Meta-analysis of correlated traits via summary statistics from GWASs with an application in hypertension

Genome-wide association studies (GWASs) have identified many genetic variants underlying complex traits. Many detected genetic loci harbor variants that associate with multiple-even distinct-traits. Most current analysis approaches focus on single traits, even though the final results from multiple traits are evaluated together. Such approaches miss the opportunity to systemically integrate the phenome-wide data available for genetic association analysis. In this study, we propose a general approach that can integrate association evidence from summary statistics of multiple traits, either correlated, independent, continuous, or binary traits, which might come from the same or different studies. We allow for trait heterogeneity effects. Population structure and cryptic relatedness can also be controlled. Our simulations suggest that the proposed method has improved statistical power over single-trait analysis in most of the cases we studied. We applied our method to the Continental Origins and Genetic Epidemiology Network (COGENT) African ancestry samples for three blood pressure traits and identified four loci (CHIC2, HOXA-EVX1, IGFBP1/IGFBP3, and CDH17; p < 5.0 × 10(-8)) associated with hypertension-related traits that were missed by a single-trait analysis in the original report. Six additional loci with suggestive association evidence (p < 5.0 × 10(-7)) were also observed, including CACNA1D and WNT3. Our study strongly suggests that analyzing multiple phenotypes can improve statistical power and that such analysis can be executed with the summary statistics from GWASs. Our method also provides a way to study a cross phenotype (CP) association by using summary statistics from GWASs of multiple phenotypes.

Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics.

Integrating single nucleotide polymorphism (SNP) p-values from genome-wide association studies (GWAS) across genes and pathways is a strategy to improve statistical power and gain biological insight. Here, we present Pascal (Pathway scoring algorithm), a powerful tool for computing gene and pathway scores from SNP-phenotype association summary statistics. For gene score computation, we implemented analytic and efficient numerical solutions to calculate test statistics. We examined in particular the sum and the maximum of chi-squared statistics, which measure the strongest and the average association signals per gene, respectively. For pathway scoring, we use a modified Fisher method, which offers not only significant power improvement over more traditional enrichment strategies, but also eliminates the problem of arbitrary threshold selection inherent in any binary membership based pathway enrichment approach. We demonstrate the marked increase in power by analyzing summary statistics from dozens of large meta-studies for various traits. Our extensive testing indicates that our method not only excels in rigorous type I error control, but also results in more biologically meaningful discoveries.

Statistics in Public Understanding of Science review: How to achieve high statistical standards?

This article proposes a checklist to improve statistical reporting in the manuscripts submitted to Public Understanding of Science. Generally, these guidelines will allow the reviewers (and readers) to judge whether the evidence provided in the manuscript is relevant. The article ends with other suggestions for a better statistical quality of the journal.

European cancer mortality predictions for the year 2016 with focus on leukemias.

BACKGROUND: Current cancer mortality statistics are important for public health decision making and resource allocation. Age standardized rates and numbers of deaths are predicted for 2016 in the European Union. PATIENTS AND METHODS: Population and death certification data for stomach, colorectum, pancreas, lung, breast, uterus, prostate, leukemia and total cancers were obtained from the World Health Organisation database and Eurostat. Figures were derived for the EU, France, Germany, Italy, Poland, Spain and the UK. Projected numbers of deaths by age group were obtained for 2016 by linear regression on estimated numbers of deaths over the most recent time period identified by a joinpoint regression model. RESULTS: Projected total cancer mortality trends for 2016 in the EU are favourable in both sexes with rates of 133.5/100,000 men and 85.2/100,000 women (8% and 3% falls since 2011, due to population ageing) corresponding to 753,600 and 605,900 deaths in men and women for a total number of 1,359,500 projected cancer deaths (+3% compared to 2011). In men lung, colorectal and prostate cancer fell 11%, 5% and 8% since 2011. Breast and colorectal cancer trends in women are favourable (8% and 7% falls, respectively), but lung and Pancreatic cancer rates rose 5% and 4% since 2011 reaching rates of 14.4 and 5.6/100,000 women. Leukemia shows favourable projected mortality for both sexes and all age groups with stronger falls in the younger age groups, rates are 4.0/100,000 men and 2.5/100,000 women, with respectively falls of 14% and 12%. CONCLUSION: The 2016 predictions for EU cancer mortality confirm the favourable trends in rates particularly for men. Lung cancer is likely to remain the leading site for female cancer rates. Continuing falls in mortality, larger in children and young adults, are predicted in leukemia, essentially due to advancements in management and therapy, and their subsequent adoption across Europe.