Charalampos Papachristou

Linkage analysis of the simulated data – evaluations and comparisons of methods

The goal of this study is to evaluate, compare, and contrast several standard and new linkage analysis methods. First, we compare a recently proposed confidence set approach with MAPMAKER/SIBS. Then, we evaluate a new Bayesian approach that accounts for heterogeneity. Finally, the newly developed software SIMPLE is compared with GENEHUNTER. We apply these methods to several replicates of the Genetic Analysis Workshop 13 simulated data to assess their ability to detect the high blood pressure genes on chromosome 21, whose positions were known to us prior to the analyses. In contrast to the standard methods, most of the new approaches are able to identify at least one of the disease genes in all the replicates considered.

Evaluation of logistic Bayesian LASSO for identifying association with rare haplotypes

It has been hypothesized that rare variants may hold the key to unraveling the genetic transmission mechanism of many common complex traits. Currently, there is a dearth of statistical methods that are powerful enough to detect association with rare haplotypes. One of the recently proposed methods is logistic Bayesian LASSO for case-control data. By penalizing the regression coefficients through appropriate priors, logistic Bayesian LASSO weeds out the unassociated haplotypes, making it possible for the associated rare haplotypes to be detected with higher powers. We used the Genetic Analysis Workshop 18 simulated data to evaluate the behavior of logistic Bayesian LASSO in terms of its power and type I error under a complex disease model. We obtained knowledge of the simulation model, including the locations of the functional variants, and we chose to focus on two genomic regions in the MAP4 gene on chromosome 3. The sample size was 142 individuals and there were 200 replicates.Despite the small sample size, logistic Bayesian LASSO showed high power to detect two haplotypes containing functional variants in these regions while maintaining low type I errors. At the same time, a commonly used approach for haplotype association implemented in the software hapassoc failed to converge because of the presence of rare haplotypes. Thus, we conclude that logistic Bayesian LASSO can play an important role in the search for rare haplotypes.

A LASSO penalized regression approach for genome-wide association analyses using related individuals: application to the Genetic Analysis Workshop 19 simulated data

We propose a novel LASSO (least absolute shrinkage and selection operator) penalized regression method used to analyze samples consisting of (potentially) related individuals. Developed in the context of linear mixed models, our method models the relatedness of individuals in the sample through a random effect whose covariance structure is a linear function of known matrices with elements combinations of the condensed coefficients of identity between the individuals in the sample. We implement our method to analyze the simulated family data provided by the 19th Genetic Analysis Workshop in an effort to identify loci regulating the simulated trait of systolic blood pressure. The analyses were performed with full knowledge of the simulation model. Our findings demonstrate that we can significantly reduce the rate of false positive signals by incorporating the relatedness of the study participants.

Population-Based Association and Gene by Environment Interactions in Genetic Analysis Workshop 18

In the past decade, genome‐wide association studies have been successful in identifying genetic loci that play a role in many complex diseases. Despite this, it has become clear that for many traits, investigation of single common variants does not give a complete picture of the genetic contribution to the phenotype. Therefore a number of new approaches are currently being investigated to further the search for susceptibility loci or regions. We summarize the contributions to Genetic Analysis Workshop 18 (GAW18) that concern this search using methods for population‐based association analysis. Many of the members of our GAW18 working group made use of data types that have only recently become available through the use of next‐generation sequencing technologies, with many focusing on the investigation of rare variants instead of or in combination with common variants. Some contributors used a haplotype‐based approach, which to date has been used relatively infrequently but may become more important for analyzing rare variant association data. Others analyzed gene‐gene or gene‐environment interactions, where novel statistical approaches were needed to make the best use of the available information without requiring an excessive computational burden. GAW18 provided participants with the chance to make use of state‐of‐the‐art data, statistical techniques, and technology. We report here some of the experiences and conclusions that were reached by workshop participants who analyzed the GAW18 data as a population‐based association study.

A Confidence Set Inference Method for Identifying SNPs That Regulate Quantitative Phenotypes

Aims: We introduce a family-based confidence set inference (CSI) method that can be used in preliminary genome-wide association studies to obtain confidence sets of SNPs that contribute a specific percentage to the additive genetic variance of quantitative traits. Methods: Developed in the framework of generalized linear mixed models, the method utilizes data from outbred families of arbitrary size and structure. Through our own simulation study and analysis of the Genetics Analysis Workshop 16 simulated data, we study the properties of our method and compare its performance to that of the family association method described by Chen and Abecasis [Am J Hum Genet 2007;81:913–926]. We also analyze the Framingham Heart Study data to identify SNPs regulating high-density lipoprotein levels. Results: The simulation studies demonstrated that CSI yields confidence sets with correct coverage and that it can outperform the method introduced by Chen and Abecasis [Am J Hum Genet 2007;81:913–926]. Furthermore, we identified five SNPs that potentially regulate high-density lipoprotein levels: rs9989419, rs11586238, rs1754415, rs9355648, and rs9356560. Conclusion: The CSI method provides confidence sets of SNPs that contribute to the genetic variance of quantitative traits and is a competitive alternative to currently used family association methods. The approach is particularly useful in genome-wide association studies as it significantly reduces the number of SNPs investigated in follow-up studies.

Genetic Variance Components Estimation for Binary Traits Using Multiple Related Individuals

Understanding and modeling genetic or nongenetic factors that influence susceptibility to complex traits has been the focus of many genetic studies. Large pedigrees with known complex structure may be advantageous in epidemiological studies since they can significantly increase the number of factors whose influence on the trait can be estimated. We propose a likelihood approach, developed in the context of generalized linear mixed models, for modeling dichotomous traits based on data from hundreds of individuals all of whom are potentially correlated through either a known pedigree or an estimated covariance matrix. Our approach is based on a hierarchical model where we first assess the probability of each individual having the trait and then formulate a likelihood assuming conditional independence of individuals. The advantage of our formulation is that it easily incorporates information from pertinent covariates as fixed effects and at the same time takes into account the correlation between individuals that share genetic background or other random effects. The high dimensionality of the integration involved in the likelihood prohibits exact computations. Instead, an automated Monte Carlo expectation maximization algorithm is employed for obtaining the maximum likelihood estimates of the model parameters. Through a simulation study we demonstrate that our method can provide reliable estimates of the model parameters when the sample size is close to 500. Implementation of our method to data from a pedigree of 491 Hutterites evaluated for Type 2 diabetes (T2D) reveal evidence of a strong genetic component to T2D risk, particularly for younger and leaner cases. Genet. Epidemiol. 2011.

A Confidence Set Inference Procedure for Gene Mapping Using Markers with Incomplete Polymorphism

A recent approach for gene mapping based on confidence set inference (CSI) promises several advantages, including avoidance of corrections for multiple tests, availability of confidence intervals with known statistical properties, and sufficient localizations of disease genes. This paper proposes an extended CSI procedure that can handle markers with incomplete polymorphism, thereby increasing the applicability of the set of CSI methods in practical situations. Simulation studies show that the new procedure retains the main advantages of the original CSI. Although it generally requires more data to achieve a similar power, this increase is moderate for markers with 80% heterozygosity or higher. We also investigate the effects of relative risk estimates and disease models. Our analyses show that perturbation from actual relative risks or multilocus disease models generally leads to reduction in power or inflation in type I error, as expected. Nevertheless, for certain classes of two-locus disease models, CSI can still perform well, with reasonably high actual coverage probabilities for at least one of the disease loci. Application of CSI to the data provided by the Genetic Analysis Workshop 13 yields encouraging results, as they compare favorably to those obtained from GENEHUNTER using its NPL sib-pair method.

Multiple testing in high-throughput sequence data: experiences from Group 8 of Genetic Analysis Workshop 17

The use of high‐throughput sequence data in genetic epidemiology allows the investigation of common and rare variants in the entire genome, thus increasing the amount of information and the potential number of statistical tests performed within one study. As a consequence, the problem of multiple testing may become even more pressing than in previous studies. As an important challenge, the exact number of statistical tests depends on the actual statistical method used. Furthermore, many statistical approaches for the analysis of sequence data require permutation. Thus it may be difficult to also use permutation to estimate correct type I error levels as in genome‐wide association studies. In view of this, a separate group at Genetic Analysis Workshop 17 was formed with a focus on multiple testing. Here, we present the approaches used for the workshop. Apart from tackling the multiple testing problem, the new group focused on different issues. Some contributors developed and investigated modifications of existing collapsing methods. Others aimed at improving the identification of functional variants through a reduction and analysis of the underlying data dimensions. Two research groups investigated the overall accumulation of rare variation across the genome and its value in predicting phenotypes. Finally, other investigators left the path of traditional statistical analyses by reversing null and alternative hypotheses and by proposing a novel resampling method. We describe and discuss all these approaches. Genet. Epidemiol. 35:S61–S66, 2011.

Confidence set of putative quantitative trait loci in whole genome scans with application to the Genetic Analysis Workshop 17 simulated data

As genetic maps become more highly dense, the ability to sufficiently localize putative disease loci becomes an achievable goal. This has prompted an increased interest in methods for constructing confidence intervals for the location of variants that contribute to a trait. Such intervals are important because, by reducing the number of candidate loci, they can help in the design of cost-effective and time-efficient follow-up studies. We introduce a new approach that can be used in whole-genome scans to obtain a confidence set of loci that contribute at least a predetermined percentage h to the overall genetic variation of a quantitative phenotype. The method is developed in the framework of generalized linear mixed models and can accommodate families of arbitrary size and structure. We apply our method to the Genetic Analysis Workshop 17 simulated data where we scan chromosomes 6, 15, 20, 21, and 22 to uncover loci regulating the simulated phenotype Q2. For the analyses we had prior knowledge of the simulation model used to generate the phenotype.

Cytokines increase engraftment of human acute myeloid leukemia cells in immunocompromised mice but not engraftment of human myelodysplastic syndrome cells

Patient-derived xenotransplantation models of human myeloid diseases including acute myeloid leukemia, myelodysplastic syndromes and myeloproliferative neoplasms are essential for studying the biology of the diseases in pre-clinical studies. However, few studies have used these models for comparative purposes. Previous work has shown that acute myeloid leukemia blasts respond to human hematopoietic cytokines whereas myelodysplastic syndrome cells do not. We compared the engraftment of acute myeloid leukemia cells and myelodysplastic syndrome cells in NSG mice to that in NSG-S mice, which have transgene expression of human cytokines. We observed that only 50% of all primary acute myeloid leukemia samples (n=77) transplanted in NSG mice provided useful levels of engraftment (>0.5% human blasts in bone marrow). In contrast, 82% of primary acute myeloid leukemia samples engrafted in NSG-S mice with higher leukemic burden and shortened survival. Additionally, all of 5 injected samples from patients with myelodysplastic syndrome showed persistent engraftment on week 6; however, engraftment was mostly low (<2%), did not increase over time, and was only transiently affected by the use of NSG-S mice. Co-injection of mesenchymal stem cells did not enhance human myelodysplastic syndrome cell engraftment. Overall, we conclude that engraftment of acute myeloid leukemia samples is more robust compared to that of myelodysplastic syndrome samples and unlike those, acute myeloid leukemia cells respond positively to human cytokines, whereas myelodysplastic syndrome cells demonstrate a general unresponsiveness to them.