Search

Abstract Background Altered lipid profile, and in particular low HDL and high triglyceride (TG) plasma levels, are within the major determinants of cardiovascular diseases. The identification of quantitative trait loci (QTL) affecting these lipid levels is a relevant issue for predictive purposes. The WWOX gene has been recently associated with HDL levels. This gene is located at chromosome 16q23, a region previously linked to familial combined hyperlipidemia (FCHL) and HDL. Our objective is to perform a genetic association analysis at the WWOX gene region with HDL, TG and TG/HDL ratio. Methods A quantitative association analysis performed in 801 individuals selected from the Spanish general population. Results For HDL levels, two regions of intron 8 display clustering of positive signals (p < 0.05) but none of them was associated in the haplotypic analysis (0.07 ≤ p ≤ 0.165). For TG levels not only intron 8 but also a 27 kb region spanning from the promoter region to intron 4 are associated in this study. For the TG/HDL genetic association analysis, positive signals are coincident with those of the isolated traits. Interestingly, haplotypic analysis at the 5' region showed that variation in this region modified both HDL and TG levels, especially the latter (p = 0.003). Conclusions Our results suggest that WWOX is a QTL for both TG and HDL.

Abstract A response to Toplak et al: Does replication groups scoring reduce false positive rate in SNP interaction discovery? BMC Genomics 2010, 11:58. Background The genomewide evaluation of genetic epistasis is a computationally demanding task, and a current challenge in Genetics. HFCC (Hypothesis-Free Clinical Cloning) is one of the methods that have been suggested for genomewide epistasis analysis. In order to perform an exhaustive search of epistasis, HFCC has implemented several tools and data filters, such as the use of multiple replication groups, and direction of effect and control filters. A recent article has claimed that the use of multiple replication groups (as implemented in HFCC) does not reduce the false positive rate, and we hereby try to clarify these issues. Results/Discussion HFCC uses, as an analysis strategy, the possibility of replicating findings in multiple replication groups, in order to select a liberal subset of preliminary results that are above a statistical criterion and consistent in direction of effect. We show that the use of replication groups and the direction filter reduces the false positive rate of a study, although at the expense of lowering the overall power of the study. A post-hoc analysis of these selected signals in the combined sample could then be performed to select the most promising results. Conclusion Replication of results in independent samples is generally used in scientific studies to establish credibility in a finding. Nonetheless, the combined analysis of several datasets is known to be a preferable and more powerful strategy for the selection of top signals. HFCC is a flexible and complete analysis tool, and one of its analysis options combines these two strategies: A preliminary multiple replication group analysis to eliminate inconsistent false positive results, and a post-hoc combined-group analysis to select the top signals.

Abstract Background Genetic admixture is a common caveat for genetic association analysis. Therefore, it is important to characterize the genetic structure of the population under study to control for this kind of potential bias. Results In this study we have sampled over 800 unrelated individuals from the population of Spain, and have genotyped them with a genome-wide coverage. We have carried out linkage disequilibrium, haplotype, population structure and copy-number variation (CNV) analyses, and have compared these estimates of the Spanish population with existing data from similar efforts. Conclusions In general, the Spanish population is similar to the Western and Northern Europeans, but has a more diverse haplotypic structure. Moreover, the Spanish population is also largely homogeneous within itself, although patterns of micro-structure may be able to predict locations of origin from distant regions. Finally, we also present the first characterization of a CNV map of the Spanish population. These results and original data are made available to the scientific community.

Abstract Background The etiology of complex diseases is due to the combination of genetic and environmental factors, usually many of them, and each with a small effect. The identification of these small-effect contributing factors is still a demanding task. Clearly, there is a need for more powerful tests of genetic association, and especially for the identification of rare effects Results We introduce a new genetic association test based on symbolic dynamics and symbolic entropy. Using a freely available software, we have applied this entropy test, and a conventional test, to simulated and real datasets, to illustrate the method and estimate type I error and power. We have also compared this new entropy test to the Fisher exact test for assessment of association with low-frequency SNPs. The entropy test is generally more powerful than the conventional test, and can be significantly more powerful when the genotypic test is applied to low allele-frequency markers. We have also shown that both the Fisher and Entropy methods are optimal to test for association with low-frequency SNPs (MAF around 1-5%), and both are conservative for very rare SNPs (MAF Conclusions We have developed a new, simple, consistent and powerful test to detect genetic association of biallelic/SNP markers in case-control data, by using symbolic dynamics and symbolic entropy as a measure of gene dependence. We also provide a standard asymptotic distribution of this test statistic. Given that the test is based on entropy measures, it avoids smoothed nonparametric estimation. The entropy test is generally as good or even more powerful than the conventional and Fisher tests. Furthermore, the entropy test is more computationally efficient than the Fisher's Exact test, especially for large number of markers. Therefore, this entropy-based test has the advantage of being optimal for most SNPs, regardless of their allele frequency (Minor Allele Frequency (MAF) between 1-50%). This property is quite beneficial, since many researchers tend to discard low allele-frequency SNPs from their analysis. Now they can apply the same statistical test of association to all SNPs in a single analysis., which can be especially helpful to detect rare effects.

Abstract Background The difficulty in elucidating the genetic basis of complex diseases roots in the many factors that can affect the development of a disease. Some of these genetic effects may interact in complex ways, proving undetectable by current single-locus methodology. Results We have developed an analysis tool called Hypothesis Free Clinical Cloning (HFCC) to search for genome-wide epistasis in a case-control design. HFCC combines a relatively fast computing algorithm for genome-wide epistasis detection, with the flexibility to test a variety of different epistatic models in multi-locus combinations. HFCC has good power to detect multi-locus interactions simulated under a variety of genetic models and noise conditions. Most importantly, HFCC can accomplish exhaustive genome-wide epistasis search with large datasets as demonstrated with a 400,000 SNP set typed on a cohort of Parkinson's disease patients and controls. Conclusion With the current availability of genetic studies with large numbers of individuals and genetic markers, HFCC can have a great impact in the identification of epistatic effects that escape the standard single-locus association analyses.

Background: In order to identify novel loci associated with Alzheimer's disease (AD), we conducted a genomewide association study (GWAS) in the Spanish population. Methods: We genotyped 1,128 individuals using the Affymetrix Nsp I 250K chip. A sample of 327 sporadic AD patients and 801 controls with unknown cognitive status from the Spanish general population were included in our initial study. To increase the power of the study, we combined our results with those of four other public GWAS datasets by applying identical quality control filters and the same imputation methods, which were then analyzed with a global meta-GWAS. A replication sample with 2,200 sporadic AD patients and 2,301 controls was genotyped to confirm our GWAS findings. Results: Meta-analysis of our data and independent replication datasets allowed us to confirm a novel genomewide significant association of AD with the membrane-spanning 4-domains subfamily A (MS4A) gene cluster (rs1562990, P = 4.40E-11, odds ratio = 0.88, 95% confidence interval 0.85 to 0.91, n = 10,181 cases and 14,341 controls). Conclusions: Our results underscore the importance of international efforts combining GWAS datasets to isolate genetic loci for complex diseases. [ABSTRACT FROM AUTHOR]

A response to Toplak et al: Does replication groups scoring reduce false positive rate in SNP interaction discovery? BMC Genomics 2010, 11:58. Background: The genomewide evaluation of genetic epistasis is a computationally demanding task, and a current challenge in Genetics. HFCC (Hypothesis-Free Clinical Cloning) is one of the methods that have been suggested for genomewide epistasis analysis. In order to perform an exhaustive search of epistasis, HFCC has implemented several tools and data filters, such as the use of multiple replication groups, and direction of effect and control filters. A recent article has claimed that the use of multiple replication groups (as implemented in HFCC) does not reduce the false positive rate, and we hereby try to clarify these issues. Results/Discussion: HFCC uses, as an analysis strategy, the possibility of replicating findings in multiple replication groups, in order to select a liberal subset of preliminary results that are above a statistical criterion and consistent in direction of effect. We show that the use of replication groups and the direction filter reduces the false positive rate of a study, although at the expense of lowering the overall power of the study. A post-hoc analysis of these selected signals in the combined sample could then be performed to select the most promising results. Conclusion: Replication of results in independent samples is generally used in scientific studies to establish credibility in a finding. Nonetheless, the combined analysis of several datasets is known to be a preferable and more powerful strategy for the selection of top signals. HFCC is a flexible and complete analysis tool, and one of its analysis options combines these two strategies: A preliminary multiple replication group analysis to eliminate inconsistent false positive results, and a post-hoc combined-group analysis to select the top signals. [ABSTRACT FROM AUTHOR]

Background: The etiology of complex diseases is due to the combination of genetic and environmental factors, usually many of them, and each with a small effect. The identification of these small-effect contributing factors is still a demanding task. Clearly, there is a need for more powerful tests of genetic association, and especially for the identification of rare effects Results: We introduce a new genetic association test based on symbolic dynamics and symbolic entropy. Using a freely available software, we have applied this entropy test, and a conventional test, to simulated and real datasets, to illustrate the method and estimate type I error and power. We have also compared this new entropy test to the Fisher exact test for assessment of association with low-frequency SNPs. The entropy test is generally more powerful than the conventional test, and can be significantly more powerful when the genotypic test is applied to low allele-frequency markers. We have also shown that both the Fisher and Entropy methods are optimal to test for association with lowfrequency SNPs (MAF around 1-5%), and both are conservative for very rare SNPs (MAF<1%) Conclusions: We have developed a new, simple, consistent and powerful test to detect genetic association of biallelic/ SNP markers in case-control data, by using symbolic dynamics and symbolic entropy as a measure of gene dependence. We also provide a standard asymptotic distribution of this test statistic. Given that the test is based on entropy measures, it avoids smoothed nonparametric estimation. The entropy test is generally as good or even more powerful than the conventional and Fisher tests. Furthermore, the entropy test is more computationally efficient than the Fisher's Exact test, especially for large number of markers. Therefore, this entropy-based test has the advantage of being optimal for most SNPs, regardless of their allele frequency (Minor Allele Frequency (MAF) between 1-50%). This property is quite beneficial, since many researchers tend to discard low allele-frequency SNPs from their analysis. Now they can apply the same statistical test of association to all SNPs in a single analysis., which can be especially helpful to detect rare effects. [ABSTRACT FROM AUTHOR]

Huntington's disease (HD) is an autosomal dominant neurodegenerative disease caused by a triplet (CAG) expansion mutation. The length of the triplet repeat is the most important factor in determining age of onset of HD, although substantial variability remains after controlling for repeat length. The Venezuelan HD kindreds encompass 18,149 individuals spanning 10 generations, 15,409 of whom are living. Of the 4,384 immortalized lymphocyte lines collected, 3,989 DNAs were genotyped for their HD alleles, representing a subset of the population at greatest genetic risk. There are 938 heterozygotes, 80 people with variably penetrant alleles, and 18 homozygotes. Analysis of the 83 kindreds that comprise the Venezuelan HD kindreds demonstrates that residual variability in age of onset has both genetic and environmental components. We created a residual age of onset phenotype from a regression analysis of the log of age of onset on repeat length. Familial correlations (correlation +/- SE) were estimated for sibling (0.40 +/- 0.09), parent-offspring (0.10 +/- 0.11), avuncular (0.07 +/- 0.11), and cousin (0.15 +/- 0.10) pairs, suggesting a familial origin for the residual variance in onset. By using a variance-components approach with all available familial relationships, the additive genetic heritability of this residual age of onset trait is 38%. A model, including shared sibling environmental effects, estimated the components of additive genetic (0.37), shared environment (0.22), and nonshared environment (0.41) variances, confirming that approximately 40% of the variance remaining in onset age is attributable to genes other than the HD gene and 60% is environmental.