Statistical methods for analysing complex genetic traits

Complex traits are caused by multiple genetic and environmental factors, and are therefore difficult to study compared with simple Mendelian diseases. The modes of inheritance of Mendelian diseases are often known. Methods to dissect such diseases are well described in literature. For complex genetic traits, the inheritance pattern is not clear and difficult to understand, and genetic variants contributing to such traits probably have small effect sizes. Hence, searching for genes responsible for complex traits requires different strategies searching for genes responsible for complex traits requires different strategies as well as new methods. A common strategy for mapping complex traits is as follows: (1) perform a genome-wide linkage analysis using dense genetic markers, and identify regions showing evidence of linkage, then (2) perform association analysis to refine these regions. Along these lines we propose several methods for detecting disease genes.The Netherlands Organization for Scientific ResearchUBL - phd migration 201

Methods to test for association between a disease and a multi-allelic marker applied to a candidate region

We report the analysis results of the Genetic Analysis Workshop 14 simulated microsatellite marker dataset, using replicate 50 from the Danacaa population. We applied several methods for association analysis of multi-allelic markers to case-control data to study the association between Kofendrerd Personality Disorder and multi-allelic markers in a candidate region previously identified by the linkage analysis. Evidence for association was found for marker D03S0127 (p < 0.01). The analyses were done without any prior knowledge of the answers

Multi-part strategy for testing differential taxa abundance in sequencing data: A simulation study with an application to a microbiome study

Comparing the microbiome across study arms is a recurrent goal in many studies. Standard statistical methods are often used for this purpose, however, they do not always represent the best choice in this context given the characteristics of microbiota sequencing data, e.g., non-negative, highly skewed counts with a large number of zeros. A multi-part strategy, that combines a two-part test (as described by Wagner et al., 2011), a Wilcoxon sum-rank test, a Chi-square and a Barnard's test was explored to compare the taxa abundance between study arms. The choice of the test is based on the data structure. The type I error of the multi-part strategy was evaluated by using a simulation study and the method was applied to real data. The script to perform the analysis with the multi-part approach is provided in the statistical software SAS. Several scenarios were simulated and in all of them the type I error was not inflated. Based on the statistical differences resulting from the two-part test (as described by Wagner et al., 2011) and the multi-part strategy (as proposed in this article), different biological implications can be extracted from the same comparison in the same data set. In the comparison of taxa abundance between study arms, we showed that careful attention needs to be paid on the data structure, in order to be able to choose an appropriate analysis method. Our approach selects the most suitable test according to the type of data observed, maintains a good type I error and is easily applicable by using the SAS macro provided

Generalizing Terwilliger's likelihood approach: a new score statistic to test for genetic association

<p>Abstract</p> <p>Background:</p> <p>In this paper, we propose a one degree of freedom test for association between a candidate gene and a binary trait. This method is a generalization of Terwilliger's likelihood ratio statistic and is especially powerful for the situation of one associated haplotype. As an alternative to the likelihood ratio statistic, we derive a score statistic, which has a tractable expression. For haplotype analysis, we assume that phase is known.</p> <p>Results:</p> <p>By means of a simulation study, we compare the performance of the score statistic to Pearson's chi-square statistic and the likelihood ratio statistic proposed by Terwilliger. We illustrate the method on three candidate genes studied in the Leiden Thrombophilia Study.</p> <p>Conclusion:</p> <p>We conclude that the statistic follows a chi square distribution under the null hypothesis and that the score statistic is more powerful than Terwilliger's likelihood ratio statistic when the associated haplotype has frequency between 0.1 and 0.4 and has a small impact on the studied disorder. With regard to Pearson's chi-square statistic, the score statistic has more power when the associated haplotype has frequency above 0.2 and the number of variants is above five.</p

Locally weighted transmission/disequilibrium test for genetic association analysis

The transmission/disequilibrium test statistic has been used for assessing genetic association in affected-parent trios. In the presence of multiple tightly linked marker loci where local dependency may exist, haplotypes are reconstructed statistically to estimate the joint effects of these markers. In this manuscript, we propose an alternative to the haplotype approach by taking a weighted average of multiple loci, where the weight is proportional to the product of (1-2X recombination fraction) and the linkage disequilibrium between markers. As an illustration, we applied the method to the simulated Aipotu data

Lack of evidence that p53 Arg72Pro influences lung cancer prognosis: an analysis of survival in 619 female patients.

The prognostic significance of the Arg72Pro polymorphism of the p53 tumour suppressor gene in cancer is controversial. To determine whether Arg72Pro is a marker for lung cancer prognosis we genotyped 619 female lung cancer patients with incident disease and examined the relationship between genotype and overall survival (OS). Nonparametric tests provided no evidence for a relationship between SNP genotype and OS (P-values 0.131, 0.161, and 0.156 for log rank, Wilcoxon and Fleming-Harrington test statistics, respectively). Under the Cox proportional hazards model the HRs associated with Arg/Pro, Pro/Pro and Pro-carrier status were: 0.98 (95%CI: 0.79-1.22), 0.76 (95%CI: 0.51-1.15) and 0.93 (95%CI: 0.76-1.15), respectively. Despite employing a comprehensive set of statistical tests including those sensitive to the detection of differences in early survival our data provide little evidence to support the tenet that the p53 Arg72Pro polymorphism is a clinically useful prognostic marker for lung cancer