Data for Genetic Analysis Workshop (GAW) 15 Problem 2, genetic causes of rheumatoid arthritis and associated traits

Circulating IFN-γ producing CD4+ T cells and IL-17A producing CD4+ T cells, HLA-shared epitope and ACPA may characterize the clinical response to therapy in rheumatoid arthritis patients

This study analyzed the association between peripheral distributions of helper T cell subsets, HLA shared-epitope (SE), anti-cyclic citrullinated peptide antibody (ACPA) and clinical response to therapy in rheumatoid arthritis (RA) patients. Frequencies of IFN-γ-producing CD4+T (Th1) and IL-17A-producing CD4+T (Th17) cells were determined by flow cytometry in 167 patients (114 cases with good-response (GR) and 53 poor-response (PR) based on DAS28). HLA-DRB1 alleles for patients and 150 healthy controls were determined by PCR-SSP. We observed that 65.2% of RA patients were SE⁺, 63.4%ACPA⁺, 43.7%SE⁺ACPA⁺ and 14.9% were SE^-ACPA^-. Higher significantly proportions of Th1 and Th17 cells were found in RA patients than controls (P < 0.05) as well as in the SE⁺ or ACPA⁺RA patients compared to SE^- and ACPA^- patients. Increased frequencies of both Th subsets were found in SE⁺ACPA⁺ versus SE^-ACPA^- patients (P < 0.001) and in the PR versus GR group (P < 0.001). We showed significant differences for Th cells frequencies between SE⁺ and SE^- patients in both groups, and between ACPA⁺ and ACPA^- cases in the PR group. Our findings suggest a close link between Th1 and Th17 cells proportions and HLA-SE/ACPA in the RA patients and remarkably in the PR group which could be indicative for the importance of immune monitoring for evaluation of response to therapy.

Weighted Transmission Disequilibrium Test for Family Trio Association Design

BACKGROUND

Family-based design is one of the most popular designs in genetic studies. Transmission disequilibrium test (TDT) for family trio design is optimal only under the additive trait model and may lose power under the other trait models. The TDT-type tests are powerful only when the underlying trait model is correctly specified. Usually, the true trait model is unknown, and the selection of the TDT-type test is problematic. Several methods, which are robust against the mis-specification of the trait model, have been proposed. In this paper, we propose a new efficiency robust procedure for family trio design, namely, the weighted TDT (WTDT) test.

METHODS

We combine information of the largest two TDT-type tests by using weights related to the three TDT-type tests and take the weighted sum as the test statistic.

RESULTS

Simulation results demonstrate that WTDT has power close to, but much more robust than, the optimal TDT-type test based on a single trait model. WTDT also outperforms other efficiency robust methods in terms of power. Applications to real and simulated data from Genetic Analysis Workshop (GAW15) illustrate the practical application of the WTDT method.

CONCLUSION

WTDT is not only efficiency robust to model mis-specifications but also efficiency robust against mis-specifications of risk allele.

A statistical measure for the skewness of X chromosome inactivation based on family trios

BACKGROUND

X chromosome inactivation (XCI) is an important gene regulation mechanism in females to equalize the expression levels of X chromosome between two sexes. Generally, one of two X chromosomes in females is randomly chosen to be inactivated. Nonrandom XCI (XCI skewing) is also observed in females, which has been reported to play an important role in many X-linked diseases. However, there is no statistical measure available for the degree of the XCI skewing based on family data in population genetics.

RESULTS

In this article, we propose a statistical approach to measure the degree of the XCI skewing based on family trios, which is represented by a ratio of two genotypic relative risks in females. The point estimate of the ratio is obtained from the maximum likelihood estimates of two genotypic relative risks. When parental genotypes are missing in some family trios, the expectation-conditional-maximization algorithm is adopted to obtain the corresponding maximum likelihood estimates. Further, the confidence interval of the ratio is derived based on the likelihood ratio test. Simulation results show that the likelihood-based confidence interval has an accurate coverage probability under the situations considered. Also, we apply our proposed method to the rheumatoid arthritis data from USA for its practical use, and find out that a locus, rs2238907, may undergo the XCI skewing against the at-risk allele. But this needs to be further confirmed by molecular genetics.

CONCLUSIONS

The proposed statistical measure for the skewness of XCI is applicable to complete family trio data or family trio data with some paternal genotypes missing. The likelihood-based confidence interval has an accurate coverage probability under the situations considered. Therefore, our proposed statistical measure is generally recommended in practice for discovering the potential loci which undergo the XCI skewing.

Generalized disequilibrium test for association in qualitative traits incorporating imprinting effects based on extended pedigrees

BACKGROUND

For dichotomous traits, the generalized disequilibrium test with the moment estimate of the variance (GDT-ME) is a powerful family-based association method. Genomic imprinting is an important epigenetic phenomenon and currently, there has been increasing interest of incorporating imprinting to improve the test power of association analysis. However, GDT-ME does not take imprinting effects into account, and it has not been investigated whether it can be used for association analysis when the effects indeed exist.

RESULTS

In this article, based on a novel decomposition of the genotype score according to the paternal or maternal source of the allele, we propose the generalized disequilibrium test with imprinting (GDTI) for complete pedigrees without any missing genotypes. Then, we extend GDTI and GDT-ME to accommodate incomplete pedigrees with some pedigrees having missing genotypes, by using a Monte Carlo (MC) sampling and estimation scheme to infer missing genotypes given available genotypes in each pedigree, denoted by MCGDTI and MCGDT-ME, respectively. The proposed GDTI and MCGDTI methods evaluate the differences of the paternal as well as maternal allele scores for all discordant relative pairs in a pedigree, including beyond first-degree relative pairs. Advantages of the proposed GDTI and MCGDTI test statistics over existing methods are demonstrated by simulation studies under various simulation settings and by application to the rheumatoid arthritis dataset. Simulation results show that the proposed tests control the size well under the null hypothesis of no association, and outperform the existing methods under various imprinting effect models. The existing GDT-ME and the proposed MCGDT-ME can be used to test for association even when imprinting effects exist. For the application to the rheumatoid arthritis data, compared to the existing methods, MCGDTI identifies more loci statistically significantly associated with the disease.

CONCLUSIONS

Under complete and incomplete imprinting effect models, our proposed GDTI and MCGDTI methods, by considering the information on imprinting effects and all discordant relative pairs within each pedigree, outperform all the existing test statistics and MCGDTI can recapture much of the missing information. Therefore, MCGDTI is recommended in practice.

Detection of imprinting effects for qualitative traits on X chromosome based on nuclear families

Methods for detecting imprinting effects have been developed primarily for autosomal markers. However, no method is available in the literature to test for imprinting effects on X chromosome. Therefore, it is necessary to suggest methods for detecting such imprinting effects. In this article, the parental-asymmetry test on X chromosome (XPAT) is first developed to test for imprinting for qualitative traits in the presence of association, based on family trios each with both parents and their affected daughter. Then, we propose 1-XPAT to deal with parent-daughter pairs, each with one parent and his/her affected daughter. By simultaneously considering family trios and parent-daughter pairs, C-XPAT (the combined test statistic of XPAT and 1-XPAT) is constructed to test for imprinting. Further, we extend the proposed methods to accommodate complete (with both parents) and incomplete (with one parent) nuclear families having multiple daughters of which at least one is affected. Simulation results demonstrate that the proposed methods control the size well, irrespective of the inbreeding coefficient in females being zero or non-zero. By incorporating incomplete nuclear families, C-XPAT is more powerful than XPAT using only complete nuclear families. For practical use, these proposed methods are applied to analyse the rheumatoid arthritis data and Turner's syndrome data.

T Cell Migration in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation in joints, associated with synovial hyperplasia and with bone and cartilage destruction. Although the primacy of T cell-related events early in the disease continues to be debated, there is strong evidence that autoantigen recognition by specific T cells is crucial to the pathophysiology of rheumatoid synovitis. In addition, T cells are key components of the immune cell infiltrate detected in the joints of RA patients. Initial analysis of the cytokines released into the synovial membrane showed an imbalance, with a predominance of proinflammatory mediators, indicating a deleterious effect of Th1 T cells. There is nonetheless evidence that Th17 cells also play an important role in RA. T cells migrate from the bloodstream to the synovial tissue via their interactions with the endothelial cells that line synovial postcapillary venules. At this stage, selectins, integrins, and chemokines have a central role in blood cell invasion of synovial tissue, and therefore in the intensity of the inflammatory response. In this review, we will focus on the mechanisms involved in T cell attraction to the joint, the proteins involved in their extravasation from blood vessels, and the signaling pathways activated. Knowledge of these processes will lead to a better understanding of the mechanism by which the systemic immune response causes local joint disorders and will help to provide a molecular basis for therapeutic strategies.

Powerful haplotype-based Hardy-Weinberg equilibrium tests for tightly linked loci

Recently, there have been many case-control studies proposed to test for association between haplotypes and disease, which require the Hardy-Weinberg equilibrium (HWE) assumption of haplotype frequencies. As such, haplotype inference of unphased genotypes and development of haplotype-based HWE tests are crucial prior to fine mapping. The goodness-of-fit test is a frequently-used method to test for HWE for multiple tightly-linked loci. However, its degrees of freedom dramatically increase with the increase of the number of loci, which may lack the test power. Therefore, in this paper, to improve the test power for haplotype-based HWE, we first write out two likelihood functions of the observed data based on the Niu's model (NM) and inbreeding model (IM), respectively, which can cause the departure from HWE. Then, we use two expectation-maximization algorithms and one expectation-conditional-maximization algorithm to estimate the model parameters under the HWE, IM and NM models, respectively. Finally, we propose the likelihood ratio tests LRT and LRT for haplotype-based HWE under the NM and IM models, respectively. We simulate the HWE, Niu's, inbreeding and population stratification models to assess the validity and compare the performance of these two LRT tests. The simulation results show that both of the tests control the type I error rates well in testing for haplotype-based HWE. If the NM model is true, then LRT is more powerful. While, if the true model is the IM model, then LRT has better performance in power. Under the population stratification model, LRT is still more powerful. To this end, LRT is generally recommended. Application of the proposed methods to a rheumatoid arthritis data set further illustrates their utility for real data analysis.

Genetic variants in the region of the C1q genes are associated with rheumatoid arthritis

Rodent models for arthritis implicate a role for complement in disease development and progression. In humans, complement deposition has been observed in inflamed synovia of rheumatoid arthritis (RA) patients. In this study we analysed whether genetic variants of complement component C1q predispose to RA. We genotyped single nucleotide polymorphisms (SNPs) in and around the C1q genes, C1qA, C1qB and C1qC, in a Dutch set of 845 RA cases and 1046 controls. Replication was sought in a sample set from North America (868 cases/1193 controls), and a meta-analysis was performed in a combined samples set of 8000 cases and 23 262 controls of European descent. We determined C1q serum levels in relation to C1q genotypes. In the discovery phase, five of the 13 SNPs tested in the C1q genes showed a significant association with RA. Additional analysis of the genomic area around the C1q genes revealed that the strongest associating SNPs were confined to the C1q locus. Within the C1q locus we observed no additional signal independent of the strongest associating SNP, rs292001 [odds ratio (OR) = 0·72 (0·58-0·88), P = 0·0006]. The variants of this SNP were associated with different C1q serum levels in healthy controls (P = 0·006). Interestingly, this SNP was also associated significantly in genome-wide association studies (GWAS) from the North American Rheumatoid Arthritis Consortium study, confirming the association with RA [OR = 0·83 (0·69-1·00), P = 0·043]. Combined analysis, including integrated data from six GWAS studies, provides support for the genetic association. Genetic variants in C1q are correlated with C1q levels and may be a risk for the development of RA.

A powerful approach for association analysis incorporating imprinting effects

MOTIVATION

For a diallelic marker locus, the transmission disequilibrium test (TDT) is a simple and powerful design for genetic studies. The TDT was originally proposed for use in families with both parents available (complete nuclear families) and has further been extended to 1-TDT for use in families with only one of the parents available (incomplete nuclear families). Currently, the increasing interest of the influence of parental imprinting on heritability indicates the importance of incorporating imprinting effects into the mapping of association variants.

RESULTS

In this article, we extend the TDT-type statistics to incorporate imprinting effects and develop a series of new test statistics in a general two-stage framework for association studies. Our test statistics enjoy the nature of family-based designs that need no assumption of Hardy-Weinberg equilibrium. Also, the proposed methods accommodate complete and incomplete nuclear families with one or more affected children. In the simulation study, we verify the validity of the proposed test statistics under various scenarios, and compare the powers of the proposed statistics with some existing test statistics. It is shown that our methods greatly improve the power for detecting association in the presence of imprinting effects. We further demonstrate the advantage of our methods by the application of the proposed test statistics to a rheumatoid arthritis dataset.

CONTACT

wingfung@hku.hk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Detection of parent-of-origin effects using general pedigree data

Genomic imprinting is an important epigenetic factor in complex traits study, which has generally been examined by testing for parent-of-origin effects of alleles. For a diallelic marker locus, the parental-asymmetry test (PAT) based on case-parents trios and its extensions to incomplete nuclear families (1-PAT and C-PAT) are simple and powerful for detecting parent-of-origin effects. However, these methods are suitable only for nuclear families and thus are not amenable to general pedigree data. Use of data from extended pedigrees, if available, may lead to more powerful methods than randomly selecting one two-generation nuclear family from each pedigree. In this study, we extend PAT to accommodate general pedigree data by proposing the pedigree PAT (PPAT) statistic, which uses all informative family trios from pedigrees. To fully utilize pedigrees with some missing genotypes, we further develop the Monte Carlo (MC) PPAT (MCPPAT) statistic based on MC sampling and estimation. Extensive simulations were carried out to evaluate the performance of the proposed methods. Under the assumption that the pedigrees and their associated affection patterns are randomly drawn from a population of pedigrees with at least one affected offspring, we demonstrated that MCPPAT is a valid test for parent-of-origin effects in the presence of association. Further, MCPPAT is much more powerful compared to PAT for trios or even PPAT for all informative family trios from the same pedigrees if there is missing data. Application of the proposed methods to a rheumatoid arthritis dataset further demonstrates the advantage of MCPPAT.

ROADTRIPS: case-control association testing with partially or completely unknown population and pedigree structure

Genome-wide association studies are routinely conducted to identify genetic variants that influence complex disorders. It is well known that failure to properly account for population or pedigree structure can lead to spurious association as well as reduced power. We propose a method, ROADTRIPS, for case-control association testing in samples with partially or completely unknown population and pedigree structure. ROADTRIPS uses a covariance matrix estimated from genome-screen data to correct for unknown population and pedigree structure while maintaining high power by taking advantage of known pedigree information when it is available. ROADTRIPS can incorporate data on arbitrary combinations of related and unrelated individuals and is computationally feasible for the analysis of genetic studies with millions of markers. In simulations with related individuals and population structure, including admixture, we demonstrate that ROADTRIPS provides a substantial improvement over existing methods in terms of power and type 1 error. The ROADTRIPS method can be used across a variety of study designs, ranging from studies that have a combination of unrelated individuals and small pedigrees to studies of isolated founder populations with partially known or completely unknown pedigrees. We apply the method to analyze two data sets: a study of rheumatoid arthritis in small UK pedigrees, from Genetic Analysis Workshop 15, and data from the Collaborative Study of the Genetics of Alcoholism on alcohol dependence in a sample of moderate-size pedigrees of European descent, from Genetic Analysis Workshop 14. We detect genome-wide significant association, after Bonferroni correction, in both studies.

Use of supplementary phenotype to identify additional rheumatoid arthritis loci in a linkage analysis of 342 UK affected sibling pair families

Background

Although rheumatoid arthritis has been shown to have moderately strong genetic component, both linked loci identified in linkage analyses and susceptibility variants from association studies are short of adequately accounting for a comprehensive catalogue of the molecular factors underlying this complex disease. The objective of this study was to use supplementary phenotype based on cumulative hazard of rheumatoid arthritis to identify linkage evidence for new and additional rheumatoid arthritis loci in a genome-wide linkage analysis of 342 affected sibling pair families from the United Kingdom.

Methods

Using proportional hazards model, we estimated cumulative hazard of rheumatoid arthritis and then used it as a quantitative trait in a non-parametric multipoint variance component linkage analysis with 353 microsatellite markers distributed across the 22 autosomal chromosomes.

Results

We identified 3 new loci with genome-wide suggestive linkage evidence for rheumatoid arthritis on 9q21.13, 15p11.1 and 20q13.33. Our results also confirmed previously reported linkage evidence in the HLA-DRB1 region on chromosome 6 and on locus 1q32.1.

Conclusion

This study demonstrates the potential for information gain through the use of supplementary phenotypes in genetic study of complex diseases to identify new and additional potential linked loci that are not detected by linkage analysis of traditional phenotypes; and our results provide further evidence of the involvement of multiple loci in the genetic aetiology of rheumatoid arthritis.

Single versus multiple imputation for genotypic data

Due to the growing need to combine data across multiple studies and to impute untyped markers based on a reference sample, several analytical tools for imputation and analysis of missing genotypes have been developed. Current imputation methods rely on single imputation, which ignores the variation in estimation due to imputation. An alternative to single imputation is multiple imputation. In this paper, we assess the variation in imputation by completing both single and multiple imputations of genotypic data using MACH, a commonly used hidden Markov model imputation method. Using data from the North American Rheumatoid Arthritis Consortium genome-wide study, the use of single and multiple imputation was assessed in four regions of chromosome 1 with varying levels of linkage disequilibrium and association signals. Two scenarios for missing genotypic data were assessed: imputation of untyped markers and combination of genotypic data from two studies. This limited study involving four regions indicates that, contrary to expectations, multiple imputations may not be necessary.

Simultaneous analysis of all single-nucleotide polymorphisms in genome-wide association study of rheumatoid arthritis

The availability of very large number of markers by modern technology makes genome-wide association studies very popular. The usual approach is to test single-nucleotide polymorphisms (SNPs) one at a time for association with disease status. However, it may not be possible to detect marginally significant effects by single-SNP analysis. Simultaneous analysis of SNPs enables detection of even those SNPs with small effect by evaluating the collective impact of several neighboring SNPs. Also, false-positive signals may be weakened by the presence of other neighboring SNPs included in the analysis. We analyzed the North American Rheumatoid Arthritis Consortium data of Genetic Analysis Workshop 16 using HLasso, a new method for simultaneous analysis of SNPs. The simultaneous analysis approach has excellent control of type I error, and many of the previously reported results of single-SNP analyses were confirmed by this approach.

More powerful haplotype sharing by accounting for the mode of inheritance

The concept of haplotype sharing (HS) has received considerable attention recently, and several haplotype association methods have been proposed. Here, we extend the work of Beckmann and colleagues [2005 Hum. Hered. 59:67-78] who derived an HS statistic (BHS) as special case of Mantel's space-time clustering approach. The Mantel-type HS statistic correlates genetic similarity with phenotypic similarity across pairs of individuals. While phenotypic similarity is measured as the mean-corrected cross product of phenotypes, we propose to incorporate information of the underlying genetic model in the measurement of the genetic similarity. Specifically, for the recessive and dominant modes of inheritance we suggest the use of the minimum and maximum of shared length of haplotypes around a marker locus for pairs of individuals. If the underlying genetic model is unknown, we propose a model-free HS Mantel statistic using the max-test approach. We compare our novel HS statistics to BHS using simulated case-control data and illustrate its use by re-analyzing data from a candidate region of chromosome 18q from the Rheumatoid Arthritis (RA) Consortium. We demonstrate that our approach is point-wise valid and superior to BHS. In the re-analysis of the RA data, we identified three regions with point-wise P-values<0.005 containing six known genes (PMIP1, MC4R, PIGN, KIAA1468, TNFRSF11A and ZCCHC2) which might be worth follow-up.

A polygenic model for integration of linkage and pathway information

We introduce an approximate model for linkage curves which accommodates the polygenic structure of complex diseases and accounts for the simultaneous action of closely located genes. The model is extended so that information on biological pathways can be integrated. Using data on rheumatoid arthritis, we describe some of the many applications which the model allows: it can be used to test for residual linkage in the presence of already established loci, to derive a global test for linkage, to test for the relevance of a gene list in terms of linkage and to help in candidate gene prioritization by integration of gene-pathway annotation data.

New take on comparative immunology: relevance to immunotherapy

It is becoming increasingly recognized that experimental animal models, while useful to address monothematic biological questions, bear unpredictable relevance to human disease. Several reasons have been proposed. However, the uncontrollable nature of human genetics and the heterogeneity of disease that can only be replicated with difficulty experimentally play a leading role. Comparative immunology is a term that generally refers to the analysis of shared or diverging facets of immunology among species; these comparisons are carried out according to the principle that evolutionarily conserved themes outline biologic functions universally relevant for survival. We propose that a similar strategy could be applied to searching for themes shared by distinct immune pathologies within our own species. Identification of common patterns may outline pathways necessary for a particular determinism to occur, such as tissue-specific rejection or tolerance. This approach is founded on the unproven but sensible presumption that nature does not require an infinite plethora of redundant mechanisms to reach its purposes. Thus, immune pathologies must follow, at least in part, common means that determine their onset and maintenance. Commonalities among diseases can, in turn, be segregated from disease-specific patterns uncovering essential mechanisms that may represent universal targets for immunotherapy.

Bayesian intervals for linkage locations

Intermediate fine mapping has received considerable attention recently, with the goal of providing statistically precise and valid chromosomal regions for fine mapping following initial identification of broad regions that are linked to a disease. The following classes of methods have been proposed and compared in the literature: (1) LOD-support intervals, (2) generalized estimating equations, (3) bootstrap, and (4) confidence set inference framework. These methods provide confidence intervals either with coverage levels deviating from the nominal confidence levels or that are not fully efficient. Here, we propose a novel Bayesian method for constructing such intervals using affected sibling pair data. The susceptibility gene location is treated as a parameter in this method, with a uniform prior. A Metropolis-Hastings algorithm is implemented to sample from the posterior distribution and highest posterior density intervals of the disease gene locations are constructed. Correct coverage levels are maintained by our method. Both simulation studies and an application to a rheumatoid arthritis dataset demonstrate the improved efficiency of the Bayesian intervals compared with existing methods.

Introduction to Genetic Analysis Workshop 15 summaries

The 15th biennial Genetic Analysis Workshop (GAW15) took place November 11-15, 2006 in St. Pete Beach, Florida. The workshop's primary focus was on the appropriate linkage, association, and other analyses of the increasingly large datasets generated by genetics research. A record number of participants (N=350) contributed 252 papers to GAW15. These contributions were organized into 17 presentation groups, with a range of 11 to 18 papers in each group (median of 15 papers per group). The data sets--or "problems"--for GAW15 included information from two real data sets and a simulated data set. The first problem utilizing real data included gene expression as the phenotype and genome-wide markers for linkage and association studies. The second problem allowed for detecting and characterizing genetic effects for rheumatoid arthritis. And the simulated problem was generated to reflect the data structure underlying the rheumatoid arthritis study. Further details on GAW15 are provided here, and the primary findings from the workshop are highlighted in the following group summary papers.

Using linkage and association to identify and model genetic effects: summary of GAW15 Group 4

Group 4 at Genetic Analysis Workshop 15 focused on methods that exploited both linkage and association information to map disease loci. All contributions considered the dichotomous trait of rheumatoid arthritis, using either affected sibpairs and/or unrelated controls. While one contribution investigated linkage and association approaches separately in genome-wide analyses, the remaining others focused on joint linkage and association methods in specific genomic regions. The latter contributions proposed new methods and/or examined existing methods that addressed whether one or more polymorphisms partially or fully explained a linkage signal, particularly the methods proposed by Li et al. that are implemented in the computer program Linkage and Association Modeling in Pedigrees (LAMP). Using simulated SNP data under linkage peaks, several contributions found that existing family-based association approaches such as those of Martin et al. and Lake et al. had power similar to LAMP and to several methods proposed by the contributors for testing that a single nucleotide polymorphism partially explains a linkage peak. In evaluating methods for identifying if a polymorphism or a set of polymorphisms fully accounted for a linkage signal, several contributions found that it was important to understand that these methods may be subject to low power in some situations and thus, a non-significant result was not necessarily indicative of the polymorphism(s) being fully responsible for the linkage signal. Finally, modeling the disease using association evidence conditional on linkage may improve understanding of the etiology of disease.

Multiple testing in the genomics era: findings from Genetic Analysis Workshop 15, Group 15

Recent advances in molecular technologies have resulted in the ability to screen hundreds of thousands of single nucleotide polymorphisms and tens of thousands of gene expression profiles. While these data have the potential to inform investigations into disease etiologies and advance medicine, the question of how to adequately control both type I and type II error rates remains. Genetic Analysis Workshop 15 datasets provided a unique opportunity for participants to evaluate multiple testing strategies applicable to microarray and single nucleotide polymorphism data. The Genetic Analysis Workshop 15 multiple testing and false discovery rate group (Group 15) investigated three general categories for multiple testing corrections, which are summarized in this review: statistical independence, error rate adjustment, and data reduction. We show that while each approach may have certain advantages, adequate error control is largely dependent upon the question under consideration and often requires the use of multiple analytic strategies.

Model selection and Bayesian methods in statistical genetics: summary of group 11 contributions to Genetic Analysis Workshop 15

The research presented in group 11 of the Genetic Analysis Workshop 15 (GAW15) falls into two major themes: Model selection approaches for gene mapping (both Bayesian and Frequentist); and other Bayesian methods. These methods either allow relaxation of some of the common assumptions, such as mode of inheritance, for studying complicated genetic systems, or allow incorporation of additional information into the model. Over half of the groups applied model selection methods on all three data sets, using models in which genetic markers were used as predictors for linkage, phenotype expression, or transmission to an affected offspring. Most groups employed variations of Stochastic Search Variable Selection as the model selection method of choice. A brief review of this class of methods is given in this summary paper, followed by highlights of other methods and overall summaries of each contribution to the GAW15 presentation group 11. These group contributions exhibit the value of framing genetic problems in terms of model selection, and highlight the impact of variable selection for gene mapping.

Data mining, neural nets, trees--problems 2 and 3 of Genetic Analysis Workshop 15

Genome-wide association studies using thousands to hundreds of thousands of single nucleotide polymorphism (SNP) markers and region-wide association studies using a dense panel of SNPs are already in use to identify disease susceptibility genes and to predict disease risk in individuals. Because these tasks become increasingly important, three different data sets were provided for the Genetic Analysis Workshop 15, thus allowing examination of various novel and existing data mining methods for both classification and identification of disease susceptibility genes, gene by gene or gene by environment interaction. The approach most often applied in this presentation group was random forests because of its simplicity, elegance, and robustness. It was used for prediction and for screening for interesting SNPs in a first step. The logistic tree with unbiased selection approach appeared to be an interesting alternative to efficiently select interesting SNPs. Machine learning, specifically ensemble methods, might be useful as pre-screening tools for large-scale association studies because they can be less prone to overfitting, can be less computer processor time intensive, can easily include pair-wise and higher-order interactions compared with standard statistical approaches and can also have a high capability for classification. However, improved implementations that are able to deal with hundreds of thousands of SNPs at a time are required.

Large-scale linkage analysis of 1302 affected relative pairs with rheumatoid arthritis

Rheumatoid arthritis is the most common systematic autoimmune disease and its etiology is believed to have both strong genetic and environmental components. We demonstrate the utility of including genetic and clinical phenotypes as covariates within a linkage analysis framework to search for rheumatoid arthritis susceptibility loci. The raw genotypes of 1302 affected relative pairs were combined from four large family-based samples (North American Rheumatoid Arthritis Consortium, United Kingdom, European Consortium on Rheumatoid Arthritis Families, and Canada). The familiality of the clinical phenotypes was assessed. The affected relative pairs were subjected to autosomal multipoint affected relative-pair linkage analysis. Covariates were included in the linkage analysis to take account of heterogeneity within the sample. Evidence of familiality was observed with age at onset (p << 0.001) and rheumatoid factor (RF) IgM (p << 0.001), but not definite erosions (p = 0.21). Genome-wide significant evidence for linkage was observed on chromosome 6. Genome-wide suggestive evidence for linkage was observed on chromosomes 13 and 20 when conditioning on age at onset, chromosome 15 conditional on gender, and chromosome 19 conditional on RF IgM after allowing for multiple testing of covariates.

Multivariate analysis of complex gene expression and clinical phenotypes with genetic marker data

This paper summarizes contributions to group 12 of the 15th Genetic Analysis Workshop. The papers in this group focused on multivariate methods and applications for the analysis of molecular data including genotypic data as well as gene expression microarray measurements and clinical phenotypes. A range of multivariate techniques have been employed to extract signals from the multi-feature data sets that were provided by the workshop organizers. The methods included data reduction techniques such as principal component analysis and cluster analysis; latent variable models including structural equations and item response modeling; joint multivariate modeling techniques as well as multivariate visualization tools. This summary paper categorizes and discusses individual contributions with regard to multiple classifications of multivariate methods. Given the wide variety in the data considered, the objectives of the analysis and the methods applied, direct comparison of the results of the various papers is difficult. However, the group was able to make many interesting comparisons and parallels between the various approaches. In summary, there was a consensus among authors in group 12 that the genetic research community should continue to draw experiences from other fields such as statistics, econometrics, chemometrics, computer science and linear systems theory.