Ranking of genome-wide association scan signals by different measures

Genome wide association scan for chronic periodontitis implicates novel locus

BACKGROUND

There is evidence for a genetic contribution to chronic periodontitis. In this study, we conducted a genome wide association study among 866 participants of the University of Pittsburgh Dental Registry and DNA Repository, whose periodontal diagnosis ranged from healthy (N = 767) to severe chronic periodontitis (N = 99).

METHODS

Genotypingi of over half-million single nucleotide polymorphisms was determined. Analyses were done twice, first in the complete dataset of all ethnicities, and second including only samples defined as self-reported Whites. From the top 100 results, twenty single nucleotide polymorphisms had consistent results in both analyses (borderline p-values ranging from 1E-05 to 1E-6) and were selected to be tested in two independent datasets derived from 1,460 individuals from Porto Alegre, and 359 from Rio de Janeiro, Brazil. Meta-analyses of the Single nucleotide polymorphisms showing a trend for association in the independent dataset were performed.

RESULTS

The rs1477403 marker located on 16q22.3 showed suggestive association in the discovery phase and in the Porto Alegre dataset (p = 0.05). The meta-analysis suggested the less common allele decreases the risk of chronic periodontitis.

CONCLUSIONS

Our data offer a clear hypothesis to be independently tested regarding the contribution of the 16q22.3 locus to chronic periodontitis.

Genome-wide association study for radiographic vertebral fractures: a potential role for the 16q24 BMD locus

Vertebral fracture risk is a heritable complex trait. The aim of this study was to identify genetic susceptibility factors for osteoporotic vertebral fractures applying a genome-wide association study (GWAS) approach. The GWAS discovery was based on the Rotterdam Study, a population-based study of elderly Dutch individuals aged > 55 years; and comprising 329 cases and 2666 controls with radiographic scoring (McCloskey–Kanis) and genetic data. Replication of one top-associated SNP was pursued by de-novo genotyping of 15 independent studies across Europe, the United States, and Australia and one Asian study. Radiographic vertebral fracture assessment was performed using McCloskey–Kanis or Genant semi-quantitative definitions. SNPs were analyzed in relation to vertebral fracture using logistic regression models corrected for age and sex. Fixed effects inverse variance and Han–Eskin alternative random effects meta-analyses were applied. Genome-wide significance was set at p < 5 × 10− 8. In the discovery, a SNP (rs11645938) on chromosome 16q24 was associated with the risk for vertebral fractures at p = 4.6 × 10− 8. However, the association was not significant across 5720 cases and 21,791 controls from 14 studies. Fixed-effects meta-analysis summary estimate was 1.06 (95% CI: 0.98–1.14; p = 0.17), displaying high degree of heterogeneity (I2 = 57%; Qhet p = 0.0006). Under Han–Eskin alternative random effects model the summary effect was significant (p = 0.0005). The SNP maps to a region previously found associated with lumbar spine bone mineral density (LS-BMD) in two large meta-analyses from the GEFOS consortium. A false positive association in the GWAS discovery cannot be excluded, yet, the low-powered setting of the discovery and replication settings (appropriate to identify risk effect size > 1.25) may still be consistent with an effect size < 1.10, more of the type expected in complex traits. Larger effort in studies with standardized phenotype definitions is needed to confirm or reject the involvement of this locus on the risk for vertebral fractures.

A review of post-GWAS prioritization approaches

In the recent decade, high-throughput genotyping and next-generation sequencing platforms have enabled genome-wide association studies (GWAS) of many complex human diseases. These studies have discovered many disease susceptible loci, and unveiled unexpected disease mechanisms. Despite these successes, these identified variants only explain a small proportion of the genetic contributions to these diseases and many more remain to be found. This is largely due to the small effect sizes of most disease-associated variants and limited sample size. As a result, it is critical to leverage other information to more effectively prioritize GWAS signals to increase replication rates and better understand disease mechanisms. In this review, we introduce the biological/genomic features that have been found to be informative for post-GWAS prioritization, and discuss available tools to utilize these features for prioritization

Comparison of six statistics of genetic association regarding their ability to discriminate between causal variants and genetically linked markers

OBJECTIVES

Genome-wide association (GWA) studies still rely on the common-disease common-variant hypothesis since the assumption is associated with increased power. In GWA studies, polymorphisms are genotyped and their association with disease is investigated. Most of the identified associations are indirect and reflect a shared inheritance of the genotyped markers and genetically linked causal variants. We have compared six statistics of genetic association regarding their ability to discriminate between markers and causal susceptibility variants, including a probability value (Pval) and a Bayes Factor (BF) based on logistic regression, and the attributable familial relative risk (FRR).

METHODS

We carried out a simulation-based sensitivity analysis to explore several conceivable scenarios. Theoretical results were illustrated by established causal associations with age-related macular degeneration and by using imputed data based on HapMap for a case-control study of breast cancer.

RESULTS

Our data indicate that a representation of genetic association by FRRs and BFs generally facilitates the distinction of causal variants. The FRR showed the best discriminative power under most investigated scenarios, but no single statistic outperformed the others in all situations. For example, rare moderate- to low-penetrance variants (allele frequency: 1%, dominant odds ratio: ≤2.0) seem to be best discriminated by BFs.

CONCLUSIONS

Present results may help to fully utilize the data generated in association studies that take advantage of next generation sequencing and/or multiple imputation based on the 1000 Genomes Project.

Ranking causal variants and associated regions in genome-wide association studies by the support vector machine and random forest

We study the number of causal variants and associated regions identified by top SNPs in rankings given by the popular 1 df chi-squared statistic, support vector machine (SVM) and the random forest (RF) on simulated and real data. If we apply the SVM and RF to the top 2r chi-square-ranked SNPs, where r is the number of SNPs with P-values within the Bonferroni correction, we find that both improve the ranks of causal variants and associated regions and achieve higher power on simulated data. These improvements, however, as well as stability of the SVM and RF rankings, progressively decrease as the cutoff increases to 5r and 10r. As applications we compare the ranks of previously replicated SNPs in real data, associated regions in type 1 diabetes, as provided by the Type 1 Diabetes Consortium, and disease risk prediction accuracies as given by top ranked SNPs by the three methods. Software and webserver are available at http://svmsnps.njit.edu.

Genetics of type 2 diabetes

PURPOSE OF REVIEW

To provide an overview of the genetics of type 2 diabetes in the context of recent progress in the understanding of the genetic architecture of the disease and its applicability to the pathogenesis of the disease as well as efforts to individualize therapy in type 2 diabetes. Efforts are underway to understand how these loci alter measurable physiologic processes in nondiabetic humans. However, it is important to understand the potential pitfalls in such studies and the limitations underlying measurement of insulin secretion and action using qualitative methodologies.

RECENT FINDINGS

The availability of large population-based cohorts and the ease with which large numbers of common genetic variants can be genotyped has enabled the discovery of multiple loci and pathways associated with type 2 diabetes. Recent efforts examining quantitative traits such as fasting glucose concentrations have led to the discovery of other genes likely to be important in the development of diabetes.

SUMMARY

The past 4 years have witnessed a significant increase in our understanding of genetic predisposition to type 2 diabetes. Hopefully more progress will be made in applying this knowledge to the pathophysiology of type 2 diabetes in the coming years.

Empirical Bayes and semi-Bayes adjustments for a vast number of estimations

Investigators in modern molecular/genetic epidemiology studies commonly analyze data on a vast number of candidate genetic markers. In such situations, rather than conventional estimation of effects (odds ratios), more accurate estimation methods are needed. The author proposes consideration of empirical Bayes and semi-Bayes methods, which yield 'adjustments for multiple estimations' by shrinking conventional effect estimates towards the overall average effect.