Measuring gametic disequilibrium from multilocus data

A non-threshold region-specific method for detecting rare variants in complex diseases

A region-specific method, NTR (non-threshold rare) variant detection method, was developed—it does not use the threshold for defining rare variants and accounts for directions of effects. NTR also considers linkage disequilibrium within the region and accommodates common and rare variants simultaneously. NTR weighs variants according to minor allele frequency and odds ratio to combine the effects of common and rare variants on disease occurrence into a single score and provides a test statistic to assess the significance of the score. In the simulations, under different effect sizes, the power of NTR increased as the effect size increased, and the type I error of our method was controlled well. Moreover, NTR was compared with several other existing methods, including the combined multivariate and collapsing method (CMC), weighted sum statistic method (WSS), sequence kernel association test (SKAT), and its modification, SKAT-O. NTR yields comparable or better power in simulations, especially when the effects of linkage disequilibrium between variants were at least moderate. In an analysis of diabetic nephropathy data, NTR detected more confirmed disease-related genes than the other aforementioned methods. NTR can thus be used as a complementary tool to help in dissecting the etiology of complex diseases.

Evaluating differences in linkage disequilibrium between populations

We propose two methods to evaluate the statistical significance of differences in linkage disequilibrium (LD) between populations, where LD is measured by the standardised parameter D'. The first method is based on bootstrapping individuals within populations in order to test LD differences for each pair of loci. Using this approach we propose a solution to the problem of testing multiple locus-pairs by means of a single test for the number of pairs that exhibit significant LD differences among populations. The second method provides the Bayesian posterior probability that one population has greater LD than the other for each locus pair. Both methods can handle genotypes with unknown phase, and are demonstrated using two data sets. For the purpose of demonstration, we apply the methods to two different sets of data from humans. First, we explore the issue of LD differences between reproductively isolated populations using a new data set of twelve Xq25 microsatellites, typed in four European populations. Second, we examine evidence for LD differences between Alzheimer cases and controls from the Icelandic population using 19 single nucleotide polymorphisms (SNPs) from a 97 kb region flanking the Apolipoprotein E (APOE) gene on chromosome 19.

Association of molecular markers with toxicity outcomes in a randomized trial of chemotherapy for advanced colorectal cancer: the FOCUS trial

PURPOSE

Predicting efficacy and toxicity could potentially allow individualization of cancer therapy. We investigated putative pharmacogenetic markers of chemotherapy toxicity in a large randomized trial.

PATIENTS, MATERIALS, AND METHODS

Patients were randomly assigned to different sequences of chemotherapy for advanced colorectal cancer. First-line therapy was fluorouracil (FU), irinotecan/FU (IrFU) or oxaliplatin/FU (OxFU). Patients allocated first-line FU had planned second-line irinotecan alone, IrFU, or OxFU. The primary toxicity outcome measure was toxicity-induced delay or dose reduction; the secondary outcome was Common Terminology Criteria of Adverse Events grade >or= 3 toxicity. DNA was analyzed in 1,188 patients; 1,036 were assessable for the primary outcome, including 688 treated with FU, 270 with IrFU (first or second line), 280 with OxFU (first or second line), 184 with irinotecan alone, and 454 with any irinotecan-containing regimen. Ten polymorphisms were assessed: thymidylate synthase-enhancer region (TYMS-ER), thymidylate synthase 1494 (TYMS-1494), dihydropyrimidine dehydrogenase (DPYD), methylenetetrahydrofolate reductase (MTHFR), mutL homolog 1 (MLH1), UDP glucuronyltransferase (UGT1A1), ATP-binding cassette group B gene 1 (ABCB1), x-ray cross-complementing group 1 (XRCC1), glutathione-S-transferase P1 (GSTP1), and excision repair cross-complementing gene 2 (ERCC2). Results Using the primary outcome measure, no polymorphism was significantly associated (P < .01) with the toxicity of any regimen or with the difference in toxicity of IrFU or OxFU versus FU alone. Trends (of doubtful significance) were seen for associations of XRCC1, ERCC2, and GSTP1 with toxicity during irinotecan regimens: XRCC1, primary end point, any irinotecan-containing regimen (P = .045); ERCC2, secondary end point, irinotecan alone (P = .003); GSTP1, secondary end point; IrFU (P = .039); and irinotecan alone (P = .05). There was no evidence of association of UGT1A1*28 with irinotecan toxicity.

CONCLUSION

These results do not support the routine clinical use of the evaluated polymorphisms, including UGT1A1*28. Further investigation of XRCC1, ERCC2, and GSTP1 as potential predictors of irinotecan toxicity is warranted.

A multilocus linkage disequilibrium measure based on mutual information theory and its applications

Evaluating the patterns of linkage disequilibrium (LD) is important for association mapping study as well as for studying the genomic architecture of human genome (e.g., haplotype block structures). Commonly used bi-allelic pairwise measures for assessing LD between two loci, such as r(2) and D', may not make full and efficient use of modern multilocus data. Though extended to multilocus scenarios, their performance is still questionable. Meanwhile, most existing measures for an entire multilocus region, such as normalized entropy difference, do not consider existence of LD heterogeneity across the region under investigation. Additionally, these existing multilocus measures cannot handle distant regions where long-range LD patterns may exist. In this study, we proposed a novel multilocus LD measure developed based on mutual information theory. Our proposed measure described LD pattern between two chromosome regions each of which may consist of multiple loci (including multi-allele loci). As such, the proposed measure can better characterize LD patterns between two arbitrary regions. As potential applications, we developed algorithms on the proposed measure for partitioning haplotype blocks and for selecting haplotype tagging SNPs (htSNPs), which were helpful for follow-up association tests. The results on both simulated and empirical data showed that our LD measure had distinct advantages over pairwise and other multilocus measures. First, our measure was more robust, and can capture comprehensively the LD information between neighboring as well as disjointed regions. Second, haplotype blocks were better described via our proposed measure. Furthermore, association tests with htSNPs from the proposed algorithm had improved power over tests on single markers and on haplotypes.

Haplotype block partitioning as a tool for dimensionality reduction in SNP association studies

BACKGROUND

Identification of disease-related genes in association studies is challenged by the large number of SNPs typed. To address the dilution of power caused by high dimensionality, and to generate results that are biologically interpretable, it is critical to take into consideration spatial correlation of SNPs along the genome. With the goal of identifying true genetic associations, partitioning the genome according to spatial correlation can be a powerful and meaningful way to address this dimensionality problem.

RESULTS

We developed and validated an MCMC Algorithm To Identify blocks of Linkage DisEquilibrium (MATILDE) for clustering contiguous SNPs, and a statistical testing framework to detect association using partitions as units of analysis. We compared its ability to detect true SNP associations to that of the most commonly used algorithm for block partitioning, as implemented in the Haploview and HapBlock software. Simulations were based on artificially assigning phenotypes to individuals with SNPs corresponding to region 14q11 of the HapMap database. When block partitioning is performed using MATILDE, the ability to correctly identify a disease SNP is higher, especially for small effects, than it is with the alternatives considered. Advantages can be both in terms of true positive findings and limiting the number of false discoveries. Finer partitions provided by LD-based methods or by marker-by-marker analysis are efficient only for detecting big effects, or in presence of large sample sizes. The probabilistic approach we propose offers several additional advantages, including: a) adapting the estimation of blocks to the population, technology, and sample size of the study; b) probabilistic assessment of uncertainty about block boundaries and about whether any two SNPs are in the same block; c) user selection of the probability threshold for assigning SNPs to the same block.

CONCLUSION

We demonstrate that, in realistic scenarios, our adaptive, study-specific block partitioning approach is as or more efficient than currently available LD-based approaches in guiding the search for disease loci.

A Markov regression random-effects model for remission of functional disability in patients following a first stroke: a Bayesian approach

Few attempts have been made to model the dynamics of stroke-related disability. It is possible though, using panel data and multi-state Markov regression models that incorporate measured covariates and latent variables (random effects). This study aimed to model a series of functional transitions (following a first stroke) using a three-state Markov model with or without considering random effects. Several proportional hazards parameterizations were considered. A Bayesian approach that utilizes the Markov Chain Monte Carlo (MCMC) and Gibbs sampling functionality of WinBUGS (a Windows-based Bayesian software package) was developed to generate the marginal posterior distributions of the various transition parameters (e.g. the transition rates and transition probabilities). Model building and comparisons was guided by reference to the deviance information criteria (DIC). Of the four proportional hazards models considered, exponential regression was preferred because it led to the smallest deviances. Adding random effects further improved the model fit. Of the covariates considered, only age, infarct size, and baseline functional status were significant. By using our final model we were able to make individual predictions about functional recovery in stroke patients.

The use of allelic expression differences to ascertain functional polymorphisms acting in cis: analysis of MMP1 transcripts in normal lung tissue

Summary Aberrant expression of matrix metalloproteinase 1 (MMP1) has been implicated in a number of pathological conditions of the lung. In vitro results and analysis of tumours and cell lines suggest that an insertion/deletion polymorphism at position -1607 in the promoter of the gene can influence expression levels. However, whether this polymorphism is associated with differences in expression in normal lung tissue remains to be established. Polymorphisms affecting expression in cis will lead to alleles with different expression levels and will result in unequal expression of both alleles in heterozygous individuals (allelic expression imbalance, AEI). This can be detected using a transcribed marker. Here we follow a new approach and use AEI to ascertain that the -1607 polymorphism is associated with allelic expression differences of MMP1 in normal lung tissue. This approach could be used to map the sites associated with inter-individual expression differences in other genes. This is of particular interest since such sites allow prediction of expression levels, and can be used to test whether genetically determined differences in expression influence inter-individual differences of a phenotype of interest, such as disease predisposition.

SNPs, haplotypes, and model selection in a candidate gene region: the SIMPle analysis for multilocus data

Modern molecular techniques make discovery of numerous single nucleotide polymorphims (SNPs) in candidate gene regions feasible. Conventional analysis relies on either independent tests with each variant or the use of haplotypes in association analysis. The first technique ignores the dependencies between SNPs. The second, though it may increase power, often introduces uncertainty by estimating haplotypes from population data. Additionally, as the number of loci expands for a haplotype, ambiguity in interpretation increases for determining the underlying genetic components driving a detected association. Here, we present a genotype-level analysis to jointly model the SNPs via a SNP interaction model with phase information (SIMPle) to capture the underlying haplotype structure. This analysis estimates both the risk associated with each variant and the importance of phase between pairwise combinations of SNPs. Thus, rather than selecting between genotype- or haplotype-level approaches, the SIMPle method frames the analysis of multilocus data in a model selection paradigm, the aim to determine which SNPs, phase terms, and linear combinations best describe the relation between genetic variation and a trait of interest. To avoid unstable estimation due to sparse data and to incorporate both the dependencies among terms and the uncertainty in model selection, we propose a Bayes model averaging procedure. This highlights key SNPs and phase terms and yields a set of best representative models. Using simulations, we demonstrate the utility of the SIMPle model to identify crucial SNPs and underlying haplotype structures across a variety of causal models and genetic architectures.

Simultaneous detection of linkage disequilibrium and genetic differentiation of subdivided populations

We propose a new method for simultaneously detecting linkage disequilibrium and genetic structure in subdivided populations. Taking subpopulation structure into account with a hierarchical model, we estimate the magnitude of genetic differentiation and linkage disequilibrium in a metapopulation on the basis of geographical samples, rather than decompose a population into a finite number of random-mating subpopulations. We assume that Hardy-Weinberg equilibrium is satisfied in each locality, but do not assume independence between marker loci. Linkage states remain unknown. Genetic differentiation and linkage disequilibrium are expressed as hyperparameters describing the prior distribution of genotypes or haplotypes. We estimate related parameters by maximizing marginal-likelihood functions and detect linkage equilibrium or disequilibrium by the Akaike information criterion. Our empirical Bayesian model analyzes genotype and haplotype frequencies regardless of haploid or diploid data, so it can be applied to most commonly used genetic markers. The performance of our procedure is examined via numerical simulations in comparison with classical procedures. Finally, we analyze isozyme data of ayu, a severely exploited fish species, and single-nucleotide polymorphisms in human ALDH2.

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 approximately 30%. For low QTL heritabilities (h2QTL<0.1) and single trait studies we recommend selecting around 5% of the upper and lower tails of the trait distribution.

Assessing the performance of the haplotype block model of linkage disequilibrium

Several recent studies have suggested that linkage disequilibrium (LD) in the human genome has a fundamentally "blocklike" structure. However, thus far there has been little formal assessment of how well the haplotype block model captures the underlying structure of LD. Here we propose quantitative criteria for assessing how blocklike LD is and apply these criteria to both real and simulated data. Analyses of several large data sets indicate that real data show a partial fit to the haplotype block model; some regions conform quite well, whereas others do not. Some improvement could be obtained by genotyping higher marker densities but not by increasing the number of samples. Nonetheless, although the real data are only moderately blocklike, our simulations indicate that, under a model of uniform recombination, the structure of LD would actually fit the block model much less well. Simulations of a model in which much of the recombination occurs in narrow hotspots provide a much better fit to the observed patterns of LD, suggesting that there is extensive fine-scale variation in recombination rates across the human genome.

Haplotype blocks and linkage disequilibrium in the human genome

There is great interest in the patterns and extent of linkage disequilibrium (LD) in humans and other species. Characterizing LD is of central importance for gene-mapping studies and can provide insights into the biology of recombination and human demographic history. Here, we review recent developments in this field, including the recently proposed 'haplotype-block' model of LD. We describe some of the recent data in detail and compare the observed patterns to those seen in simulations.

Bayesian analysis of genetic differentiation between populations

We introduce a Bayesian method for estimating hidden population substructure using multilocus molecular markers and geographical information provided by the sampling design. The joint posterior distribution of the substructure and allele frequencies of the respective populations is available in an analytical form when the number of populations is small, whereas an approximation based on a Markov chain Monte Carlo simulation approach can be obtained for a moderate or large number of populations. Using the joint posterior distribution, posteriors can also be derived for any evolutionary population parameters, such as the traditional fixation indices. A major advantage compared to most earlier methods is that the number of populations is treated here as an unknown parameter. What is traditionally considered as two genetically distinct populations, either recently founded or connected by considerable gene flow, is here considered as one panmictic population with a certain probability based on marker data and prior information. Analyses of previously published data on the Moroccan argan tree (Argania spinosa) and of simulated data sets suggest that our method is capable of estimating a population substructure, while not artificially enforcing a substructure when it does not exist. The software (BAPS) used for the computations is freely available from http://www.rni.helsinki.fi/~mjs.

Estimation of linkage disequilibrium for loci with multiple alleles: basic approach and an application using data from bighorn sheep

The great expansion of population genetic data using molecular techniques now allows examination of the extent of linkage disequilibrium for many pairs of loci, each locus often with multiple alleles. The expectation-maximization (EM) algorithm for generating maximum likelihood estimates of gametic frequencies from multiallelic genotypic data is described and applied. The EM algorithm is used in desert bighorn sheep where the population size, and consequently the sample size, is often small. We calculated haplotype frequencies for all pairwise combinations of five major histocompatibility loci and three microsatellite loci in 14 populations; the performance of the algorithm is discussed. Disequilibrium values are calculated and tested for statistical significance. High levels of disequilibrium are found between all pairs of major histocompatibility complex (MHC) loci and between MHC and a linked microsatellite locus.