Construction of the model for the Genetic Analysis Workshop 14 simulated data: genotype-phenotype relationships,gene interaction,linkage, association,disequilibrium, and ascertainment effects for a complex phenotype

The Genetic Analysis Workshop 14 simulated dataset was designed 1) To test the ability to find genes related to a complex disease (such as alcoholism). Such a disease may be given a variety of definitions by different investigators, have associated endophenotypes that are common in the general population, and is likely to be not one disease but a heterogeneous collection of clinically similar, but genetically distinct, entities. 2) To observe the effect on genetic analysis and gene discovery of a complex set of gene x gene interactions. 3) To allow comparison of microsatellite vs. large-scale single-nucleotide polymorphism (SNP) data. 4) To allow testing of association to identify the disease gene and the effect of moderate marker x marker linkage disequilibrium. 5) To observe the effect of different ascertainment/disease definition schemes on the analysis. Data was distributed in two forms. Data distributed to participants contained about 1,000 SNPs and 400 microsatellite markers. Internet-obtainable data consisted of a finer 10,000 SNP map, which also contained data on controls. While disease characteristics and parameters were constant, four "studies" used varying ascertainment schemes based on differing beliefs about disease characteristics. One of the studies contained multiplex two- and three-generation pedigrees with at least four affected members. The simulated disease was a psychiatric condition with many associated behaviors (endophenotypes), almost all of which were genetic in origin. The underlying disease model contained four major genes and two modifier genes. The four major genes interacted with each other to produce three different phenotypes, which were themselves heterogeneous. The population parameters were calibrated so that the major genes could be discovered by linkage analysis in most datasets. The association evidence was more difficult to calibrate but was designed to find statistically significant association in 50% of datasets. We also simulated some marker x marker linkage disequilibrium around some of the genes and also in areas without disease genes. We tried two different methods to simulate the linkage disequilibrium.     

Detection of Phenotype Modifier Genes Using Two-Locus Linkage Analysis in Complex Disorders Such as Major Psychosis

Objective: To increase power to detect modifier loci conferring susceptibility to specific phenotypes such as disease diagnoses which are part of a broader disorder spectrum by jointly modeling a modifier and a broad susceptibility gene and to identify modifier loci conferring specific susceptibility to schizophrenia (SZ) or to bipolar disorder (BP) using the approach. Methods: We implemented a two-locus linkage analysis model where a gene 1 genotype increases the risk of a broad phenotype and a gene 2 genotype modifies the expression of gene 1 by conferring susceptibility to a specific phenotype. Results: Compared to a single-locus analysis within the broad phenotype, the proposed approach had greater power to detect the modifier gene 2 (0.96 vs. 0.54 under a simulation scenario including heterogeneity). In a sample of 12 mixed SZ and BP Eastern Quebec kindreds, D8S1110 at 8p22 showed the strongest evidence of linkage to a gene determining a specific phenotype (SZ or BP) among subjects susceptible to major psychosis because of putative genes at 10p13 (D10S245, conditional maximized LOD (cMOD) = 4.20, p = 0.0003) and 3q21-q23 (D3S2418, cMOD = 4.09, p = 0.0005). Conclusion: The proposed strategy is useful to detect modifier loci conferring susceptibility to a specific phenotype within a broader phenotype.     

Comparison of the linkage results of two phenotypic constructs from longitudinal data in the Framingham Heart Study: analyses on data measured at three time points and on the average of three measurements

Background

Family studies are often conducted in a cross-sectional manner without long-term follow-up data. The relative contribution of a gene to a specific trait could change over the lifetime. The Framingham Heart Study offers a unique opportunity to investigate potential gene × time interaction. We performed linkage analysis on the body mass index (BMI) measured in 1970, 1978, and 1986 for this project.

Results

We analyzed the data in two different ways: three genome-wide linkage analyses on each exam, and one genome-wide linkage analysis on the mean of the three measurements. Variance-component linkage analyses were performed by the SOLAR program. Genome-wide scans show consistent evidence of linkage of quantitative trait loci (QTLs) on chromosomes 3, 6, 9, and 16 in three measurements with a maximum multipoint LOD score > 2.2. However, only chromosome 9 has a LOD score = 2.14 when the mean values were analyzed. More interestingly, we found potential gene × environment interactions: increasing LOD scores with age on chromosomes 3, 9, and 16 and decreasing LOD scores on chromosome 6 in the three exams.

Conclusion

The results indicate two points: 1) it is possible that a gene (or genes) influencing BMI is (are) up- or down-regulated as people aged due to aging process or changes in lifestyle, environments, or genetic epistasis; 2) using mean values from longitudinal data may reduce the power to detect linkage and may have no power to detect gene × time, and/or gene × gene interactions.

     

Residual linkage: why do linkage peaks not disappear after an association study?

Family-based candidate gene and genome-wide association studies are a logical progression from linkage studies for the identification of gene and polymorphisms underlying complex traits. An efficient way to analyse phenotypic and genotypic data is to model linkage and association simultaneously. An important result from such an analysis is whether any evidence for linkage remains after fitting polymorphisms at candidate genes (residual linkage), because this may indicate locus and allelic heterogeneity in the population and will influence subsequent molecular strategies. Here we report that substantial residual linkage is to be expected, even under genetic homogeneity and when the underlying causal polymorphisms are genotyped and fitted in the model. We simulated a powerful design to detect linkage to quantitative trait loci, with 5, 10 or 20 causal SNPs spread throughout the genome. These SNPs were responsible for all genetic variation, and hence for both linkage and association. Residual linkage at the largest linkage peak from a genome-wide scan was substantial, with mean LOD scores of 0.4, 0.7, and 1.4 for the case of 5, 10 and 20 underlying causal SNPs, respectively. For less powerful designs, the proportion of the original LOD scores that remains after association will be even larger. All cases of ‘significant’ residual linkage are false positives. The reason for the apparent paradox of detecting residual linkage after fitting causal polymorphisms is that the linkage signals at the largest peaks in a genome-scan are severely inflated, even if all peaks correspond to true linkage. Our findings are general and apply to linkage mapping of any phenotype and to any pedigree structure.     

Genome-Wide Linkage and Positional Association Analyses Identify Associations of Novel AFF3 and NTM Genes with Triglycerides:The GenSalt Study

We conducted a genome-wide linkage scan and positional association study to identify genes and variants influencing blood lipid levels among participants of the Genetic Epidemiology Network of Salt-Sensitivity(Gen Salt) study. The Gen Salt study was conducted among1906 participants from 633 Han Chinese families. Lipids were measured from overnight fasting blood samples using standard methods.Multipoint quantitative trait genome-wide linkage scans were performed on the high-density lipoprotein, low-density lipoprotein, and logtransformed triglyceride phenotypes. Using dense panels of single nucleotide polymorphisms(SNPs), single-marker and gene-based association analyses were conducted to follow-up on promising linkage signals. Additive associations between each SNP and lipid phenotypes were tested using mixed linear regression models. Gene-based analyses were performed by combining P-values from singlemarker analyses within each gene using the truncated product method(TPM). Significant associations were assessed for replication among777 Asian participants of the Multi-ethnic Study of Atherosclerosis(MESA). Bonferroni correction was used to adjust for multiple testing.In the Gen Salt study, suggestive linkage signals were identified at 2p11.2-2q12.1 [maximum multipoint LOD score(MML)=2.18 at2q11.2] and 11q24.3-11q25(MML=2.29 at 11q25) for the log-transformed triglyceride phenotype. Follow-up analyses of these two regions revealed gene-based associations of charged multivesicular body protein 3(CHMP3), ring finger protein 103(RNF103),AF4/FMR2 family, member 3(AFF3), and neurotrimin(NTM) with triglycerides(P=4 10 4, 1.00 10 5, 2.00 10 5, and1.00 10 7, respectively). Both the AFF3 and NTM triglyceride associations were replicated among MESA study participants(P=1.00 10 7and 8.00 10 5, respectively). Furthermore, NTM explained the linkage signal on chromosome 11. In conclusion, we identified novel genes associated with lipid phenotypes in linkage regions on chromosomes 2 and 11.     

Genetic interactions of modifier genes and modifiable alleles in Drosophila melanogaster

We have examined the effects of mutations in the six allele-specific modifier genes su(Hw), e(we), su(f), su(s), su(wa), and su(pr) on the expression of 18 modifiable alleles, situated at 11 loci. Ten of the modifiable alleles are associated with insertions of the gypsy retrotransposon and the others include alleles associated with insertions of copia and 412. We tested or retested 90 of the 108 possible combinations and examined the expression of modifiable alleles in flies mutant for pairs of modifier genes in various heterozygous and homozygous configurations. Our principal findings are: (1) a screen of 40,000 mutagenized X chromosomes yielded three new mutations in known modifier genes, but revealed no new modifier genes; (2) the modification effects of different mutations in a given modifier gene were qualitatively similar; (3) each of the six modifiers suppressed some modifiable alleles, enhanced others, and had no noticeable effect on still others; (4) the modifier genes could be placed in four classes, according to their effects on the gypsy-insertion alleles; and (5) the effects of mutations in different modifier genes combined additively. Implications of these results for models of modifier gene action are discussed.     

Parkinson disease loci in the mid-western Amish

Previous evidence has shown that Parkinson disease (PD) has a heritable component, but only a small proportion of the total genetic contribution to PD has been identified. Genetic heterogeneity complicates the verification of proposed PD genes and the identification of new PD susceptibility genes. Our approach to overcome the problem of heterogeneity is to study a population isolate, the mid-western Amish communities of Indiana and Ohio. We performed genome-wide association and linkage analyses on 798 individuals (31 with PD), who are part of a 4,998 member pedigree. Through these analyses, we identified a region on chromosome 5q31.3 that shows evidence of association (p value < 1 × 10^?4) and linkage (multipoint HLOD = 3.77). We also found further evidence of linkage on chromosomes 6 and 10 (multipoint HLOD 4.02 and 4.35 respectively). These data suggest that locus heterogeneity, even within the Amish, may be more extensive than previously appreciated.     

Genetic predisposition to familial neuroblastoma: identification of two novel genomic regions at 2p and 12p

OBJECTIVES: The rarity of familial neuroblastoma (NB) has allowed only a few linkage studies, most of which did not show any evidence of linkage to regions involved in somatic alterations or to genes implicated in other neurocristopathies seldom associated with NB. We screened a highly informative family with recurrent NB by genome-wide linkage analysis aimed at identifying chromosomal regions for NB predisposing genes. METHODS: A genome-wide screen was performed using 382 microsatellite markers. Multipoint model-based linkage analysis was carried out under a dominant mode of inheritance for the disease using the 'affected only' approach. RESULTS: Our analysis identified two haplotypes co-segregating with the disease on chromosomes 2p and 12p, and yielded maximum lod-score values of 3.01 (p < 0.0001) for markers on both intervals. CONCLUSIONS: Evidence of linkage was reported at 16p in North American families, whereas our studies excluded this interval and indicated other loci for disease predisposition, thus confirming the remarkable genetic heterogeneity of NB. These results suggest an oligogenic inheritance in NB involving more loci in genetic determination of the disease.     

Performance comparison of two-point linkage methods using microsatellite markers flanking known disease locations

The Genetic Analysis Workshop 14 simulated data presents an interesting, challenging, and plausible example of a complex disease interaction in a dataset. This paper summarizes the ease of detection for each of the simulated Kofendrerd Personality Disorder (KPD) genes across all of the replicates for five standard linkage statistics. Using the KPD affection status, we have analyzed the microsatellite markers flanking each of the disease genes, plus an additional 2 markers that were not linked to any of the disease loci. All markers were analyzed using the following two-point linkage methods: 1) a MMLS, which is a standard admixture LOD score maximized over theta, alpha, and mode of inheritance, 2) a MLS calculated by GENEHUNTER, 3) the Kong and Cox LOD score as computed by MERLIN, 4) a MOD score (standard heterogeneity LOD maximized over theta, alpha, and a grid of genetic model parameters), and 5) the PPL, a Bayesian statistic that directly measures the strength of evidence for linkage to a marker. All of the major loci (D1-D4) were detectable with varying probabilities in the different populations. However, the modifier genes (D5 and D6) were difficult to detect, with similar distributions under the null and alternative across populations and statistics. The pooling of the four datasets in each replicate (n = 350 pedigrees) greatly improved the chance of detecting the major genes using all five methods, but failed to increase the chance to detect D5 and D6.     

Genome-Wide Linkage Study Suggests a Susceptibility Locus for Isolated Bilateral Microtia on 4p15.32–4p16.2

Microtia is a congenital deformity where the external ear is underdeveloped. Genetic investigations have identified many susceptibility genes of microtia-related syndromes. However, no causal genes were reported for isolated microtia, the main form of microtia. We conducted a genome-wide linkage analysis on a 5-generation Chinese pedigree with isolated bilateral microtia. We identified a suggestive linkage locus on 4p15.32–4p16.2 with parametric LOD score of 2.70 and nonparametric linkage score (Zmean) of 12.28 (simulated occurrence per genome scan equal to 0.46 and 0.47, respectively). Haplotype reconstruction analysis of the 4p15.32–4p16.2 region further confined the linkage signal to a 10-Mb segment located between rs12505562 and rs12649803 (9.65–30.24 cM; 5.54–15.58 Mb). Various human organ developmental genes reside in this 10-Mb susceptibility region, such as EVC, EVC2, SLC2A9, NKX3-2, and HMX1. The coding regions of three genes, EVC known for cartilage development and NKX3-2, HMX1 involved in microtia, were selected for sequencing with 5 individuals from the pedigree. Of the 38 identified sequence variants, none segregates along with the disease phenotype. Other genes or DNA sequences of the 10-Mb region warrant for further investigation. In conclusion, we report a susceptibility locus of isolated microtia, and this finding will encourage future studies on the genetic basis of ear deformity.     

The Human Obesity Gene Map: The 1997 Update

An update of the human obesity gene map incorporating published results up to October 1997 is presented. Evidence from Mendelian disorders exhibiting obesity as a clinical feature; single-gene mutation rodent models; quantitative trait loci uncovered in human genome-wide scans and in crossbreeding experiments with mouse, rat, and pig models; association and case-control studies with candidate genes; and linkage studies with genes and other markers is reviewed. All chromosomal locations of the animal loci are converted into human genome locations based on syntenic relationships between the genomes. A complete listing of all of these loci reveals that all but chromosome Y of the 24 human chromosomes are represented. Some chromosomes show at least three putative loci related to obesity on both arms (1, 2, 6, 8, 11, and 20) and several on one chromosome arm only (3p, 4q, 5q, 7q, 12q, 13q, 15q, 15p, 22q, and Xq). Studies reporting negative association and linkage results are also listed, with the exception of the unlinked markers from genome-wide scans.     

Establishing appropriate genome-wide significance levels for canine linkage analyses

A threshold of 3.3 for a genome-wide maximum LOD score (MAXLOD) has been demonstrated in human linkage studies as corresponding to a type I error rate of 5%. Generalization of this work to other species assumes the presence of an infinitely dense marker map. While this assumption is increasingly realistic for the human genome, it may be unrealistic for the dog genome. In this study we establish the analytic and empirical thresholds for MAXLOD in canine linkage studies corresponding to type I error rates of 5% and 1% for autosomal traits. Empirical thresholds are computed via simulation assuming a 10 cM map with no fine mapping performed. Pedigree structures for simulations were drawn from two canine disease studies. Five thousand replicates of genome-wide null genotype data were simulated and analyzed for each disease. We determined that MAXLOD thresholds of 3.2 and 2.7 correspond to analytic and empirical type I error rates of 5%, respectively. In all cases, the MAXLOD thresholds from simulations were always at least 0.5 LOD units below the corresponding analytic thresholds. We therefore recommend that a threshold of 3.2 be used for canine linkage studies when fine mapping is performed, and that researchers perform their own simulation studies to assess genome-wide empirical significance levels when no fine mapping is performed.     

Modifier gene study of meconium ileus in cystic fibrosis: statistical considerations and gene mapping results

Cystic fibrosis (CF) is a monogenic disease due to mutations in the CFTR gene. Yet, variability in CF disease presentation is presumed to be affected by modifier genes, such as those recently demonstrated for the pulmonary aspect. Here, we conduct a modifier gene study for meconium ileus (MI), an intestinal obstruction that occurs in 16–20% of CF newborns, providing linkage and association results from large family and case–control samples. Linkage analysis of modifier traits is different than linkage analysis of primary traits on which a sample was ascertained. Here, we articulate a source of confounding unique to modifier gene studies and provide an example of how one might overcome the confounding in the context of linkage studies. Our linkage analysis provided evidence of a MI locus on chromosome 12p13.3, which was segregating in up to 80% of MI families with at least one affected offspring (HLOD = 2.9). Fine mapping of the 12p13.3 region in a large case–control sample of pancreatic insufficient Canadian CF patients with and without MI pointed to the involvement of ADIPOR2 in MI (p = 0.002). This marker was substantially out of Hardy–Weinberg equilibrium in the cases only, and provided evidence of a cohort effect. The association with rs9300298 in the ADIPOR2 gene at the 12p13.3 locus was replicated in an independent sample of CF families. A protective locus, using the phenotype of no-MI, mapped to 4q13.3 (HLOD = 3.19), with substantial heterogeneity. A candidate gene in the region, SLC4A4, provided preliminary evidence of association (p = 0.002), warranting further follow-up studies. Our linkage approach was used to direct our fine-mapping studies, which uncovered two potential modifier genes worthy of follow-up.     

Human QTL linkage mapping

Human quantitative trait locus (QTL) linkage mapping, although based on classical statistical genetic methods that have been around for many years, has been employed for genome-wide screening for only the last 10–15 years. In this time, there have been many success stories, ranging from QTLs that have been replicated in independent studies to those for which one or more genes underlying the linkage peak have been identified to a few with specific functional variants that have been confirmed in in vitro laboratory assays. Despite these successes, there is a general perception that linkage approaches do not work for complex traits, possibly because many human QTL linkage studies have been limited in sample size and have not employed the family configurations that maximize the power to detect linkage. We predict that human QTL linkage studies will continue to be productive for the next several years, particularly in combination with RNA expression level traits that are showing evidence of regulatory QTLs of large effect sizes and in combination with high-density genome-wide SNP panels. These SNP panels are being used to identify QTLs previously localized by linkage and linkage results are being used to place informative priors on genome-wide association studies.     

Detecting susceptibility genes in case-control studies using set association

Complex diseases are generally caused by intricate interactions of multiple genes and environmental factors. Most available linkage and association methods are developed to identify individual susceptibility genes assuming a simple disease model blind to any possible gene - gene and gene - environmental interactions. We used a set association method that uses single-nucleotide polymorphism markers to locate genetic variation responsible for complex diseases in which multiple genes are involved. Here we extended the set association method from bi-allelic to multiallelic markers. In addition, we studied the type I error rates and power for both approaches using simulations based on the coalescent process. Both bi-allelic set association (BSA) and multiallelic set association (MSA) tests have the correct type I error rates. In addition, BSA and MSA can have more power than individual marker analysis when multiple genes are involved in a complex disease. We applied the MSA approach to the simulated data sets from Genetic Analysis Workshop 13. High cholesterol level was used as the definitive phenotype for a disease. MSA failed to detect markers with significant linkage disequilibrium with genes responsible for cholesterol level. This is due to the wide spacing between the markers and the lack of association between the marker loci and the simulated phenotype.     

Power and type I error rate of false discovery rate approaches in genome-wide association studies

In genome-wide genetic studies with a large number of markers, balancing the type I error rate and power is a challenging issue. Recently proposed false discovery rate (FDR) approaches are promising solutions to this problem. Using the 100 simulated datasets of a genome-wide marker map spaced about 3 cM and phenotypes from the Genetic Analysis Workshop 14, we studied the type I error rate and power of Storey's FDR approach, and compared it to the traditional Bonferroni procedure. We confirmed that Storey's FDR approach had a strong control of FDR. We found that Storey's FDR approach only provided weak control of family-wise error rate (FWER). For these simulated datasets, Storey's FDR approach only had slightly higher power than the Bonferroni procedure. In conclusion, Storey's FDR approach is more powerful than the Bonferroni procedure if strong control of FDR or weak control of FWER is desired. Storey's FDR approach has little power advantage over the Bonferroni procedure if there is low linkage disequilibrium among the markers. Further evaluation of the type I error rate and power of the FDR approaches for higher linkage disequilibrium and for haplotype analyses is warranted.     

New loci regulating rat myelin oligodendrocyte glycoprotein-induced experimental autoimmune encephalomyelitis

Myelin oligodendrocyte glycoprotein-induced experimental autoimmune encephalomyelitis (EAE) is an inflammatory disease in rats that closely mimics many clinical and histopathological aspects of multiple sclerosis. Non-MHC quantitative trait loci regulating myelin oligodendrocyte glycoprotein-induced EAE have previously been identified in the EAE-permissive strain, DA, on rat chromosomes 4, 10, 15, and 18. To find any additional gene loci in another well-known EAE-permissive strain and thereby to assess any genetic heterogeneity in the regulation of the disease, we have performed a genome-wide linkage analysis in a reciprocal (LEW.1AV1 x PVG.1AV1) male/female F(2) population (n = 185). We examined reciprocal crosses, but no parent-of-origin effect was detected. The parental rat strains share the RT1(av1) MHC haplotype; thus, non-MHC genes control differences in EAE susceptibility. We identified Eae16 on chromosome 8 and Eae17 on chromosome 13, significantly linked to EAE phenotypes. Two loci, on chromosomes 1 and 17, respectively showed suggestive linkage to clinical and histopathological EAE phenotypes. Eae16 and Eae17 differ from those found in previously studied strain combinations, thus demonstrating genetic heterogeneity of EAE. Furthermore, we detected a locus-specific parent-of-origin effect with suggestive linkage in Eae17. Further genetic and functional dissection of these loci may disclose critical disease-regulating molecular mechanisms.     

Use of a random coefficient regression (RCR) model to estimate growth parameters

We used a random coefficient regression (RCR) model to estimate growth parameters for the time series of observed serum glucose levels in the Replicate 1 of the Genetic Analysis Workshop 13 simulated data. For comparison, a two time-point interval was also selected and the slope between these two observations was calculated. This process yielded four phenotypes: the RCR growth phenotype, a two time-point slope phenotype, and Time 1 and Time 2 serum glucose level phenotypes. These four phenotypes were used for linkage analyses on simulated chromosomes 5, 7, 9, and 21, those chromosomes that contained loci affecting the growth course for serum glucose levels. The linkage analysis of the RCR-derived phenotype showed overwhelming evidence for linkage at one locus (LOD 65.78 on chromosome 5), while showing elevated but nonsignificant LOD scores for two other loci (LOD 1.25 on chromosome 7, LOD 1.10 on chromosome 9), and no evidence of linkage for the final locus. The two time-point slope phenotype showed evidence for linkage at one locus (LOD 4.16 on chromosome 5) but no evidence for linkage at any of the other loci. A parallel cross-sectional approach, using as input phenotypes the endpoints of the two-point slope phenotype, gave strong linkage results for the major locus on chromosome 5 (maximal LOD scores of 17.90 and 27.24 for Time 1 and Time 2, respectively) while showing elevated but nonsignificant linkage results on chromosome 7 (maximal LOD scores of 1.71 and 1.48) and no evidence for linkage at the two remaining loci. The RCR growth parameter showed more power to detect linkage to the major locus than either the cross-sectional or two-point slope approach, but the cross-sectional approach gave a higher maximal LOD score for one of the minor loci.     

Data acquisition for meta-analysis of genome-wide linkage studies using the genome search meta-analysis method

BACKGROUND: The Genome Search Meta-Analysis (GSMA) method enables researchers to pool results across genome-wide linkage studies, to increase the power to detect linkage. Results from individual studies must be extracted, with the maximum evidence for linkage placed into bins, usually of 30 cM width, and ranked within the study. Ranks are then summed across studies, with high summed ranks potentially showing evidence for linkage in the meta-analysis. OBJECTIVES: In this paper we study the properties of the GSMA method considering two different issues: (1) data binning from genome-wide results when indexed markers or graphs are available, based on either predefined boundary markers, or equal-length bins; (2) the use of selected instead of genome-wide results, using simulation to estimate power and type I error rates of GSMA. This is relevant when published papers show only summary results (e.g. with NPL score >1). Results: Using digitizing software to extract linkage statistics from graphs and assigning equal bin length is accurate, with the resulting ranking of bins similar to those defined through boundary markers. Simulation results show that power can fall substantially when genome-wide results are not available, particularly when only results from a single marker are available in a linked region. However there is no increase in false positive findings. CONCLUSIONS: The GSMA method is robust across different bin definitions and methods of data presentation and extraction. Using studies based on only the top ranked bins does not produce false positive results, but lacks power to detect genes conferring a modest increase in risk. Therefore, we advise that effort should be made to obtain genome-wide results from investigators or from published papers to avoid limiting the utility of the GSMA.     

Haplotype sharing analysis using mantel statistics

OBJECTIVE: The potential value of haplotypes has attracted widespread interest in the mapping of complex traits. Haplotype sharing methods take the linkage disequilibrium information between multiple markers into account, and may have good power to detect predisposing genes. We present a new approach based on Mantel statistics for spacetime clustering, which is developed in order to improve the power of haplotype sharing analysis for gene mapping in complex disease. METHODS: The new statistic correlates genetic similarity and phenotypic similarity across pairs of haplotypes for case-only and case-control studies. The genetic similarity is measured as the shared length between haplotypes around a putative disease locus. The phenotypic similarity is measured as the mean-corrected cross-product based on the respective phenotypes. We analyzed two tests for statistical significance with respect to type I error: (1) assuming asymptotic normality, and (2) using a Monte Carlo permutation procedure. The results were compared to the chi(2) test for association based on 3-marker haplotypes. RESULTS: The results of the type I error rates for the Mantel statistics using the permutational procedure yielded pointwise valid tests. The approach based on the assumption of asymptotic normality was seriously liberal. CONCLUSION: Power comparisons showed that the Mantel statistics were better than or equal to the chi(2) test for all simulated disease models.