaxia (16.2%), Bar Holding Response for 10 s (12.1%), Bar Holding Response with 4 paws (8.1%), Vertical Clinging (9.3%), Vertical Climbing (5%), Startle Response (21.2%), Eyelid Opening (14.6%), Visual Placing (22%), Body Weight at Day 10 (27%), Body Weight at Day 15 in Females (52.5%), Body Weight at Day 15 in Males (17%), Body Weight at Day 30 in Females (42%), and Body Weight at Day 30 in Males (48%). A factorial analysis of the correlations between the measures of development did not provide evidence of a general factor. A general genetic factor of development was also rejected because few common genetic correlates were discovered for the 19 measures of development (Body Weight at Days 15 and 30 in Females on Chromosome 2, Eyelid Opening and Body Weight at Day 10 on Chromosome 5 and mitochondrial genome for five measures). Co-identification of genes, the function of which were previously known thanks to newly discovered QTL, should help to explain the function of QTL. Present data help to highlight candidate regions including several genes that could be candidates for the QTL function. Large confidence intervals were obtained as usual from the F2 intercrossed population. More stringent methods are suggested for more efficient co-identification.

Animals↗
PubMed2003

Mapping quantitative trait loci underlying appearance quality of rice grains (Oryza sativa L.).

Ninety-eight backcross inbred lines (BILs) derived from a subspecific cross of Nipponbare (japonica)/Kasalath (indica)//Nipponbare and its genetic linkage map were used to identify quantitative trait locus (QTL) controlling rice appearance quality traits such as grain length (GL), grain width (GW), length-width ratio (LWR), percentage of grains with chalkiness (PGWC), square of chalky endosperm (SCE), degree of endosperm chalkiness (DEC) and grain endosperm transparency (GET) by composite interval mapping over two years. A total of 33 QTLs were identified to be significant in at least one year for seven traits and the number of QTL for each trait ranged from four to seven, showing that appearance quality of rice grains were controlled by multigenes. The phenotypic variation explained by individual QTLs ranged from 6.2% to 15.2% for GL, 8.3% to 32.5% for GW, 6.8% to 19.8% for LWR, 6.4% to 28.5% for PGWC, 6.1% to 16.9% for SCE, 9.3% to 17.2% for DEC, and 5.6% to 25.2% for GET. The distribution of QTL were characterized by clustering with the intervals of C1488-C563 on chromosome 3, R830-R3166 and R1436-R2289 on chromosome 5, R2147-R2171 on chromosome 6 each harboring over 3 QTLs. The QTL-by-environment interactions were observed by comparing QTL mapping of the same population grown in 2 consecutive years, but were trait dependent. The QTL for GL and LWR were rather stable across years while that for PGWC, SCE and DEC were very sensitive to environments.

Chromosome Mapping↗
PubMed1994

Mapping quantitative trait loci in crosses between outbred lines using least squares.

The use of genetic maps based upon molecular markers has allowed the dissection of some of the factors underlying quantitative variation in crosses between inbred lines. For many species crossing inbred lines is not a practical proposition, although crosses between genetically very different outbred lines are possible. Here we develop a least squares method for the analysis of crosses between outbred lines which simultaneously uses information from multiple linked markers. The method is suitable for crosses where the lines may be segregating at marker loci but can be assumed to be fixed for alternative alleles at the major quantitative trait loci (QTLs) affecting the traits under analysis (e.g., crosses between divergent selection lines or breeds with different selection histories). The simultaneous use of multiple markers from a linkage group increases the sensitivity of the test statistic, and thus the power for the detection of QTLs, compared to the use of single markers or markers flanking an interval. The gain is greater for more closely spaced markers and for markers of lower information content. Use of multiple markers can also remove the bias in the estimated position and effect of a QTL which may result when different markers in a linkage group vary in their heterozygosity in the F1 (and thus in their information content) and are considered only singly or a pair at a time. The method is relatively simple to apply so that more complex models can be fitted than is currently possible by maximum likelihood. Thus fixed effects of background genotype can be fitted simultaneously with the exploration of a single linkage group which will increase the power to detect QTLs by reducing the residual variance. More complex models with several QTLs in the same linkage group and two-locus interactions between QTLs can similarly be examined. Thus least squares provides a powerful tool to extend the range of crosses from which QTLs can be dissected whilst at the same time allowing flexible and realistic models to be explored.

Alleles↗
PubMed2001

Quantitative trait loci mapping of serum IgE in an isolated Hutterite population.

Serum immunoglobulin E (IgE) is a quantitative trait associated with asthma. A variance components approach to the analysis of data from a subset of a founder population of Hutterites indicated that over half the variability in IgE levels may be due to genes. Two variance components methods suggest that the genetic model underlying IgE does not consist of a single major gene, but of multiple loci with more modest effects. Both approaches identified putative quantitative trait loci on chromosomes 1 and 21.

Adult↗
PubMed2001

Quantitative trait loci mapping associated with plant regeneration ability from seed derived calli in rice (Oryza sativa L.).

Quantitative trait loci (QTLs), which are associated with the ability of plant regeneration from seed derived calli, were detected using a recombinant inbred (RI) population from a cross between 'Milyang 23 (toingil)' and 'Gihobyeo (japonica)' in rice (Oryza sativa L.). A tongil type cultivar, 'Milyang 23', has a lower frequency of callus induction and plant regeneration than those of japonica 'Gihobyeo'. Transgressive segregations were observed for the callus induction rate and plant regeneration ability from seed derived calli of the RI population. An interval mapping analysis was used to identify the QTL controlling the plant regeneration ability. Two QTLs for the callus induction rate were detected on chromosomes 1 and 2, explaining the 10.9% total phenotypic variation. Four QTLs that are associated with the plant regeneration ability were located on chromosomes 2, 3, and 11, accounting for 25.7% of the total phenotypic variation.

Chromosome Mapping↗
PubMed2003

Mapping complex traits using Random Forests.

Random Forest is a prediction technique based on growing trees on bootstrap samples of data, in conjunction with a random selection of explanatory variables to define the best split at each node. In the case of a quantitative outcome, the tree predictor takes on a numerical value. We applied Random Forest to the first replicate of the Genetic Analysis Workshop 13 simulated data set, with the sibling pairs as our units of analysis and identity by descent (IBD) at selected loci as our explanatory variables. With the knowledge of the true model, we performed two sets of analyses on three phenotypes: HDL, triglycerides, and glucose. The goal was to approach the mapping of complex traits from a multivariate perspective. The first set of analyses mimics a candidate gene approach with a high proportion of true genes among the predictors while the second set represents a genome scan analysis using microsatellite markers. Random Forest was able to identify a few of the major genes influencing the phenotypes, such as baseline HDL and triglycerides, but failed to identify the major genes regulating baseline glucose levels.

Chromosome Mapping↗