On the synthesis and interpretation of consistent but weak gene-disease associations in the era of genome-wide association studies is a research paper published in International Journal of Epidemiology (2006). On theSindex it has a DataRank of 5.0. It has been cited 119 times, with 93 citing works in its 1-hop citation network.
Emerging technologies are allowing researchers to study hundreds of thousands of genetic variants simultaneously as risk factors for common complex diseases. Both theoretical considerations and empirical evidence suggest that specific genetic variants causally associated with common diseases will have small effects (risk ratios mostly <2.0). However, the combination of even a few small effects (e.g. effects of fewer than 20 common genetic variants) could account for a sizeable population attributable fraction of common diseases and shed important light on disease pathogenesis and environmental determinants. Nevertheless, the inauguration of genome-wide association studies only magnifies the challenge of differentiating between the expected, true weak associations from the numerous spurious effects caused by misclassification, confounding and significance-chasing biases. Standards are urgently needed for presenting and interpreting cumulative evidence on gene-disease associations, especially for consistent but weak associations. Criteria for synthesis of the evidence should include sound methods for study conduct and analysis, biological plausibility, experimental evidence and adequate replication in large-scale, collaborative studies. Efforts by the Human Genome Epidemiology Network (HuGENet) are currently ongoing to streamline and operationalize these criteria for data on genetic associations with common diseases.
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Base Score Contribution
0.718
From this paper's citation signal
Citation Network Contribution
4.3
From 79 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 14% comes from its base citations and 86% from the citation network (79 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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