Dataset Information

The missing metric: quantifying contributions of reviewers.

ABSTRACT: The number of contributing reviewers often outnumbers the authors of publications. This has led to apathy towards reviewing and the conclusion that the peer-review system is broken. Given the trade-offs between submitting and reviewing manuscripts, reviewers and authors naturally want visibility for their efforts. While study after study has called for revolutionizing publication practices, the current paradigm does not recognize reviewers' time and expertise. We propose the R-index as a simple way to quantify scientists' contributions as reviewers. We modelled its performance using simulations based on real data to show that early-mid career scientists, who complete high-quality reviews of longer manuscripts within their field, can perform as well as leading scientists reviewing only for high-impact journals. By giving citeable academic recognition for reviewing, R-index will encourage more participation with better reviews, regardless of the career stage. Moreover, the R-index will allow editors to exploit scores to manage and improve their review team, and for journals to promote high average scores as signals of a practical and efficient service to authors. Peer-review is a pervasive necessity across disciplines and the simple utility of this missing metric will credit a valuable aspect of academic productivity without having to revolutionize the current peer-review system.

SUBMITTER: Cantor M

PROVIDER: S-EPMC4448813 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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The missing metric: quantifying contributions of reviewers.

Cantor Maurício M Gero Shane S

Royal Society open science 20150211 2

The number of contributing reviewers often outnumbers the authors of publications. This has led to apathy towards reviewing and the conclusion that the peer-review system is broken. Given the trade-offs between submitting and reviewing manuscripts, reviewers and authors naturally want visibility for their efforts. While study after study has called for revolutionizing publication practices, the current paradigm does not recognize reviewers' time and expertise. We propose the R-index as a simple ...[more]

PMID: 26064609

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Project description:Recent work has shown that much of the missing heritability of complex traits can be resolved by estimates of heritability explained by all genotyped SNPs. However, it is currently unknown how much heritability is missing due to poor tagging or additional causal variants at known GWAS loci. Here, we use variance components to quantify the heritability explained by all SNPs at known GWAS loci in nine diseases from WTCCC1 and WTCCC2. After accounting for expectation, we observed all SNPs at known GWAS loci to explain 1.29 x more heritability than GWAS-associated SNPs on average (P=3.3 x 10??). For some diseases, this increase was individually significant: 2.07 x for Multiple Sclerosis (MS) (P=6.5 x 10??) and 1.48 x for Crohn's Disease (CD) (P = 1.3 x 10?³); all analyses of autoimmune diseases excluded the well-studied MHC region. Additionally, we found that GWAS loci from other related traits also explained significant heritability. The union of all autoimmune disease loci explained 7.15 x more MS heritability than known MS SNPs (P < 1.0 x 10?¹? and 2.20 x more CD heritability than known CD SNPs (P = 6.1 x 10??), with an analogous increase for all autoimmune diseases analyzed. We also observed significant increases in an analysis of > 20,000 Rheumatoid Arthritis (RA) samples typed on ImmunoChip, with 2.37 x more heritability from all SNPs at GWAS loci (P = 2.3 x 10??) and 5.33 x more heritability from all autoimmune disease loci (P < 1 x 10?¹? compared to known RA SNPs (including those identified in this cohort). Our methods adjust for LD between SNPs, which can bias standard estimates of heritability from SNPs even if all causal variants are typed. By comparing adjusted estimates, we hypothesize that the genome-wide distribution of causal variants is enriched for low-frequency alleles, but that causal variants at known GWAS loci are skewed towards common alleles. These findings have important ramifications for fine-mapping study design and our understanding of complex disease architecture.

Dataset Information

The missing metric: quantifying contributions of reviewers.

Publications

The missing metric: quantifying contributions of reviewers.

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