Mapping Complex Disease Research Articles

The role of Self‐Defined Race/Ethnicity in Population Structure Control

Population-based association studies are powerful tools for the genetic mapping of complex diseases. However, this method is sensitive to potential confounding by population structure. While statistical methods that use genetic markers to detect and control for population structure have been the focus of current literature, the utility of self-defined race/ethnicity in controlling for population structure has been controversial. In this study of 1334 individuals, who self-identified as either African American, European American or Hispanic, we demonstrated that when the true underlying genetic structure and the self-defined racial/ethnic groups were roughly in agreement with each other, the self-defined race/ethnicity information was useful in the control of population structure.

A Combinatorial Searching Method for Detecting a Set of Interacting Loci Associated with Complex Traits

Complex diseases are presumed to be the results of the interaction of several genes and environmental factors, with each gene only having a small effect on the disease. Mapping complex disease genes therefore becomes one of the greatest challenges facing geneticists. Most current approaches of association studies essentially evaluate one marker or one gene (haplotype approach) at a time. These approaches ignore the possibility that effects of multilocus functional genetic units may play a larger role than a single-locus effect in determining trait variability. In this article, we propose a Combinatorial Searching Method (CSM) to detect a set of interacting loci (may be unlinked) that predicts the complex trait. In the application of the CSM, a simple filter is used to filter all the possible locus-sets and retain the candidate locus-sets, then a new objective function based on the cross-validation and partitions of the multi-locus genotypes is proposed to evaluate the retained locus-sets. The locus-set with the largest value of the objective function is the final locus-set and a permutation procedure is performed to evaluate the overall p-value of the test for association between the final locus-set and the trait. The performance of the method is evaluated by simulation studies as well as by being applied to a real data set. The simulation studies show that the CSM has reasonable power to detect high-order interactions. When the CSM is applied to a real data set to detect the locus-set (among the 13 loci in the ACE gene) that predicts systolic blood pressure (SBP) or diastolic blood pressure (DBP), we found that a four-locus gene-gene interaction model best predicts SBP with an overall p-value = 0.033, and similarly a two-locus gene-gene interaction model best predicts DBP with an overall p-value = 0.045.

Efficient Study Designs for Test of Genetic Association Using Sibship Data and Unrelated Cases and Controls

Linkage mapping of complex diseases is often followed by association studies between phenotypes and marker genotypes through use of case-control or family-based designs. Given fixed genotyping resources, it is important to know which study designs are the most efficient. To address this problem, we extended the likelihood-based method of Li et al., which assesses whether there is linkage disequilibrium between a disease locus and a SNP, to accommodate sibships of arbitrary size and disease-phenotype configuration. A key advantage of our method is the ability to combine data from different family structures. We consider scenarios for which genotypes are available for unrelated cases, affected sib pairs (ASPs), or only one sibling per ASP. We construct designs that use cases only and others that use unaffected siblings or unrelated unaffected individuals as controls. Different combinations of cases and controls result in seven study designs. We compare the efficiency of these designs when the number of individuals to be genotyped is fixed. Our results suggest that (1) when the disease is influenced by a single gene, the one sibling per ASP-control design is the most efficient, followed by the ASP-control design, and familial cases contribute more association information than singleton cases; (2) when the disease is influenced by multiple genes, familial cases provide more association information than singleton cases, unless the effect of the locus being tested is much smaller than at least one other untested disease locus; and (3) the case-control design can be useful for detecting genes with small effect in the presence of genes with much larger effect. Our findings will be helpful for researchers designing and analyzing complex disease-association studies and will facilitate genotyping resource allocation.

Genetic architecture of the F7 gene in a Spanish population: implication for mapping complex diseases and for functional assays

Delineating the genetic variability of loci coding for complex diseases helps to understand the individual variation in disease susceptibility and drug response. We present the allelic architecture of the F7 gene. This gene is the major determinant of FVII plasma levels, and these plasma levels constitute an important intermediate risk factor for cardiovascular disease. As part of the Genetic Analysis of Idiopathic Thrombophila Project, we completely re-sequenced the F7 locus (promoter, exons, introns, and 3'-untranslated region) in 40 unrelated individuals. We found 49 polymorphisms with only two amino acid changes suggesting that regulatory non-coding and intronic variants are responsible for the FVII variability. These results are important for mapping susceptibility alleles of complex diseases, because differences in pair-wise linkage disequilibrium patterns between DNA variants and haplotype frequency distributions may help to detect disease-associated alleles. In addition, we present the results of an in silico search that established genomic comparisons among different species. In conclusion, our study of the F7 DNA sequence variations is an example of a strategy for analyzing the genetic architecture of a quantitative trait locus. Furthermore, it provides a model for future analyses of genetic factors that contribute to the susceptibility of complex diseases in humans.

An evaluation of the replicate pool method: quick estimation of genome‐wide linkage peak p‐values

The calculation of empirical p-values for genome-wide non-parametric linkage tests continues to present significant computational challenges for many complex disease mapping studies. The gold standard approach is to use gene dropping to simulate null genome scans. Unfortunately, this approach is too computationally expensive for many data sets of interest. An alternative, more efficient method for sampling null genome scans is to pre-calculate pools of family-specific statistics and then resample from these replicate pools to generate "pseudo-replicate" genome scans. In this study, we use simulations to explore properties of the replicate pool p-value estimator pRP and show that it provides an excellent approximation to the traditional gene-dropping estimator for significantly less computational effort. While the computational efficiency of the replicate pool estimator is noticeable in almost all data sets, by applying the replicate pool method to several previously characterized data sets we show that savings in computational effort can be especially significant (on the order of 10,000-fold or more) when one or more large families are analyzed. We also estimate replicate pool p-values for the schizophrenia data described by Abecasis et al. and show that pRP closely approximates gene-drop p-values for all linkage peaks reported for this study. Lastly, we expand upon Song et al.'s previous work by deriving a conservative estimator of the variance for PRP that can easily be computed in practical settings. We have implemented the replicate pool method along with our variance estimator in a new program called Pseudo, which is the first widely available automated implementation of the replicate pool method.

A Fine-Scale Linkage-Disequilibrium Measure Based on Length of Haplotype Sharing

High-throughput genotyping technologies for SNPs have enabled the recent completion of the International HapMap Project (phase I), which has stimulated much interest in studying genomewide linkage-disequilibrium (LD) patterns. Conventional LD measures, such as D' and r(2), are two-point measurements, and their relationship with physical distance is highly noisy. We propose a new LD measure, Delta , defined in terms of the correlation coefficient for shared haplotype lengths around two loci, thereby borrowing information from multiple loci. A U-statistic-based estimator of Delta , which takes into consideration the dependence structure of the observed data, is developed and compared with an estimator based on the usual empirical correlation coefficient. Furthermore, we propose methods for inferring LD-decay rates and recombination hotspots on the basis of Delta . The results from coalescent-simulation studies and analysis of HapMap SNP data demonstrate that the proposed estimators of Delta are superior to the two most popular conventional LD measures, in terms of their close relationship with physical distance and recombination rate, their small variability, and their strong robustness to marker-allele frequencies. These merits may offer new opportunities for mapping complex disease genes and for investigating recombination mechanisms on the basis of better-quantified LD.

PopGen: Population-Based Recruitment of Patients and Controls for the Analysis of Complex Genotype-Phenotype Relationships

Objective: Patient samples used for mapping complex human disease genes are unlikely to be representative of the phenotype spectrum of the respective population as a whole. On the other hand, most ongoing prospective studies are probably too small for evaluating polygenic disease markers. Design: Precise estimates of population-specific genotypic risks can be obtained efficiently through the complete ascertainment of patients in a geographically confined area. The PopGen project uses the most northern part of Germany as a target region for such a pursuit. Results: PopGen currently pursues recruitment, sampling and processing activities in close collaboration with a multitude of clinical partners, covering cardiovascular, neuropsychiatric and environmental diseases. Conclusion: PopGen has successfully established itself as a large-scale genetic epidemiological project of international recognition.

Comparisons of case-selection approaches based on allele sharing and/or disease severity index: application to the GAW14 simulated data

For mapping complex disease traits, linkage studies are often followed by a case-control association strategy in order to identify disease-associated genes/single-nucleotide polymorphisms (SNPs). Substantial efforts are required in selecting the most informative cases from a large collection of affected individuals in order to maximize the power of the study, while taking into consideration study cost. In this article, we applied and extended three case-selection strategies that use allele-sharing information method for families with multiple affected offspring to select most informative cases using additional information on disease severity. Our results revealed that most significant associations, as measured by the lowest p-values, were obtained from a strategy that selected a case with the most allele sharing with other affected sibs from linked families ("linked-best"), despite reduction in sample size resulting from discarding unlinked families. Moreover, information on disease severity appears to be useful to improve the ability to detect associations between markers and disease loci.

Comparison of family-based association tests in chromosome regions selected by linkage-based confidence intervals

We use the Genetic Analysis Workshop 14 simulated data to explore the effectiveness of a two-stage strategy for mapping complex disease loci consisting of an initial genome scan with confidence interval construction for gene location, followed by fine mapping with family-based tests of association on a dense set of single-nucleotide polymorphisms. We considered four types of intervals: the 1-LOD interval, a basic percentile bootstrap confidence interval based on the position of the maximum Zlr score, and asymptotic and bootstrap confidence intervals based on a generalized estimating equations method. For fine mapping we considered two family-based tests of association: a test based on a likelihood ratio statistic and a transmission-disequilibrium-type test implemented in the software FBAT. In two of the simulation replicates, we found that the bootstrap confidence intervals based on the peak Zlr and the 1-LOD support interval always contained the true disease loci and that the likelihood ratio test provided further strong confirmatory evidence of the presence of disease loci in these regions.

Approaching Inherited Disease on a Genomic Scale

We review current approaches that can extend our understanding of monogenic disease towards complex disease. Recent studies showed that currently established disease genes differ in their protein size, tissue specificity and the phylogenetic distribution of homologs. These characteristics can be explained by the fact that monogenic disease mutations must be sufficiently deleterious to produce a clearly recognizable phenotype, but also must not be lethal in an early embryonic stage. On the other hand, deletion of each gene in the human genome must be evolutionarily disadvantageous. For most genes, this disadvantage might manifest as an increased susceptibility to complex disease. Accordingly, mildly deleterious variants can be observed in a wide spectrum of genes. The phenotypic manifestation of these mildly deleterious variants might depend on somatic mutations, which cause the breakdown of compensating mechanisms in individual cells. At present, association studies are the most promising strategy for mapping complex disease phenotypes. However, these are restricted to the identification of common disease variants and often provide only marginally convincing statistical evidence. Novel computational strategies, which take prior biological knowledge into account, therefore might play a major role in the design and interpretation of large-scale association studies. Keywords: human genes, phenotype, genome sequence, HapMap project, genetic variants

Haplotype-based linkage disequilibrium mapping via direct data mining

With the availability of large-scale, high-density single-nucleotide polymorphism markers and information on haplotype structures and frequencies, a great challenge is how to take advantage of haplotype information in the association mapping of complex diseases in case-control studies. We present a novel approach for association mapping based on directly mining haplotypes (i.e. phased genotype pairs) produced from case-control data or case-parent data via a density-based clustering algorithm, which can be applied to whole-genome screens as well as candidate-gene studies in small genomic regions. The method directly explores the sharing of haplotype segments in affected individuals that are rarely present in normal individuals. The measure of sharing between two haplotypes is defined by a new similarity metric that combines the length of the shared segments and the number of common alleles around any marker position of the haplotypes, which is robust against recent mutations/genotype errors and recombination events. The effectiveness of the approach is demonstrated by using both simulated datasets and real datasets. The results show that the algorithm is accurate for different population models and for different disease models, even for genes with small effects, and it outperforms some recently developed methods.

Sibship T2 association tests of complex diseases for tightly linked markers

For population case-control association studies, the false-positive rates can be high due to inappropriate controls, which can occur if there is population admixture or stratification. Moreover, it is not always clear how to choose appropriate controls. Alternatively, the parents or normal sibs can be used as controls of affected sibs. For late-onset complex diseases, parental data are not usually available. One way to study late-onset disorders is to perform sib-pair or sibship analyses. This paper proposes sibship-based Hotelling's T2 test statistics for high-resolution linkage disequilibrium mapping of complex diseases. For a sample of sibships, suppose that each sibship consists of at least one affected sib and at least one normal sib. Assume that genotype data of multiple tightly linked markers/haplotypes are available for each individual in the sample. Paired Hotelling's T2 test statistics are proposed for high-resolution association studies using normal sibs as controls for affected sibs, based on two coding methods: 'haplotype/allele coding' and 'genotype coding'. The paired Hotelling's T2 tests take into account not only the correlation among the markers, but also take the correlation within each sib-pair. The validity of the proposed method is justified by rigorous mathematical and statistical proofs under the large sample theory. The non-centrality parameter approximations of the test statistics are calculated for power and sample size calculations. By carrying out power and simulation studies, it was found that the non-centrality parameter approximations of the test statistics were accurate. By power and type I error analysis, the test statistics based on the 'haplotype/allele coding' method were found to be advantageous in comparison to the test statistics based on the 'genotype coding' method. The test statistics based on multiple markers can have higher power than those based on a single marker. The test statistics can be applied not only for bi-allelic markers, but also for multi-allelic markers. In addition, the test statistics can be applied to analyse the genetic data of multiple markers which contain double heterozygotes -- that is, unknown linkage phase data. An SAS macro, Hotel_sibs.sas, is written to implement the method for data analysis.

TXGnet: Y.F. Leung’s Functional Genomics

With its sequencing completed in 2003, scientists set their sights on determining the basic structure and inner workings of the human genome. This movement has spawned numerous new scientific specialties that have been supported by the growth of data-generating technologies. One of these interdisciplinary fields, functional genomics, is devoted to linking gene expression to function (or dysfunction) in cells, organs, and tissue. On his website titled Y.F. Leung’s Functional Genomics, located at http://genomicshome.com/, Harvard researcher Yuk Fai Leung sketches out the current state of this new field of study. The homepage of the site is divided into three central sections. The main section, titled All About Functional Genomics, is an assemblage of links to relevant outside resources such as “omics” glossaries and the Department of Energy Genomes to Life program. Also in this section are pages of resources for related fields including bioinformatics and proteomics. Leung has also brought together resources on the use of chaos and nonlinear dynamics in genomics, and on the ethical, legal, and social issues surrounding genomics. A group of links to institutes and core facilities conducting functional genomics work is also provided. Microarray technology has been crucial to the development of functional genomics. The Microarrays subsection gives an overview of what exactly these tools are, as described through videos, technology reviews, even cartoons, and provides descriptions of all of the various equipment and technology required to perform this sort of analysis. The Language & Standard subpage lists links to resources on communicating results with others within the discipline. Listings of relevant courses, video seminars, conferences, and workshops are also available. The Bioinformatics subsection of the website contains links to more than 50 databases. This subsection, like the Microarrays subsection, also has pages devoted to the language and algorithms used in bioinformatics as well as data standardization. The Ontology page has links to resources on the efforts to develop a standard, universal vocabulary that can be used across the “omics” fields for all organisms. Other subsections are devoted to proteomics and to genome mapping, complex disease mapping, and linkage analysis. They are populated much as the other two subsections, with pages of glossaries, educational opportunities, calendars, and the like. This website also has a novel Feature Sections element that bench scientists will find useful. Leung has put together a microarray software comparison featuring 13 primary types of software, including programs for data preprocessing, analysis, and annotation. The page for each software type has a definition of the software’s use, suggested readings, and lists of the products available in each category. Also on offer is a compendium of peer-reviewed journal articles related to functional genomics, including a list of biotech business articles. Leung also provides a reading list of books on functional genomics topics and an in-depth functional genomics glossary.

A Powerful Strategy to Account for Multiple Testing in the Context of Haplotype Analysis

Haplotypes--that is, linear arrangements of alleles on the same chromosome that were inherited as a unit--are expected to carry important information in the context of association fine mapping of complex diseases. In consideration of a set of tightly linked markers, there is an enormous number of different marker combinations that can be analyzed. Therefore, a severe multiple-testing problem is introduced. One method to deal with this problem is Bonferroni correction by the number of combinations that are considered. Bonferroni correction is appropriate for independent tests but will result in a loss of power in the presence of linkage disequilibrium in the region. A second method is to perform simulations. It is unfortunate that most methods of haplotype analysis already require simulations to obtain an uncorrected P value for a specific marker combination. Thus, it seems that nested simulations are necessary to obtain P values that are corrected for multiple testing, which, apparently, limits the applicability of this approach because of computer running-time restrictions. Here, an algorithm is described that avoids such nested simulations. We check the validity of our approach under two disease models for haplotype analysis of family data. The true type I error rate of our algorithm corresponds to the nominal significance level. Furthermore, we observe a strong gain in power with our method to obtain the global P value, compared with the Bonferroni procedure to calculate the global P value. The method described here has been implemented in the latest update of our program FAMHAP.

Mapping complex disease loci in whole-genome association studies.

Identification of the genetic polymorphisms that contribute to susceptibility for common diseases such as type 2 diabetes and schizophrenia will aid in the development of diagnostics and therapeutics. Previous studies have focused on the technique of genetic linkage, but new technologies and experimental resources make whole-genome association studies more feasible. Association studies of this type have good prospects for dissecting the genetics of common disease, but they currently face a number of challenges, including problems with multiple testing and study design, definition of intermediate phenotypes and interaction between polymorphisms.

Family-based association study of schizophrenia with 444 markers and analysis of a new susceptibility locus mapped to 11q13.3.

Family-based linkage disequilibrium (LD) mapping has been suggested as a powerful and practical alternative to linkage analysis. We have performed a genome-wide LD survey of susceptibility loci for schizophrenia in a Japanese population. We first typed 119 schizophrenic pedigrees (357 individuals) using 444 microsatellite markers, and analyzed the data using the pedigree disequilibrium test. This analysis revealed 14 markers demonstrating significant transmission distortion. To corroborate these findings, the statistical methods were changed to the extended transmission disequilibrium test (ETDT), using 80 independent complete trios (schizophrenic proband and both parents), with 68 derived from initial pedigrees and 12 newly recruited trios. ETDT supported two markers for continued association, D11S987 on 11q13.3 (P = 0.00009) and D16S423 on 16p13.3 (P = 0.002). We scrutinized the most significant genomic locus on 11q11-13 by adding 26 new markers for analysis. Results of three-marker haplotype analysis in the region showed evidence of association with schizophrenia (most significant haplotype P = 0.0005, global P = 0.022). Although the present study may have missed other potential genomic intervals because of the sparse mapping density, we hope that it has identified promising anchor points for further studies to identify risk-conferring genes for schizophrenia in the Japanese population. In addition, we provide useful information on genomic LD structures in Japanese populations, which can be used for LD mapping of complex diseases.

Genotyping error detection through tightly linked markers.

The identification of genotyping errors is an important issue in mapping complex disease genes. Although it is common practice to genotype multiple markers in a candidate region in genetic studies, the potential benefit of jointly analyzing multiple markers to detect genotyping errors has not been investigated. In this article, we discuss genotyping error detections for a set of tightly linked markers in nuclear families, and the objective is to identify families likely to have genotyping errors at one or more markers. We make use of the fact that recombination is a very unlikely event among these markers. We first show that, with family trios, no extra information can be gained by jointly analyzing markers if no phase information is available, and error detection rates are usually low if Mendelian consistency is used as the only standard for checking errors. However, for nuclear families with more than one child, error detection rates can be greatly increased with the consideration of more markers. Error detection rates also increase with the number of children in each family. Because families displaying Mendelian consistency may still have genotyping errors, we calculate the probability that a family displaying Mendelian consistency has correct genotypes. These probabilities can help identify families that, although showing Mendelian consistency, may have genotyping errors. In addition, we examine the benefit of available haplotype frequencies in the general population on genotyping error detections. We show that both error detection rates and the probability that an observed family displaying Mendelian consistency has correct genotypes can be greatly increased when such additional information is available.

Markers informative for ancestry demonstrate consistent megabase-length linkage disequilibrium in the African American population.

Admixture mapping is a potentially powerful tool for mapping complex genetic diseases. For application of this method, admixed individuals must have genomes composed of large segments derived intact from each founding population. Such segments are thought to be present in African Americans (AA) and should be demonstrable by examination of linkage disequilibrium (LD). Previous studies using a variety of polymorphic markers have variably reported long-range LD or rapid decay of LD. To further define the extent and characteristics of LD caused by admixture in the AA population, the current study utilized a set of 52 diallelic markers that were selected for large standard variances between putative representatives of the founder populations. LD was examined in over 250 marker-pairs, including linked markers from four different chromosomal regions and an equal number of matched unlinked comparisons. In the representative founder populations, strong LD was not observed for markers separated by more than 10 kb. In contrast, results indicated significant LD ( P<0.001, D'>0.3) in AA over large genomic segments exceeding 10 centiMorgans (cM) and 15 megabases (Mb). Only marginally significant LD was present between unlinked markers in this population, suggesting that choosing appropriate levels of significance for admixture mapping can minimize false positive results. The ability to detect LD for extended chromosomal segments in AA decayed not only as a function of the distance between markers, but also as a function of the standard variance of the markers. This examination of several genomic segments provides strong evidence that appropriate selection of informative markers is a crucial prerequisite for the application of admixture mapping to the AA population.

Multivariate sib-pair linkage analysis of longitudinal phenotypes by three step-wise analysis approaches

BackgroundCurrent statistical methods for sib-pair linkage analysis of complex diseases include linear models, generalized linear models, and novel data mining techniques. The purpose of this study was to further investigate the utility and properties of a novel pattern recognition technique (step-wise discriminant analysis) using the chromosome 10 linkage data from the Framingham Heart Study and by comparing it with step-wise logistic regression and linear regression.ResultsThe three step-wise approaches were compared in terms of statistical significance and gene localization. Step-wise discriminant linkage analysis approach performed best; next was step-wise logistic regression; and step-wise linear regression was the least efficient because it ignored the categorical nature of disease phenotypes. Nevertheless, all three methods successfully identified the previously reported chromosomal region linked to human hypertension, marker GATA64A09. We also explored the possibility of using the discriminant analysis to detect gene × gene and gene × environment interactions. There was evidence to suggest the existence of gene × environment interactions between markers GATA64A09 or GATA115E01 and hypertension treatment and gene × gene interactions between markers GATA64A09 and GATA115E01. Finally, we answered the theoretical question "Is a trichotomous phenotype more efficient than a binary?" Unlike logistic regression, discriminant sib-pair linkage analysis might have more power to detect linkage to a binary phenotype than a trichotomous one.ConclusionWe confirmed our previous speculation that step-wise discriminant analysis is useful for genetic mapping of complex diseases. This analysis also supported the possibility of the pattern recognition technique for investigating gene × gene or gene × environment interactions.

Association mapping of complex diseases in linked regions: estimation of genetic effects and feasibility of testing rare variants.

Association mapping in linked regions is a current major approach for the identification of genes for complex diseases. Loci contributing to linkage, even with small values of sibling recurrence risk (lambda(s)), may be equivalent to substantial underlying genetic effects for association studies. For disease alleles with a frequency as low as 1%, highly reliable association studies (80% power for significance level alpha=10(-6)) require only 277, 781, and 1289 families or cases and controls for loci detected with lambda(s) of 1.5, 1.1, and 1.05, respectively, under a multiplicative genetic model. Under alternative models, provided epistatic effects are minor, larger achievable sample sizes will provide sufficient power to map almost any disease gene that may have initially contributed to linkage.