Quantitative trait locus mapping using human pedigrees

In the past decade phenomenal progress has been made in molecular and statistical genetic methods for localizing quantitative trait loci. Because of these advances, we can anticipate a long period of active genetic research in which the genes influencing human quantitative variability will be mapped and their effects accurately evaluated. Here, we review the current state of the science in statistical genetic methods for quantitative trait linkage analysis. In particular, we detail a variance component-based framework for localizing quantitative trait loci and for accurately estimating their relative effect sizes. Attention is paid to the optimal design of human family studies for localizing genes of small to moderate effect. In addition, methods and strategies are described for dealing with the most important complications of quantitative variation, including the assessment of genotype x environment interaction and epistasis.     

Pooled analysis of data from multiple quantitative trait locus mapping populations

Quantitative trait locus (QTL) analysis on pooled data from multiple populations (pooled analysis) provides a means for evaluating, as a whole, evidence for existence of a QTL from different studies and examining differences in gene effect of a QTL among different populations. Objectives of this study were to: (1) develop a method for pooled analysis and (2) conduct pooled analysis on data from two soybean mapping populations. Least square interval mapping was extended for pooled analysis by inclusion of populations and cofactor markers as indicator variables and covariate variables separately in the multiple linear models. The general linear test approach was applied for detecting a QTL. Single population-based and pooled analyses were conducted on data from two F_2:3 mapping populations, Hamilton (susceptible) × PI 90763 (resistant) and Magellan (susceptible) × PI 404198A (resistant), for resistance to soybean cyst nematode (SCN) in soybean. It was demonstrated that where a QTL was shared among populations, pooled analysis showed increased LOD values on the QTL candidate region over single population analyses. Where a QTL was not shared among populations, however, the pooled analysis showed decreased LOD values on the QTL candidate region over single population analyses. Pooled analysis on data from genetically similar populations may have higher power of QTL detection than single population-based analyses. QTLs were identified by pooled analysis on linkage groups (LGs) G, B1 and J for resistance to SCN race 2 whereas QTLs on LGs G, B1 and E for resistance to SCN race 5 in soybean PI 90763 and PI 404198A. QTLs on LG G and B1 were identified in both PI 90763 and PI 404198A whereas QTLs on LG E and J were identified in PI 90763 only. QTLs on LGs G and B1 for resistance to race 2 may be the same or closely linked with QTLs on LG G and B1 for resistance to race 5, respectively. It was further demonstrated that QTLs on G and B1 carried by PI 90763 were not significantly different in gene effect from QTLs on LGs G and B1 in PI 404198A, respectively.     

QTL Express: mapping quantitative trait loci in simple and complex pedigrees

QTL Express is the first application for Quantitative Trait Locus (QTL) mapping in outbred populations with a web-based user interface. User input of three files containing a marker map, trait data and marker genotypes allows mapping of single or multiple QTL by the regression approach, with the option to perform permutation or bootstrap tests.     

Comparison of sequential and fixed-structure sampling of pedigrees in complex segregation analysis of a quantitative trait.

In designing a study to demonstrate the existence of a major locus for a quantitative trait, an investigator chooses a sampling rule to ascertain pedigrees. The choice of sampling rule can significantly affect the study's power. Here, we compare two types of sampling rules for family studies: fixed-structure rules, in which the same set of relatives are sampled for each proband, and sequential rules, in which the relative or relatives to be sampled next may depend on the trait values of the individuals already observed. We compare fixed-structure and sequential sampling in the setting of extended pedigrees, a quantitative trait, and the genetic mixed model. Using computer simulation, we show that sequential sampling can increase power to detect segregation at a dominant major locus by over 60% in comparison with fixed-structure sampling. Just as important, this substantially increased power is obtained with an easily implemented sampling rule, one that might reasonably be employed in a family study of a quantitative trait.     

Analysis of quantitative trait loci using hybrid pedigrees: Quantitative traits of animals

A method is proposed for analysis of quantitative traits in animal hybrid pedigrees formed by crosses between outbred lines differing in allele frequencies of the genes controlling the trait studied. The method is based on the decomposition of trait variances into components and uses maximization of the likelihood function for estimating model parameters, which allows the estimation of additive and dominance effects of the gene involved in trait determination and its allele frequencies, as well as determination of the chromosomal position of this gene relative to genotyped markers. To test the linkage of this gene with markers, a statistic with the noncentral x ² distribution has been chosen. Analytical expressions for the power of this method have been derived. The method has been tested on small model hybrid pedigrees. Phenotypic values of the trait and information on marker genotypes for each individual in hybrid pedigrees are initial data for the analysis of a quantitative trait.     

Mapping quantitative trait loci in complex pedigrees: a two-step variance component approach

There is a growing need for the development of statistical techniques capable of mapping quantitative trait loci (QTL) in general outbred animal populations. Presently used variance component methods, which correctly account for the complex relationships that may exist between individuals, are challenged by the difficulties incurred through unknown marker genotypes, inbred individuals, partially or unknown marker phases, and multigenerational data. In this article, a two-step variance component approach that enables practitioners to routinely map QTL in populations with the aforementioned difficulties is explored. The performance of the QTL mapping methodology is assessed via its application to simulated data. The capacity of the technique to accurately estimate parameters is examined for a range of scenarios.     

Multiple-trait quantitative trait locus mapping with incomplete phenotypic data

Background

The goal of linkage analysis is to determine the chromosomal location of the gene(s) for a trait of interest such as a common disease. Three-locus linkage analysis is an important case of multi-locus problems. Solutions can be found analytically for the case of triple backcross mating. However, in the present study of linkage analysis and gene mapping some natural inequality restrictions on parameters have not been considered sufficiently, when the maximum likelihood estimates (MLEs) of the two-locus recombination fractions are calculated.

Results

In this paper, we present a study of estimating the two-locus recombination fractions for the phase-unknown triple backcross with two offspring in each family in the framework of some natural and necessary parameter restrictions. A restricted expectation-maximization (EM) algorithm, called REM is developed. We also consider some extensions in which the proposed REM can be taken as a unified method.

Conclusion

Our simulation work suggests that the REM performs well in the estimation of recombination fractions and outperforms current method. We apply the proposed method to a published data set of mouse backcross families.     

Quantitative trait locus mapping reveals complex genetic architecture of quantitative virulence in the wheat pathogen Zymoseptoria tritici

We conducted a comprehensive analysis of virulence in the fungal wheat pathogen Zymoseptoria tritici using quantitative trait locus (QTL) mapping. High‐throughput phenotyping based on automated image analysis allowed the measurement of pathogen virulence on a scale and with a precision that was not previously possible. Across two mapping populations encompassing more than 520 progeny, 540 710 pycnidia were counted and their sizes and grey values were measured. A significant correlation was found between pycnidia size and both spore size and number. Precise measurements of percentage leaf area covered by lesions provided a quantitative measure of host damage. Combining these large and accurate phenotypic datasets with a dense panel of restriction site‐associated DNA sequencing (RADseq) genetic markers enabled us to genetically dissect pathogen virulence into components related to host damage and those related to pathogen reproduction. We showed that different components of virulence can be under separate genetic control. Large‐ and small‐effect QTLs were identified for all traits, with some QTLs specific to mapping populations, cultivars and traits and other QTLs shared among traits within the same mapping population. We associated the presence of four accessory chromosomes with small, but significant, increases in several virulence traits, providing the first evidence for a meaningful function associated with accessory chromosomes in this organism. A large‐effect QTL involved in host specialization was identified on chromosome 7, leading to the identification of candidate genes having a large effect on virulence.     

Identifying pedigrees segregating at a major locus for a quantitative trait: an efficient strategy for linkage analysis

Having found evidence for segregation at a major locus for a quantitative trait, a logical next step is to identify those pedigrees in which major-locus segregation is occurring. If the quantitative trait is a risk factor for an associated disease, identifying such segregating pedigrees can be important in classifying families by etiology, in risk assessment, and in suggesting treatment modalities. Identifying segregating pedigrees can also be helpful in selecting pedigrees to include in a subsequent linkage study to map the major locus. Here, we describe a strategy to identify pedigrees segregating at a major locus for a quantitative trait. We apply this pedigree selection strategy to simulated data generated under a major-locus or mixed model with a rare dominant allele and sampled according to one of several fixed-structure or sequential sampling designs. We demonstrate that for the situations considered, the pedigree selection strategy is sensitive and specific and that a linkage study based only on the pedigrees classified as segregating extracts essentially all the linkage information in the entire sample of pedigrees. Our results suggest that for large-scale linkage studies involving many genetic markers, the savings from this strategy can be substantial and that, compared with fixed-structure sampling, sequential sampling of pedigrees can greatly improve the efficiency for linkage analysis of a quantitative trait.     

Accommodating pathway information in expression quantitative trait locus analysis

The availability of high-throughput genotyping technologies and microarray assays has allowed researchers to consider pursuing investigations whose ultimate goal is the identification of genetic variations that influence levels of gene expression, e.g., "expression quantitative trait locus" or "eQTL" mapping studies. However, the large number of genes whose expression levels can be tested for association with genetic variations in such studies can create both statistical and biological interpretive problems. We consider the integrated analysis of eQTL mapping data that incorporates pathway, function, and disease process information. The goal of this analysis is to determine if compelling patterns emerge from the data that are consistent with the notion that perturbations in the molecular physiologic environment induced by genetic variations implicate the expression patterns of multiple genes via genetic network relationships or feedback mechanisms. We apply available genetic network and pathway analysis software, as well as a novel regression analysis technique, to carry out the proposed studies. We also consider extensions of the proposed strategies and areas of future research.     

Quantitative trait locus analysis for hemostasis and thrombosis

Susceptibility to thrombosis varies in human populations as well as many in inbred mouse strains. The objective of this study was to characterize the genetic control of thrombotic risk on three chromosomes. Previously, utilizing a tail-bleeding/rebleeding assay as a surrogate of hemostasis and thrombosis function, three mouse chromosome substitution strains (CSS) (B6-Chr5^A/J, Chr11^A/J _, Chr17^A/J) were identified (Hmtb1, Hmtb2, Hmtb3). The tail-bleeding/rebleeding assay is widely used and distinguishes mice with genetic defects in blood clot formation or dissolution. In the present study, quantitative trait locus (QTL) analysis revealed a significant locus for rebleeding (clot stability) time (time between cessation of initial bleeding and start of the second bleeding) on chromosome 5, suggestive loci for bleeding time (time between start of bleeding and cessation of bleeding) also on chromosomes 5, and two suggestive loci for clot stability on chromosome 17 and one on chromosome 11. The three CSS and the parent A/J had elevated clot stability time. There was no interaction of genes on chromosome 11 with genes on chromosome 5 or chromosome 17. On chromosome 17, twenty-three candidate genes were identified in synteny with previously identified loci for thrombotic risk on human chromosome 18. Thus, we have identified new QTLs and candidate genes not previously known to influence thrombotic risk. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Quantitative trait linkage analysis of longitudinal change in body weight

One of the great strengths of the Framingham Heart Study data, provided for the Genetic Analysis Workshop 13, is the long-term survey of phenotypic data. We used this unique data to create new phenotypes representing the pattern of longitudinal change of the provided phenotypes, especially systolic blood pressure and body weight. We performed a linear regression of body weight and systolic blood pressure on age and took the slopes as new phenotypes for quantitative trait linkage analysis using the SOLAR package. There was no evidence for heritability of systolic blood pressure change. Heritability was estimated as 0.15 for adult life "body weight change", measured as the regression slope, and "body weight gain" (including only individuals with a positive regression slope), and as 0.22 for body weight "change up to 50" (regression slope of weight on age up to an age of 50). With multipoint analysis, two regions on the long arm of chromosome 8 showed the highest LOD scores of 1.6 at 152 cM for "body weight change" and of >1.9 around location 102 cM for "body weight gain" and "change up to 50". The latter two LOD scores almost reach the threshold for suggestive linkage. We conclude that the chromosome 8 region may harbor a gene acting on long-term body weight regulation, thereby contributing to the development of the metabolic syndrome.     

A quantitative trait locus analysis of personality in wild bighorn sheep

Personality, the presence of persistent behav105 ioral differences among individuals over time or contexts, potentially has important ecological and evolutionary consequences. However, a lack of knowledge about its genetic architecture limits our ability to understand its origin, evolution, and maintenance. Here, we report on a genome‐wide quantitative trait locus (QTL) analysis for two personality traits, docility and boldness, in free‐living female bighorn sheep from Ram Mountain, Alberta, Canada. Our variance component linkage analysis based on 238 microsatellite loci genotyped in 310 pedigreed individuals identified suggestive docility and boldness QTL on sheep chromosome 2 and 6, respectively. A lack of QTL overlap indicated that genetic covariance between traits was not modulated by pleiotropic effects at a major locus and may instead result from linkage disequilibrium or pleiotropic effects at QTL of small effects. To our knowledge, this study represents the first attempt to dissect the genetic architecture of personality in a free‐living wildlife population, an important step toward understanding the link between molecular genetic variation in personality and fitness and the evolutionary processes maintaining this variation.     

Quantitative trait locus analysis of the early domestication of sunflower

Genetic analyses of the domestication syndrome have revealed that domestication-related traits typically have a very similar genetic architecture across most crops, being conditioned by a small number of quantitative trait loci (QTL), each with a relatively large effect on the phenotype. To date, the domestication of sunflower (Helianthus annuus L.) stands as the only counterexample to this pattern. In previous work involving a cross between wild sunflower (also H. annuus) and a highly improved oilseed cultivar, we found that domestication-related traits in sunflower are controlled by numerous QTL, typically of small effect. To provide insight into the minimum genetic changes required to transform the weedy common sunflower into a useful crop plant, we mapped QTL underlying domestication-related traits in a cross between a wild sunflower and a primitive Native American landrace that has not been the target of modern breeding programs. Consistent with the results of the previous study, our data indicate that the domestication of sunflower was driven by selection on a large number of loci, most of which had small to moderate phenotypic effects. Unlike the results of the previous study, however, nearly all of the QTL identified herein had phenotypic effects in the expected direction, with the domesticated allele producing a more crop-like phenotype and the wild allele producing a more wild-like phenotype. Taken together, these results are consistent with the hypothesis that selection during the post-domestication era has resulted in the introduction of apparently maladaptive alleles into the modern sunflower gene pool.     

Quantitative trait locus analysis of parasitoid counteradaptation to symbiont-conferred resistance

Insect hosts and parasitoids are engaged in an intense struggle of antagonistic coevolution. Infection with heritable bacterial endosymbionts can substantially increase the resistance of aphids to parasitoid wasps, which exerts selection on parasitoids to overcome this symbiont-conferred protection (counteradaptation). Experimental evolution in the laboratory has produced counteradapted populations of the parasitoid wasp Lysiphlebus fabarum. These populations can parasitize black bean aphids (Aphis fabae) protected by the bacterial endosymbiont Hamiltonella defensa, which confers high resistance against L. fabarum. We used two experimentally evolved parasitoid populations to study the genetic architecture of the counteradaptation to symbiont-conferred resistance by QTL analysis. With simple crossing experiments, we showed that the counteradaptation is a recessive trait depending on the maternal genotype. Based on these results, we designed a customized crossing scheme to genotype a mapping population phenotyped for the ability to parasitize Hamiltonella-protected aphids. Using 1835 SNP markers obtained by ddRAD sequencing, we constructed a high-density linkage map consisting of six linkage groups (LGs) with an overall length of 828.3 cM and an average marker spacing of 0.45 cM. We identified a single QTL associated with the counteradaptation to Hamiltonella in L. fabarum on linkage group 2. Out of 120 genes located in this QTL, several genes encoding putative venoms may represent candidates for counteradaptation, as parasitoid wasps inject venoms into their hosts during oviposition.Subject terms: Experimental evolution, Evolutionary genetics, Evolutionary ecology, Genetic linkage study     

Quantitative trait locus analysis of fatty acid concentrations in maize.

A study was conducted to determine the number and chromosomal location of quantitative trait loci (QTL) influencing the concentration of five fatty acids in 200 F2S1 lines derived from an Illinois High Oil (IHO) by Illinois Low Oil (Early Maturity) (ILO(EM)) cross. Restriction fragment length polymorphism (RFLP) analysis was performed on the 200 S1 lines and concentrations of palmitic (16:0), stearic (18:0), oleic (18:1), linoleic (18:2), and linolenic (18:3) acids were determined in self-pollinated kernels harvested from plants grown in replicated field trials during 1992 and 1993. A series of 74 cDNA and genomic clones were used and these revealed 80 polymorphic loci spaced, on average, 24 cM apart throughout the maize genome. Analysis of variance detected significant (p < 0.05) associations between several RFLP loci and the concentration of each fatty acid. A total of 15 RFLP loci clustered in 12 chromosomal regions were associated with the concentration of 16:0, 17 loci clustered in 10 regions were associated with the concentration of 18:0, 12 loci clustered in eight regions were associated with the concentration of 18:1 and 18:2, and 17 loci clustered in eight regions were associated with the concentration of 18:3. Multiple linear regression models consisting of four RFLP loci explained 24 and 62% of the total phenotypic and genotypic variation (R2) among the 200 F2S1 lines for 16:0, five loci explained 51 and 71% of the variation for 18:0, three loci explained 67 and 79% of the variation for 18:1, two loci explained 67 and 81% of the variation for 18:2, and seven loci explained 52 and 78% of the variation for 18:3 in these 200 F2S1 lines. The ratio of 18:1 to 18:2 was tightly interrelated as the same QTL were associated with the concentrations of 18:1 and 18:2. A quantitative trait locus that explained 63% of the phenotypic variation in the ratio of 18:1 to 18:2 is tightly linked to umc65 on chromosome 6 in the region of the linoleic acid1 locus.     

Quantitative trait locus analysis of abnormal circadian period in CS mice

CS mice show a free-running period (κ) longer than 24 h and rhythm splitting in constant darkness (DD). These features in behavioral circadian rhythms are distinctive as compared with other inbred strains of mice, which exhibit robust free-running rhythms with κ shorter than 24 h. To identify the genes affecting κ, quantitative trait locus (QTL) analysis was initially conducted by using 289 F₂ mice derived from a cross between CS and C57BL/6J strain. A suggestive QTL (LOD = 3.71) with CS allele increasing κ was detected on the distal region of Chromosome (Chr) 19. Next, using 192 F₂ mice from a cross between CS and MSM strain, the presence of the QTL on Chr 19 was examined, and we confirmed the QTL at the genome-wide significant level (LOD = 4.61 with 10.4% of the total variance explained). This QTL was named long free-running period (Lfp). Three other suggestive QTLs (LOD = 3.24–4.28) were mapped to the midportion of Chr 12 in (CS×C57BL/6J)F₂ mice, and to the proximal and middle region of Chr 19 in (CS×MSM)F₂ mice, respectively, of which, CS alleles for two QTLs on Chr 19 have the effect of lengthening κ. None of these QTLs were mapped to the chromosomal regions of previously described QTLs for κ and known clock genes (Clock, mPer1, Bmal1, mCry1, mCry2, mTim, and Csnk1e). Received: 5 July 2000 / Accepted: 5 December 2000     

Optimal sibship selection for genotyping in quantitative trait locus linkage analysis

In this paper we present a novel method for selecting optimally informative sibships of any size for quantitative trait locus (QTL) linkage analysis. The method allocates a quantitative index of potential informativeness to each sibship on the basis of observed trait scores and an assumed true QTL model. Any sample of phenotypically screened sibships can therefore be easily rank-ordered for selective genotyping. The quantitative index is the sibship's expected contribution to the non-centrality parameter. This expectation represents the weighted sum of chi(2) test statistics that would be obtained given the observed trait values over all possible sibship genotypic configurations; each configuration is weighted by the likelihood of it occurring given the assumed true genetic model. The properties of this procedure are explored in relation to the accuracy of the assumed true genetic model and sibship size. In comparison to previous methods of selecting phenotypically extreme sibships for genotyping, the proposed method is considerably more efficient and is robust with regard to the specification of the genetic model.     

Mapping quantitative trait loci using multiple phenotypes in general pedigrees

The use of correlated phenotypes may dramatically increase the power to detect the underlying quantitative trait loci (QTLs). Current approaches for multiple phenotypes include regression-based methods, the multivariate variance of components method, factor analysis and structural equations. Issues with these methods include: 1) They are computation intensive and are subject to problems of optimization algorithms; 2) Existing claims on the asymptotic distribution of the likelihood ratio statistic for the multivariate variance of components method are contradictory and erroneous; 3) The dimension reduction of the parameter space under the null hypothesis, a phenomenon that is unique to multivariate analyses, makes the asymptotic distribution of the likelihood ratio statistic more complicated than expected. In this article, three cases of varying complexity are considered. For each case, the efficient score statistic, which is asympotically equivalent to the likelihood ratio statistic, is derived, so is its asymptotic distribution [correction]. These methods are straightforward to calculate. Finite-sample properties of these score statistics are studied through extensive simulations. These score statistics are for use with general pedigrees.     

Segregation analysis of a complex quantitative trait: approaches for identifying influential data points

BACKGROUND/AIMS: Complex traits pose a particular challenge to standard methods for segregation analysis (SA), and for such traits it is difficult to assess the ability of complex SA (CSA) to approximate the true mode of inheritance. Here we use an oligogenic Bayesian Markov chain Monte Carlo method for SA (OSA) to verify results from a single-locus likelihood-based CSA for data on a quantitative measure of reading ability. METHODS: We compared the profile likelihood from CSA, maximized over the trait allele frequency, to the posterior distribution of genotype effects from OSA to explore differences in the overall parameter estimates from SA on the original phenotype data and the same data Winsorized to reduce the potential influence of three outlying data points. RESULTS: Bayesian OSA revealed two modes of inheritance, one of which coincided with the QTL model from CSA. Winsorizing abolished the model originally estimated by CSA; both CSA and OSA identified only the second OSA model. CONCLUSION: Differences between the results from the two methods alerted us to the presence of influential data points, and identified the QTL model best supported by the data. Thus, the Bayesian OSA proved a valuable tool for assessing and verifying inheritance models from CSA.