High resolution mapping of quantitative trait loci by linkage disequilibrium analysis

Two methods, linkage analysis and linkage disequilibrium (LD) mapping or association study, are usually utilised for mapping quantitative trait loci (QTL). Linkage mapping is appropriate for low resolution mapping to localise trait loci to broad chromosome regions within a few cM (<10 cM), and is based on family data. Linkage disequilibrium mapping, on the other hand, is useful in high resolution or fine mapping, and is based on both population and family data. Using only one marker, one may carry out single-point linkage analysis and linkage disequilibrium mapping. Using two or more markers, it is possible to flank the QTL by multipoint analysis. The development and thus availability of dense marker maps, such as single nucleotide polymorphisms (SNP) in human genome, presents a tremendous opportunity for multipoint fine mapping. In this article, we propose a regression approach of mapping QTL by linkage disequilibrium mapping based on population data. Assuming that two marker loci flank one quantitative trait locus, a two-point linear regression is proposed to analyse population data. We derive analytical formulas of parameter estimations, and non-centrality parameters of appropriate tests of genetic effects and linkage disequilibrium coefficients. The merit of the method is shown by the power calculation and comparison. The two-point regression model can capture much more linkage and linkage disequilibrium information than that derived when only one marker is used. For a complex disease with heritability h(2)> or =0.15, a study with sample size of 250 can provide high power for QTL detection under moderate linkage disequilibria.     

Pedigree linkage disequilibrium mapping of quantitative trait loci

In this paper, we propose to use pedigrees of any size and any types of relatives in joint high-resolution linkage disequilibrium (LD) and linkage mapping of quantitative trait loci (QTL) by variance component models. Two or multiple markers can be simultaneously used in modeling association with the trait locus, instead of using one marker a time in the analysis. The proposed method can provide a unified result by using two or multiple markers in the modeling. This may avoid the complications of different results obtained from the separate analysis of marker by marker. The models simultaneously incorporate both linkage and LD information. The measures of LD are modeled by mean coefficients, and linkage information is modeled by variance-covariance matrix. Using analytical formulas to calculate the regression coefficients, the genetic effects are shown to be decomposed into additive and dominance components. The noncentrality parameter approximations of test statistics of LD are provided to make power calculations. Power and type I error rates are explored to investigate the merit of the proposed method by both the analytical formulas and simulations. Comparing with the association between-family and association within-family ('AbAw') approach of Fulker and Abecasis et al, it is evident that the method proposed in this article is more powerful. The method is applied to investigate the relation between polymorphisms in the angiotensin 1-converting enzyme (ACE) genes and circulating ACE levels, with a better result than that of the 'AbAw' approach. Moreover, two markers I/D and 4656(CT)3/2 can fully interpret association with the trait locus at a 0.01 significance level, which provides a unique result for the ACE data.     

Finding disease genes: a fast and flexible approach for analyzing high-throughput data

Linkage disequilibrium (LD) is the non-random distribution of alleles across the genome, and it can create serious problems for modern linkage studies. In particular, computational feasibility is often obtained at the expense of power, precision, and/or accuracy. In our new approach, we combine linkage results over multiple marker subsets to provide fast, efficient, and robust analyses, without compromising power, precision, or accuracy. Allele frequencies and LD in the densely spaced markers are used to construct subsamples that are highly informative for linkage. We have tested our approach extensively, and implemented it in the software package EAGLET (Efficient Analysis of Genetic Linkage: Estimation and Testing). Relative to several commonly used methods we show that EAGLET has increased power to detect disease genes across a range of trait models, LD patterns, and family structures using both simulated and real data. In particular, when the underlying LD pattern is derived from real data, we find that EAGLET outperforms several commonly used linkage methods. In-depth analysis of family data, simulated with linkage and under the real-data derived LD pattern, showed that EAGLET had 78.1% power to detect a dominant disease with incomplete penetrance, whereas the method that uses one marker per cM had 69.7% power, and the cluster-based approach implemented in MERLIN had 76.7% power. In this same setting, EAGLET was three times faster than MERLIN, and it narrowed the MERLIN-based confidence interval for trait location by 29%. Overall, EAGLET gives researchers a fast, accurate, and powerful new tool for analyzing high-throughput linkage data, and large extended families are easily accommodated.     

Power of Linkage Disequilibrium Mapping to Detect a Quantitative Trait Locus (QTL) in Selected Samples of Unrelated Individuals

We considered a strategy to map quantitative trait loci (QTLs) using linkage disequilibrium (LD) when the QTL and marker locus were multiallelic. The strategy involved phenotyping a large number of unrelated individuals and genotyping only selected individuals from the two tails of the trait distribution. Power to detect trait‐marker association was assessed as a function of the number of QTL and marker alleles. Two patterns of LD were used to study their influence on power. When the frequency of the QTL allele with the largest effect and that of the marker allele linked in coupling were equal, power was maximum. In this case, increasing the number of QTL alleles reduced the power. The maximum difference in power between the two LD patterns studied was ～30%. For low QTL heritabilities (h²_QTL < 0.1) and single trait studies we recommend selecting around 5% of the upper and lower tails of the trait distribution.     

Haplotypes vs single marker linkage disequilibrium tests: what do we gain?

The genetic dissection of complex diseases represents a formidable challenge for modern human genetics. Recently, it has been suggested that linkage disequilibrium (LD) based methods will be a powerful approach for delineating complex disease genes. Most proposed LD test statistics search for association between a single marker and a putative trait locus. However, the power of a single marker association test may suffer because LD information contained in flanking markers is ignored. Intuitively, haplotypes (which can be regarded as a collection of ordered markers) may be more powerful than individual, unorganised markers. In this study, we derive the analytical tools based on standard chi-square statistics to directly investigate and compare the power between multilocus haplotypes and single marker LD tests. More specifically, novel formulas are obtained in order to calculate expected haplotype frequencies of unlimited size. This study demonstrates that the use of haplotypes can significantly improve the power and robustness of mapping disease genes. Additionally, we detail how the power of haplotype based association tests are affected by important population genetic parameters such as the genetic distance between markers and disease locus, mode of disease inheritance, age of trait causing mutation, frequency of associated marker allele, and level of initial LD. Finally, published data from the Hereditary Hemochromatosis disease region is used to illustrate the utility of haplotypes.     

Targeted genome screen of panic disorder and anxiety disorder proneness using homology to murine QTL regions*

Family and twin studies have indicated that genes influence susceptibility to panic and phobic anxiety disorders, but the location of the genes involved remains unknown. Animal models can simplify gene‐mapping efforts by overcoming problems that complicate human pedigree studies including genetic heterogeneity and high phenocopy rates. Homology between rodent and human genomes can be exploited to map human genes underlying complex traits. We used regions identified by quantitative trait locus (QTL)‐mapping of anxiety phenotypes in mice to guide a linkage analysis of a large multiplex pedigree (99 members, 75 genotyped) segregating panic disorder/agoraphobia. Two phenotypes were studied: panic disorder/agoraphobia and a phenotype (“D‐type”) designed to capture early‐onset susceptibility to anxiety disorders. A total of 99 markers across 11 chromosomal regions were typed. Parametric lod score analysis provided suggestive evidence of linkage (lod = 2.38) to a locus on chromosome 10q under a dominant model with reduced penetrance for the anxiety‐proneness (D‐type) phenotype. Nonparametric (NPL) analysis provided evidence of linkage for panic disorder/agoraphobia to a locus on chromosome 12q13 (NPL = 4.96, P = 0.006). Modest evidence of linkage by NPL analysis was also found for the D‐type phenotype to a region of chromosome 1q (peak NPL = 2.05, P = 0.035). While these linkage results are merely suggestive, this study illustrates the potential advantages of using mouse gene‐mapping results and exploring alternative phenotype definitions in linkage studies of anxiety disorder. © 2001 Wiley‐Liss, Inc.     

Effectiveness of Shrinkage and Variable Selection Methods for the Prediction of Complex Human Traits using Data from Distantly Related Individuals

Genome‐wide association studies (GWAS) have detected large numbers of variants associated with complex human traits and diseases. However, the proportion of variance explained by GWAS‐significant single nucleotide polymorphisms has been usually small. This brought interest in the use of whole‐genome regression (WGR) methods. However, there has been limited research on the factors that affect prediction accuracy (PA) of WGRs when applied to human data of distantly related individuals. Here, we examine, using real human genotypes and simulated phenotypes, how trait complexity, marker‐quantitative trait loci (QTL) linkage disequilibrium (LD), and the model used affect the performance of WGRs. Our results indicated that the estimated rate of missing heritability is dependent on the extent of marker‐QTL LD. However, this parameter was not greatly affected by trait complexity. Regarding PA our results indicated that: (a) under perfect marker‐QTL LD WGR can achieve moderately high prediction accuracy, and with simple genetic architectures variable selection methods outperform shrinkage procedures and (b) under imperfect marker‐QTL LD, variable selection methods can achieved reasonably good PA with simple or moderately complex genetic architectures; however, the PA of these methods deteriorated as trait complexity increases and with highly complex traits variable selection and shrinkage methods both performed poorly. This was confirmed with an analysis of human height.     

Using multivariate genetic modeling to detect pleiotropic quantitative trait loci

Large numbers of sibling pairs or other relatives are needed to detect linkage between a quantitative trait locus (QTL) and a marker, especially if the variance of the QTL is low relative to the total phenotypic variance of the trait. One strategy to increase the power to detect linkage is to reduce the environmental variance in the trait under analysis. This approach was explored by carrying out a series of simulation studies in which multivariate observations were used to estimate individual genotypic values at a QTL, that pleiotropically affected more than one trait. Simulations for different QTL allele frequencies with a completely informative marker showed that the power to detect the QTL increased substantially when estimates of individual genotypic values at the QTL were used in the linkage analysis instead of phenotypic observations. An advantage of this approach is that, rather than employing phenotypic selection, individuals with extreme genotypes may be selected when ascertaining a sample of extreme families.     

Simulation studies of detection of a complex disease in a partially isolated population.

Simulation studies were undertaken with POPGEN, a new population simulation program, to explore strategies for detecting loci underlying rare and common disorders in a small population that has been partially isolated for 10 generations. Haplotype-sharing analysis (HSA) and non-parametric linkage analysis (NPL) were applied to the simulated haplotype and pedigree data for 100 cases, 100 controls, and an average of 28 multiplex pedigrees from cases' families, for a 2-5 cM map of markers. When identity by descent (IBD) status was known (using unique founder marker allele designations assigned during simulation), a linkage disequilibrium (LD) signal could be detected under disease-generating models predicting relative risk to sibs of 11.8 (high-RR) or 2.67 (mod-RR). Detection was more difficult when marker alleles were down-coded to resemble microsatellites (heterozygosities 0.75-0.80). False-positive peaks on nondisease chromosomes were uncommon. NPL analysis was more powerful than HSA at this marker density using down-coded alleles and assuming availability of all affected relatives. LD mapping of common disorders is likely to require denser maps of highly polymorphic markers to approximate full IBD information. LD and linkage mapping provide independent information, and strategies that combine these two methods could be useful in studies of small isolated populations.     

A simple method to localise pleiotropic susceptibility loci using univariate linkage analyses of correlated traits

Univariate linkage analysis is used routinely to localise genes for human complex traits. Often, many traits are analysed but the significance of linkage for each trait is not corrected for multiple trait testing, which increases the experiment-wise type-I error rate. In addition, univariate analyses do not realise the full power provided by multivariate data sets. Multivariate linkage is the ideal solution but it is computationally intensive, so genome-wide analysis and evaluation of empirical significance are often prohibitive. We describe two simple methods that efficiently alleviate these caveats by combining P-values from multiple univariate linkage analyses. The first method estimates empirical pointwise and genome-wide significance between one trait and one marker when multiple traits have been tested. It is as robust as an appropriate Bonferroni adjustment, with the advantage that no assumptions are required about the number of independent tests performed. The second method estimates the significance of linkage between multiple traits and one marker and, therefore, it can be used to localise regions that harbour pleiotropic quantitative trait loci (QTL). We show that this method has greater power than individual univariate analyses to detect a pleiotropic QTL across different situations. In addition, when traits are moderately correlated and the QTL influences all traits, it can outperform formal multivariate VC analysis. This approach is computationally feasible for any number of traits and was not affected by the residual correlation between traits. We illustrate the utility of our approach with a genome scan of three asthma traits measured in families with a twin proband.     

Targeted genome screen of panic disorder and anxiety disorder proneness using homology to murine QTL regions

Family and twin studies have indicated that genes influence susceptibility to panic and phobic anxiety disorders, but the location of the genes involved remains unknown. Animal models can simplify gene-mapping efforts by overcoming problems that complicate human pedigree studies including genetic heterogeneity and high phenocopy rates. Homology between rodent and human genomes can be exploited to map human genes underlying complex traits. We used regions identified by quantitative trait locus (QTL)-mapping of anxiety phenotypes in mice to guide a linkage analysis of a large multiplex pedigree (99 members, 75 genotyped) segregating panic disorder/agoraphobia. Two phenotypes were studied: panic disorder/agoraphobia and a phenotype ("D-type") designed to capture early-onset susceptibility to anxiety disorders. A total of 99 markers across 11 chromosomal regions were typed. Parametric lod score analysis provided suggestive evidence of linkage (lod = 2.38) to a locus on chromosome 10q under a dominant model with reduced penetrance for the anxiety-proneness (D-type) phenotype. Nonparametric (NPL) analysis provided evidence of linkage for panic disorder/agoraphobia to a locus on chromosome 12q13 (NPL = 4.96, P = 0.006). Modest evidence of linkage by NPL analysis was also found for the D-type phenotype to a region of chromosome 1q (peak NPL = 2.05, P = 0.035). While these linkage results are merely suggestive, this study illustrates the potential advantages of using mouse gene-mapping results and exploring alternative phenotype definitions in linkage studies of anxiety disorder.     

Combined Linkage and Association Mapping of Quantitative Trait Loci with Missing Completely at Random Genotype Data

In genetics study, the genotypes or phenotypes can be missing due to various reasons. In this paper, the impact of missing genotypes is investigated for high resolution combined linkage and association mapping of quantitative trait loci (QTL). We assume that the genotype data are missing completely at random (MCAR). Two regression models, “genotype effect model” and “additive effect model”, are proposed to model the association between the markers and the trait locus. If the marker genotype is not missing, the model is exactly the same as those of our previous study, i.e., the number of genotype or allele is used as weight to model the effect of the genotype or allele in single marker case. If the marker genotype is missing, the expected number of genotype or allele is used as weight to model the effect of the genotype or allele. By analytical formulae, we show that the “genotype effect model” can be used to model the additive and dominance effects simultaneously, and the “additive effect model” can only be used to model the additive effect. Based on the two models, F-test statistics are proposed to test association between the QTL and markers. The non-centrality parameter approximations of F-test statistics are derived to calculate power and to compare power, which show that the power of the F-tests is reduced due to the missingness. By simulation study, we show that the two models have reasonable type I error rates for a dataset of moderate sample size. However, the type I error rates can be very slightly inflated if all individuals with missing genotypes are removed from analysis. Hence, the proposed method can help to get correct type I error rates although it does not improve power. As a practical example, the method is applied to analyze the angiotensin-1 converting enzyme (ACE) data. Edited by Pak Sham.     

Detecting linkage disequilibrium in the presence of locus heterogeneity

Locus heterogeneity is a common phenomenon in complex diseases and is one of the most important factors that affect the power of either linkage or linkage disequilibrium (LD) analysis. In linkage analysis, the heterogeneity LOD score (HLOD) rather than LOD itself is often used. However, the existing methods for detecting linkage disequilibrium, such as the TDT and many of its variants, do not take into account locus heterogeneity. We propose two novel likelihood-based methods, an LD-Het likelihood and an LD-multinomial likelihood, to test linkage disequilibrium (LD) that explicitly incorporate locus heterogeneity in the analysis. The LD-Het is applicable to general nuclear family data but requires a working penetrance model. The LD-multinomial is only applicable to affected sib-pair data but does not require specification of a trait model. For affected sib-pair data, both methods have similar power to detect LD under the recessive model, but the LD-multinomial model has greater power when the underlying model is dominant or additive.     

定位人类复杂性状位点的连锁不平衡指数研究进展

随着遗传领域中快速增长的单核苷酸多态性(single nucleotide polymorphism,SNP)和详细的人类单体型数据的获得,群体水平上的连锁不平衡(linkage disequilibrium,LD)定位或关联研究被广泛用来精细定位人类复杂性状位点.一个简单的LD定位方法的关键是选取一个优良的指数来有效地度量性状基因与它紧密相连的遗传标记之间的连锁不平衡程度.本文就精细定位人类复杂性状位点的连锁不平衡指数作一综述.     

Major quantitative trait locus for eosinophil count is located on chromosome 2q

BACKGROUND: Eosinophils are granulocytic white blood cells implicated in asthma and atopic disease. The degree of eosinophilia in the blood of patients with asthma correlates with the severity of asthmatic symptoms. Quantitative trait loci (QTL) linkage analysis of eosinophil count may be a more powerful strategy of mapping genes involved in asthma than linkage analysis using affected relative pairs. OBJECTIVE: To identify QTLs responsible for variation in eosinophil count in adolescent twins. METHODS: We measured eosinophil count longitudinally in 738 pairs of twins at 12, 14, and 16 years of age. We typed 757 highly polymorphic microsatellite markers at an average spacing of approximately 5 centimorgans across the genome. We then used multipoint variance components linkage analysis to test for linkage between marker loci and eosinophil concentrations at each age across the genome. RESULTS: We found highly significant linkage on chromosome 2q33 in 12-year-old twins (logarithm of the odds=4.6; P=.000002) and suggestive evidence of linkage in the same region in 14-year-olds (logarithm of the odds=1.0; P=.016). We also found suggestive evidence of linkage at other areas of the genome, including regions on chromosomes 2, 3, 4, 8, 9, 11, 12, 17, 20, and 22. CONCLUSION: A QTL for eosinophil count is present on chromosome 2q33. This QTL might represent a gene involved in asthma pathophysiology.     

Monte Carlo methods for linkage analysis of two-locus disease models

Parametric linkage analysis of simultaneous mapping of the two disease loci of a qualitative trait governed by a two-locus model has been shown to provide greater power in detecting linkage than standard lod-score analysis that maps a single disease locus. Despite its great potential for power gains, two-locus parametric analysis has not been used routinely in disease gene mapping, due to the computational intensity of currently available methods and programs. In this paper, we propose a Markov chain Monte Carlo (MCMC) method for performing lod-score analysis of qualitative traits governed by two-locus models. This method obtains lod-score estimates that can be arbitrarily close to their corresponding exact values. The algorithm implementing this MCMC method is linear in the number of markers. This feature enables us to perform two-locus analysis mapping each trait to a set of markers, instead of just to a single marker. We analyzed an alcohol dependence dataset composed of 105 pedigrees with various sizes and various degrees of missingness in the observed marker and disease data. The estimates from our MCMC procedure match up well with the lod scores from exact analysis, but it took much less time for the MCMC procedure to obtain the results. We also performed a simulation study to investigate power gains with additional markers. Our results indicate that an additional marker on each map can provide a great deal more information for linkage measured in terms of the magnitude of lod scores.     

Allele association studies with SSR and SNP markers at known physical distances within a 1 Mb region embracing the ALDH2 locus in the Japanese, demonstrates linkage disequilibrium extending up to 400 kb

There has been considerable recent debate concerning the distances over which levels of allelic association useful for genomic quantitative trait locus (QTL) scans can be detected. We have examined simple sequence repeat (SSR) polymorphisms and two single nucleotide polymorphisms (SNPs) in the region flanking the aldehyde dehydrogenase 2 locus, ALDH2, in populations of Japanese alcoholics and controls. These groups differ significantly in the allele frequencies for the functional SNP in exon XII of this gene located on chromosome 12. The results obtained with SSR markers complement recent investigations with SNPs over similar distances at the TCR alpha/delta locus. Significant allelic association with this marker could be detected for SSRs over distances up to 400 kb and over 37 kb for the SNP thereby extending the distance over which LD at this locus could be detected by an order of magnitude. Furthermore, as a proof of principle, we show that comparisons of allele frequency differences for the SSR markers in the case (alcoholics) and control populations would have detected the ALDH2 marker as a putative QTL. Extending the tests to include alleles at two or three flanking loci suggests that the power to detect QTLs through association can be enhanced significantly.     

Attention-deficit/hyperactivity disorder (ADHD): feasibility of linkage analysis in a genetic isolate using extended and multigenerational pedigrees

Segregation analyses converge in explaining the predisposition to attention-deficit/hyperactivity disorder (ADHD) as the consequence of a major gene and exclude purely environmental or cultural transmission. As a result of the ADHD phenotype restrictions, collection of extended families or design of linkage studies using families has been extremely difficult and thus currently linkage studies have been performed using only concordant or discordant sib-pairs rather than large families. On the other hand, intergenerational studies are represented by the transmission disequilibrium test (TDT) using trios. We collected pedigree data on ADHD from the Paisa community from Antioquia, Colombia, a genetic isolate. The goal of this study was to genetically map a putative gene predisposing to ADHD in a set of 27 multigenerational Paisa families. Here we present the results of a power simulation using SIMLINK to detect linkage of ADHD. ADHD was assumed to be a dichotomous trait with incomplete penetrance and a phenocopy rate of 3% in males and 0.2% in females. We simulated cosegregation of the trait and a marker locus in our pedigrees. We assumed Hardy-Weinberg and linkage equilibrium, equally frequent marker alleles and evaluated power at several recombination fractions between the trait and marker loci. Also, the ADHD trait was assumed to be genetically heterogeneous and different functions of age-dependent penetrance were simulated. We found exceptionally good power to detect linkage (expected LOD > 14 if theta is 0.1 or less), and that the presence of heterogeneity up to 50% does not affect substantially the projected LOD scores even for a theta recombination value of 0.05 (eLOD > 5.87). Having now obtained blood samples and confirmatory interviews in five families (representing 20% of the projected number of families), we performed a new analysis. The expected mean LOD in these five families reached values close to 10 and remained invariant when heterogeneity and different penetrance models were considered. We discuss the relative benefits of using extended and multigenerational families for genetic mapping studies as opposed to using nuclear families, affected sib pairs or sporadic cases which require the collection of over 1000 analytical units to get the same power exhibited by the small number of pedigrees described here.     

A male-specific quantitative trait locus on 1p21 controlling human stature

Background: Many genome-wide scans aimed at complex traits have been statistically underpowered due to small sample size. Combining data from several genome-wide screens with comparable quantitative phenotype data should improve statistical power for the localisation of genomic regions contributing to these traits. Objective: To perform a genome-wide screen for loci affecting adult stature by combined analysis of four previously performed genome-wide scans. Methods: We developed a web based computer tool, Cartographer, for combining genetic marker maps which positions genetic markers accurately using the July 2003 release of the human genome sequence and the deCODE genetic map. Using Cartographer, we combined the primary genotype data from four genome-wide scans and performed variance components (VC) linkage analyses for human stature on the pooled dataset of 1417 individuals from 277 families and performed VC analyses for males and females separately. Results: We found significant linkage to stature on 1p21 (multipoint LOD score 4.25) and suggestive linkages on 9p24 and 18q21 (multipoint LOD scores 2.57 and 2.39, respectively) in males-only analyses. We also found suggestive linkage to 4q35 and 22q13 (multipoint LOD scores 2.18 and 2.85, respectively) when we analysed both females and males and to 13q12 (multipoint LOD score 2.66) in females-only analyses. Conclusions: We strengthened the evidence for linkage to previously reported quantitative trait loci (QTL) for stature and also found significant evidence of a novel male-specific QTL on 1p21. Further investigation of several interesting candidate genes in this region will help towards characterisation of this first sex-specific locus affecting human stature.