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1.  A comparison of two collapsing methods in different approaches 
BMC Proceedings  2014;8(Suppl 1):S8.
Sequencing technologies have enabled the investigation of whole genomes of many individuals in parallel. Studies have shown that the joint consideration of multiple rare variants may explain a relevant proportion of the genetic basis for disease so that grouping of rare variants, termed collapsing, can enrich the association signal.
Following this assumption, we investigate the type I error and the power of two proposed collapsing methods (combined multivariate and collapsing method and the functional principal component analysis [FPCA]-based statistic) using the case-control data provided for the Genetic Analysis Workshop 18 with knowledge of the true model. Variants with a minor allele frequency (MAF) of 0.05 or less were collapsed per gene for combined multivariate and collapsing. Neither of the methods detected any of the truly associated genes reliably. Although combined multivariate and collapsing identified one gene with a power of 0.66, it had an unacceptably high false-positive rate of 75%. In contrast, FPCA covered the type I error level well but at the cost of low power. A strict filtering of variants by small MAF might lead to a better performance of the collapsing methods. Furthermore, the inclusion of information on functionality of the variants could be helpful.
PMCID: PMC4143760  PMID: 25519408
2.  Genetic Analysis Workshop 18: Methods and strategies for analyzing human sequence and phenotype data in members of extended pedigrees 
BMC Proceedings  2014;8(Suppl 1):S1.
Genetic Analysis Workshop 18 provided a platform for developing and evaluating statistical methods to analyze whole-genome sequence data from a pedigree-based sample. In this article we present an overview of the data sets and the contributions that analyzed these data. The family data, donated by the Type 2 Diabetes Genetic Exploration by Next-Generation Sequencing in Ethnic Samples Consortium, included sequence-level genotypes based on sequencing and imputation, genome-wide association genotypes from prior genotyping arrays, and phenotypes from longitudinal assessments. The contributions from individual research groups were extensively discussed before, during, and after the workshop in theme-based discussion groups before being submitted for publication.
PMCID: PMC4143625  PMID: 25519310
3.  Identifying rare variants from exome scans: the GAW17 experience 
BMC Proceedings  2011;5(Suppl 9):S1.
Genetic Analysis Workshop 17 (GAW17) provided a platform for evaluating existing statistical genetic methods and for developing novel methods to analyze rare variants that modulate complex traits. In this article, we present an overview of the 1000 Genomes Project exome data and simulated phenotype data that were distributed to GAW17 participants for analyses, the different issues addressed by the participants, and the process of preparation of manuscripts resulting from the discussions during the workshop.
PMCID: PMC3287821  PMID: 22373325
4.  Comparison of collapsing methods for the statistical analysis of rare variants 
BMC Proceedings  2011;5(Suppl 9):S115.
Novel technologies allow sequencing of whole genomes and are considered as an emerging approach for the identification of rare disease-associated variants. Recent studies have shown that multiple rare variants can explain a particular proportion of the genetic basis for disease. Following this assumption, we compare five collapsing approaches to test for groupwise association with disease status, using simulated data provided by Genetic Analysis Workshop 17 (GAW17). Variants are collapsed in different scenarios per gene according to different minor allele frequency (MAF) thresholds and their functionality. For comparing the different approaches, we consider the family-wise error rate and the power. Most of the methods could maintain the nominal type I error levels well for small MAF thresholds, but the power was generally low. Although the methods considered in this report are common approaches for analyzing rare variants, they performed poorly with respect to the simulated disease phenotype in the GAW17 data set.
PMCID: PMC3287839  PMID: 22373249
5.  Evaluation of single-nucleotide polymorphism imputation using random forests 
BMC Proceedings  2009;3(Suppl 7):S65.
Genome-wide association studies (GWAS) have helped to reveal genetic mechanisms of complex diseases. Although commonly used genotyping technology enables us to determine up to a million single-nucleotide polymorphisms (SNPs), causative variants are typically not genotyped directly. A favored approach to increase the power of genome-wide association studies is to impute the untyped SNPs using more complete genotype data of a reference population.
Random forests (RF) provides an internal method for replacing missing genotypes. A forest of classification trees is used to determine similarities of probands regarding their genotypes. These proximities are then used to impute genotypes of untyped SNPs.
We evaluated this approach using genotype data of the Framingham Heart Study provided as Problem 2 for Genetic Analysis Workshop 16 and the Caucasian HapMap samples as reference population. Our results indicate that RFs are faster but less accurate than alternative approaches for imputing untyped SNPs.
PMCID: PMC2795966  PMID: 20018059
6.  Look who is calling: a comparison of genotype calling algorithms 
BMC Proceedings  2009;3(Suppl 7):S59.
In genome-wide association studies, high-level statistical analyses rely on the validity of the called genotypes, and different genotype calling algorithms (GCAs) have been proposed. We compared the GCAs Bayesian robust linear modeling using Mahalanobis distance (BRLMM), Chiamo++, and JAPL using the autosomal single-nucleotide polymorphisms (SNPs) from the 500 k Affymetrix Array Set data of the Framingham Heart Study as provided for the Genetic Analysis Workshop 16, Problem 2, and prepared standard quality control (sQC) for each algorithm. Using JAPL, most individuals were retained for the analysis. The lowest number of SNPs that successfully passed sQC was observed for BRLMM and the highest for Chiamo++. All three GCAs fulfilled all sQC criteria for 79% of the SNPs but at least one GCA failed for 18% of the SNPs. Previously undetected errors in strand coding were identified by comparing genotype concordances between GCAs. Concordance dropped with the number of GCAs failing sQC. We conclude that JAPL and Chiamo++ are the GCAs of choice if the aim is to keep as many subjects and SNPs as possible, respectively.
PMCID: PMC2795959  PMID: 20018052
7.  ACPA: automated cluster plot analysis of genotype data 
BMC Proceedings  2009;3(Suppl 7):S58.
Genome-wide association studies have become standard in genetic epidemiology. Analyzing hundreds of thousands of markers simultaneously imposes some challenges for statisticians. One issue is the problem of multiplicity, which has been compared with the search for the needle in a haystack. To reduce the number of false-positive findings, a number of quality filters such as exclusion of single-nucleotide polymorphisms (SNPs) with a high missing fraction are employed. Another filter is exclusion of SNPs for which the calling algorithm had difficulties in assigning the genotypes. The only way to do this is the visual inspection of the cluster plots, also termed signal intensity plots, but this approach is often neglected. We developed an algorithm ACPA (automated cluster plot analysis), which performs this task automatically for autosomal SNPs. It is based on counting samples that lie too close to the cluster of a different genotype; SNPs are excluded when a certain threshold is exceeded. We evaluated ACPA using 1,000 randomly selected quality controlled SNPs from the Framingham Heart Study data that were provided for the Genetic Analysis Workshop 16. We compared the decision of ACPA with the decision made by two independent readers. We achieved a sensitivity of 88% (95% CI: 81%-93%) and a specificity of 86% (95% CI: 83%-89%). In a screening setting in which one aims at not losing any good SNP, we achieved 99% (95% CI: 98%-100%) specificity and still detected every second low-quality SNP.
PMCID: PMC2795958  PMID: 20018051
10.  Picking single-nucleotide polymorphisms in forests 
BMC Proceedings  2007;1(Suppl 1):S59.
With the development of high-throughput single-nucleotide polymorphism (SNP) technologies, the vast number of SNPs in smaller samples poses a challenge to the application of classical statistical procedures. A possible solution is to use a two-stage approach for case-control data in which, in the first stage, a screening test selects a small number of SNPs for further analysis. The second stage then estimates the effects of the selected variables using logistic regression (logReg). Here, we introduce a novel approach in which the selection of SNPs is based on the permutation importance estimated by random forests (RFs). For this, we used the simulated data provided for the Genetic Analysis Workshop 15 without knowledge of the true model.
The data set was randomly split into a first and a second data set. In the first stage, RFs were grown to pre-select the 37 most important variables, and these were reduced to 32 variables by haplotype tagging. In the second stage, we estimated parameters using logReg.
The highest effect estimates were obtained for five simulated loci. We detected smoking, gender, and the parental DR alleles as covariates. After correction for multiple testing, we identified two out of four genes simulated with a direct effect on rheumatoid arthritis risk and all covariates without any false positive.
We showed that a two-staged approach with a screening of SNPs by RFs is suitable to detect candidate SNPs in genome-wide association studies for complex diseases.
PMCID: PMC2367487  PMID: 18466559
11.  Genetic association studies for gene expressions: permutation-based mutual information in a comparison with standard ANOVA and as a novel approach for feature selection 
BMC Proceedings  2007;1(Suppl 1):S9.
Mutual information (MI) is a robust nonparametric statistical approach for identifying associations between genotypes and gene expression levels. Using the data of Problem 1 provided for the Genetic Analysis Workshop 15, we first compared a quantitative MI (Tsalenko et al. 2006 J Bioinform Comput Biol 4:259–4) with the standard analysis of variance (ANOVA) and the nonparametric Kruskal-Wallis (KW) test. We then proposed a novel feature selection approach using MI in a classification scenario to address the small n - large p problem and compared it with a feature selection that relies on an asymptotic χ2 distribution. In both applications, we used a permutation-based approach for evaluating the significance of MI. Substantial discrepancies in significance were observed between MI, ANOVA, and KW that can be explained by different empirical distributions of the data. In contrast to ANOVA and KW, MI detects shifts in location when the data are non-normally distributed, skewed, or contaminated with outliers. ANOVA but not MI is often significant if one genotype with a small frequency had a remarkable difference in the average gene expression level relative to the other two genotypes. MI depends on genotype frequencies and cannot detect these differences. In the classification scenario, we show that our novel approach for feature selection identifies a smaller list of markers with higher accuracy compared to the standard method. In conclusion, permutation-based MI approaches provide reliable and flexible statistical frameworks which seem to be well suited for data that are non-normal, skewed, or have an otherwise peculiar distribution. They merit further methodological investigation.
PMCID: PMC2359872  PMID: 18466593

Results 1-11 (11)