Social psychological determinants of recreation: An exploratory analysis

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Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

A Meta‐Analysis of Studies Attributing Significance to Solar Irradiance

Abstract The relationship between solar irradiance and climate is greatly debated. This inferred relationship is often characterized via the statistical analysis of paleoclimate data. REDFIT is a commonly used statistical tool that overcomes uneven sampling to identify significant periodicities of variability in proxy data. We critically examine the use of REDFIT to identify solar signals in these data. By conducting a literature review, we show the REDFIT significance thresholds used by researchers to analyze paleoclimate data vary considerably. As there is some subjectivity and practicality involved in any statistical analysis, some variability is to be expected. However, we observe that the bulk of the significance thresholds used in the literature are less stringent than the critical false‐alarm level outlined by REDFIT's creators. We reexamine periodicities deemed “significant” in a published data set to show that using this more stringent, more objective critical false‐alarm threshold likely eliminates the previously inferred significance of solar signals in proxy data. Likewise, we address a lack of consideration of age model uncertainty on REDFIT's reliability in identifying solar periodicities. Overall, we show that the relationship between solar irradiance and climate, as identified by REDFIT analyses, may not be as robust as previous work might suggest