Methods for linkage analysis of quantitative trait loci in humans

This paper reviews linkage analysis methods for detecting loci associated with quantitative traits in humans. All such methods are based on the underlying principle that family members who have similar trait values should have higher than expected levels of sharing of genetic material (identity by descent) near the genes that influence those traits. A number of different statistical methods for testing that association between shared trait values and shared identity by descent have been developed over the past 30 or more years. These different types of tests are reviewed here, with emphasis on their theory and derivations. Robustness and power are also discussed.     

Genetic linkage to chromosome 22q12 for a heavy-smoking quantitative trait in two independent samples

We conducted a genomewide linkage screen of a simple heavy-smoking quantitative trait, the maximum number of cigarettes smoked in a 24-h period, using two independent samples: 289 Australian and 155 Finnish nuclear multiplex families, all of which were of European ancestry and were targeted for DNA analysis by use of probands with a heavy-smoking phenotype. We analyzed the trait, using a regression of identity-by-descent allele sharing on the sum and difference of the trait values for relative pairs. Suggestive linkage was detected on chromosome 22 at 27-29 cM in each sample, with a LOD score of 5.98 at 26.96 cM in the combined sample. After additional markers were used to localize the signal, the LOD score was 5.21 at 25.46 cM. To assess the statistical significance of the LOD score in the combined sample, 1,000 simulated genomewide screens were conducted, resulting in an empirical P value of .006 for the LOD score of 5.21. This linkage signal is driven mainly by the microsatellite marker D22S315 (22.59 cM), which had a single-point LOD score of 5.41 in the combined sample and an empirical P value <.001 from 1,000 simulated genomewide screens. This marker is located within an intron of the gene ADRBK2, encoding the beta-adrenergic receptor kinase 2. Fine mapping of this linkage region may reveal variants contributing to heaviness of smoking, which will lead to a better understanding of the genetic mechanisms underlying nicotine dependence.     

Use of multivariate linkage analysis for dissection of a complex cognitive trait

Replication of linkage results for complex traits has been exceedingly difficult, owing in part to the inability to measure the precise underlying phenotype, small sample sizes, genetic heterogeneity, and statistical methods employed in analysis. Often, in any particular study, multiple correlated traits have been collected, yet these have been analyzed independently or, at most, in bivariate analyses. Theoretical arguments suggest that full multivariate analysis of all available traits should offer more power to detect linkage; however, this has not yet been evaluated on a genomewide scale. Here, we conduct multivariate genomewide analyses of quantitative-trait loci that influence reading- and language-related measures in families affected with developmental dyslexia. The results of these analyses are substantially clearer than those of previous univariate analyses of the same data set, helping to resolve a number of key issues. These outcomes highlight the relevance of multivariate analysis for complex disorders for dissection of linkage results in correlated traits. The approach employed here may aid positional cloning of susceptibility genes in a wide spectrum of complex traits.     

Linkage analysis of quantitative trait loci in multiple line crosses

Simple line crosses, for example, backcross and F₂, are commonly used in mapping quantitative trait loci (QTL). However, these simple crosses are rarely used alone in commercial plant breeding; rather, crosses involving multiple inbred lines or several simple crosses but connected by shared inbred lines may be common in plant breeding. Mapping QTL using crosses of multiple lines is more relevant to plant breeding. Unfortunately, current statistical methods and computer programs of QTL mapping are all designed for simple line crosses or multiple line crosses but under a regular mating system. It is not straightforward to extend the existing methods to handle multiple line crosses under irregular and complicated mating designs. The major hurdle comes from irregular inbreeding, multiple generations, and multiple alleles. In this study, we develop a Bayesian method implemented via the Markov chain Monte Carlo (MCMC) algorithm for mapping QTL using complicated multiple line crosses. With the MCMC algorithm, we are able to draw a complete path of the gene flow from founder alleles to their descendents via a recursive process. This has greatly simplified the problem caused by irregular mating and inbreeding in the mapping population. Adopting the reversible jump MCMC algorithm, we are able to simultaneously search for multiple QTL along the genome. We can even infer the posterior distribution of the number of QTL, one of the most important parameters in QTL study. Application of the new MCMC based QTL mapping procedure is demonstrated using two different mating designs. Design I involves two inbred lines and their derived F₁, F₂, and BC populations. Design II is a half-diallel cross involving three inbred lines. The two designs appear different, but can be handled with the same robust computer program.     

Segregation and linkage analysis for longitudinal measurements of a quantitative trait

We present a method for using slopes and intercepts from a linear regression of a quantitative trait as outcomes in segregation and linkage analyses. We apply the method to the analysis of longitudinal systolic blood pressure (SBP) data from the Framingham Heart Study. A first-stage linear model was fit to each subject's SBP measurements to estimate both their slope over time and an intercept, the latter scaled to represent the mean SBP at the average observed age (53.7 years). The subject-specific intercepts and slopes were then analyzed using segregation and linkage analysis. We describe a method for using the standard errors of the first-stage intercepts and slopes as weights in the genetic analyses. For the intercepts, we found significant evidence of a Mendelian gene in segregation analysis and suggestive linkage results (with LOD scores >or= 1.5) for specific markers on chromosomes 1, 3, 5, 9, 10, and 17. For the slopes, however, the data did not support a Mendelian model, and thus no formal linkage analyses were conducted.     

Genetic linkage analysis of a dichotomous trait incorporating a tightly linked quantitative trait in affected sib pairs

Many complex diseases are usually considered as dichotomous traits but are also associated with quantitative biological markers or quantitative risk factors. For such dichotomous traits, although their associated quantitative traits may not directly underly the diagnosis of the disease status, if the associated quantitative trait is also linked to the chromosomal regions linked to the dichotomous trait, then joint analysis of dichotomous and quantitative traits should be more efficient than consideration of them separately. Previous studies have focused on the situation when a dichotomous trait can be modeled by a threshold process acting on a single underlying normal liability distribution. However, for many complex disorders, including most psychiatric disorders, diagnosis is generally based on a set of binary or discrete criteria. These traits cannot be modeled on the basis of a threshold process acting on an underlying continuous trait. We propose a likelihood-based method that efficiently combines such a discrete trait and an associated quantitative trait in the analysis, using affected-sib-pair data. Our simulation studies suggest that joint analysis increases the power to detect linkage of dichotomous traits. We also apply the proposed new method to an asthma genome-scan data set and incorporate the total serum immunoglobulin E level in the analysis.     

Linkage analysis of quantitative trait loci in the presence of heterogeneity

Variance component modeling for linkage analysis of quantitative traits is a powerful tool for detecting and locating genes affecting a trait of interest, but the presence of genetic heterogeneity will decrease the power of a linkage study and may even give biased estimates of the location of the quantitative trait loci. Many complex diseases are believed to be influenced by multiple genes and therefore genetic heterogeneity is likely to be present for many real applications of linkage analysis. We consider a mixture of multivariate normals to model locus heterogeneity by allowing only a proportion of the sampled pedigrees to segregate trait-influencing allele(s) at a specific locus. However, for mixtures of normals the classical asymptotic distribution theory of the maximum likelihood estimates does not hold, so tests of linkage and/or heterogeneity are evaluated using resampling methods. It is shown that allowing for genetic heterogeneity leads to an increase in power to detect linkage. This increase is more prominent when the genetic effect of the locus is small or when the percentage of pedigrees not segregating trait-influencing allele(s) at the locus is high.     

DNA fingerprint bands applied to linkage analysis with quantitative trait loci in chickens

Summary
An efficient approach to detect association between quantitative traits and bands of DNA fingerprint patterns uses intra-family tail analysis, which compares fingerprints of DNA mixes from individuals at the two tails of a phenotypic distribution. In analysis of 67 paternal half-sibs of a meat-type chicken family, of 57 sire bands generated by two probes, one sire-specific band (S6–6) was associated with abdominal fat deposition. The band effect was estimated by a linear model analysis to be 0–88 standard deviations, or about 30% of the family mean. The association between band S6–6 and abdominal fat was further examined by testing progeny of paternal half-sibs of the chickens which were used in the tail analysis, establishing genetic linkage between the DNA marker and a genetic locus affecting abdominal fat deposition.     

Theobroma cacao L.: a genetic linkage map and quantitative trait loci analysis

A genetic linkage map of Theobroma cacao (cocoa) has been constructed from 131 backcross trees derived from a cross between a single tree of the variety Catongo and an F1 tree from the cross of Catongo by Pound 12. The map comprises 138 markers: 104 RAPD loci, 32 RFLP loci and two morphologic loci. Ten linkage groups were found which cover 1068 centimorgans (cM). Only six (4%) molecular-marker loci show a significant deviation from the expected 11 segregation ratio.The average distance between two adjacent markers is 8.3 cM. The final genome-size estimates based on two-point linkage data ranged from 1078 to 1112 cM for the cocoa genome. This backcross progeny segregates for two apparently single gene loci controlling (1) anthocyanidin synthesis (Anth) in seeds, leaves and flowers and (2) self-compatibility (Autoc). The Anth locus was found to be 25 cM from Autoc and two molecular markers co-segregate with Anth. The genetic linkage map was used to localize QTLs for early flowering, trunk diameter, jorquette height and ovule number in the BC₁ generation using both single-point ANOVA and interval mapping. A minimum number of 2–4 QTLs (P<0.01) involved in the genetic expression of the traits studied was detected. Coincident map locations of a QTL for jorquette height and trunk diameter suggests the possibility of pleiotropic effects in cocoa for these traits. The combined estimated effects of the different mapped QTLs explained between 11.2% and 25.8% of the phenotypic variance observed in the BC₁ population.     

Sex-specific regulation of mitochondrial DNA levels: genome-wide linkage analysis to identify quantitative trait Loci

Altered mitochondrial DNA (mtDNA) levels have been associated with common diseases in humans. We investigated the genetic mechanism that controls mtDNA levels using genome-wide linkage analyses in families from the Genetic Analysis of Idiopathic Thrombophilia Project (GAIT). We measure mtDNA levels by quantitative real-time PCR in 386 subjects from 21 extended Spanish families. A variance component linkage method using 485 microsatellites was conducted to evaluate linkage and to detect quantitative trait loci (QTLs) involved in the control of mtDNA levels. The heritalibility of mtDNA levels was 0.33 (p = 1.82e-05). We identified a QTL on Chromosome 2 (LOD = 2.21) using all of the subjects, independently on their sex. When females and males were analysed separately, three QTLs were identified. Females showed the same QTL on Chromosome 2 (LOD = 3.09), indicating that the QTL identified in the analysis using all of the subjects was a strong female QTL, and another one on Chromosome 3 (LOD = 2.67), whereas in males a QTL was identified on Chromosome 1 (LOD = 2.81). These QTLs were fine-mapped to find associations with mtDNA levels. The most significant SNP association was for the rs10888838 on Chromosome 1 in males. This SNP mapped to the gene MRPL37, involved in mitochondrial protein translation. The rs2140855 on Chromosome 2 showed association in the analysis using all of the subjects. It was near the gene CMPK2, which encodes a mitochondrial enzyme of the salvage pathway of deoxyribonucleotide synthesis. Our results provide evidence of a sex-specific genetic mechanism for the control of mtDNA levels and provide a framework to identify new genes that influence mtDNA levels.     

Single-nucleotide polymorphism versus microsatellite markers in a combined linkage and segregation analysis of a quantitative trait

Increasingly, single-nucleotide polymorphism (SNP) markers are being used in preference to microsatellite markers. However, methods developed for microsatellites may be problematic when applied to SNP markers. We evaluated the results of using SNPs vs. microsatellites in Monte Carlo Markov chain (MCMC) oligogenic combined segregation and linkage analysis methods. These methods were developed with microsatellite markers in mind. We selected chromosome 7 from the Collaborative Study on the Genetics of Alcoholism dataset for analysis because linkage to an electrophysiological trait had been reported there. We found linkage in the same region of chromosome 7 with the Affymetrix SNP data, the Illumina SNP data, and the microsatellite marker data. The MCMC sampler appears to mix with both types of data. The sampler implemented in this MCMC oligogenic combined segregation and linkage analysis appears to handle SNP data as well as microsatellite data and it is possible that the localizations with the SNP data are better.     

Selecting pedigrees for linkage analysis of a quantitative trait: the expected number of informative meioses.

With evidence of segregation at a major locus for a quantitative trait having been found, a logical next step is to select a subset of the pedigrees to include in a linkage study to map the major locus. Ideally this subset should include much of the linkage information in the sample but include only a fraction of the pedigrees. We previously described a strategy for selecting pedigrees for linkage analysis of a quantitative trait on the basis of a pedigree likelihood-ratio statistic. For quantitative traits controlled by a major locus with a rare dominant allele, the likelihood-ratio strategy extracted nearly all the information for linkage while typically requiring marker data on only about one-third of the pedigrees. Here, we describe a new strategy to select pedigrees for linkage analysis on the basis of the expected number of potentially informative meioses in each pedigree. We demonstrate that this informative-meioses strategy provides an efficient and more general means to select pedigrees for a linkage study of a quantitative trait.     

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.     

A quantitative trait locus for body fat on chromosome 1q43 in French Canadians: linkage and association studies

Objective: To explore a quantitative trait locus (QTL) on human chromosome 1q affecting BMI, adiposity, and fat‐free mass phenotypes in the Quebec Family Study cohort. Research Methods and Procedures: Non‐parametric sibpair and variance component linkage analyses and family‐based association studies were performed with a dense set of chromosome 1q43 microsatellites and single‐nucleotide polymorphism markers in 885 adult individuals. Results: Linkage was observed between marker D1S184 and BMI (p = 0.0004) and with body fat mass or percentage body fat (p ≤ 0.0003), but no linkage was detected with fat‐free mass. Furthermore, significant linkages (p < 0.0001) were achieved with subsamples of sibpairs at both ends of phenotype distributions. Association studies with quantitative transmission disequilibrium tests refined the linkage to a region overlapping the regulator of G‐protein signaling 7 (RGS7) gene and extending to immediate upstream gene loci. Discussion: The present study indicates that the QTL on chromosome 1q43 specifically affects total adiposity and provides a genetic mapping framework for the dissection of this adiposity locus.     

Aroma in rice: genetic analysis of a quantitative trait

A new approach was developed which succeeded in tagging for the first time a major gene and two QTLs controlling grain aroma in rice. It involved a combination of two techniques, quantification of volatile compounds in the cooking water by gas chromatography, and molecular marker mapping. Four types of molecular marker were used (RFLPs, RAPDs, STSs, isozymes). Evaluation and mapping were performed on a doubled haploid line population which (1) conferred a precise character evaluation by enabling the analysis of large quantities of grains per genotype and (2) made possible the comparison of gas chromatography results and sensitive tests. The population size (135 lines) provided a good mapping precision. Several markers on chromosome 8 were found to be closely linked to a major gene controlling the presence of 2-acetyl-1-pyrroline (AcPy), the main compound of rice aroma. Moreover, our results showed that AcPy concentration in plants is regulated by at least two chromosomal regions. Estimations of recombination fractions on chromosome 8 were corrected for strong segregation distortion. This study confirms that AcPy is the major component of aroma. Use of the markers linked to AcPy major gene and QTLs for marker-assisted selection by successive backcrosses may be envisaged.     

Fixed and random effects in the variation of the finger ridge count: a study of fragile-X families.

A maximum-likelihood scoring technique for analysis of pedigree data allows for the concurrent estimation of random and of fixed effects in a quantitative trait. We included both types of effects in genetic models, to study the sources of variation in finger ridge count in 54 large families affected with the fragile-X disorder. The fixed effects were represented by fragile X and sex, and the random effects by environmental and genetic variance. We found a significant effect of fragile X in the mean of the finger ridge count on the thumb (finger 1) and index finger (finger 2), which had the lowest heritability and a negligible nonadditive component of genetic variance. This was in contrast with ridge counts on fingers 3 and 4, which showed little fragile-X effect, but high heritability and a significant nonadditive component. A contrast in genetic properties for ridge counts on fingers 1 and 2, compared with these counts on the remaining three fingers, may be relevant to increased selection pressures on functions of the thumb and of the index finger in evolution of modern man. We have also demonstrated an important effect of fragile X in increasing the additive variance in covariance, especially between male pairs. These findings suggest that the effect of the fragile-X genotype in finger ridge count is additive and superimposed on the normal hereditary variations in this trait.     

Construction of a genetic linkage map of Thlaspi caerulescens and quantitative trait loci analysis of zinc accumulation

Zinc (Zn) hyperaccumulation seems to be a constitutive species-level trait in Thlaspi caerulescens. When compared under conditions of equal Zn availability, considerable variation in the degree of hyperaccumulation is observed among accessions originating from different soil types. This variation offers an excellent opportunity for further dissection of the genetics of this trait. A T. caerulescens intraspecific cross was made between a plant from a nonmetallicolous accession [Lellingen (LE)], characterized by relatively high Zn accumulation, and a plant from a calamine accession [La Calamine (LC)], characterized by relatively low Zn accumulation. Zinc accumulation in roots and shoots segregated in the F3 population. This population was used to construct an LE/LC amplified fragment length polymorphism (AFLP)-based genetic linkage map and to map quantitative trait loci (QTL) for Zn accumulation. Two QTL were identified for root Zn accumulation, with the trait-enhancing alleles being derived from each of the parents, and explaining 21.7 and 16.6% of the phenotypic variation observed in the mapping population. Future development of more markers, based on Arabidopsis orthologous genes localized in the QTL regions, will allow fine-mapping and map-based cloning of the genes underlying the QTL.     

Simultaneous mining of linkage and linkage disequilibrium to fine map quantitative trait loci in outbred half-sib pedigrees: revisiting the location of a quantitative trait locus with major effect on milk production on bovine chromosome 14

A maximum-likelihood QTL mapping method that simultaneously exploits linkage and linkage disequilibrium and that is applicable in outbred half-sib pedigrees is described. The method is applied to fine map a QTL with major effect on milk fat content in a 3-cM marker interval on proximal BTA14. This proximal location is confirmed by applying a haplotype-based association method referred to as recombinant ancestral haplotype analysis. The origin of the discrepancy between the QTL position derived in this work and that of a previous analysis is examined and shown to be due to the existence of distinct marker haplotypes associated with QTL alleles having large substitution effects.     

Linkage between quantitative trait loci and marker loci: resolution power of three statistical approaches in single marker analysis

This paper presents a comparison of three methods of parameter estimation in analysis of linkage between a quantitative trait locus (QTL) and a marker locus: maximum likelihood, mean square for trait cumulative distribution function, and method of moments, employing simulated backcross data. The sensitivity of estimates to violation of assumptions of normality and equal variances were also studied. Some measures of discrepancy between the trait distributions in the QTL groups are considered to evaluate the potential dependence of the resolution capacity of the QTL substitution effect with respect to trait mean value and variance.