ArticleStatistics in biosciences2017
Annotation Regression for Genome-Wide Association Studies with an Application to Psychiatric Genomic Consortium Data.
Article in Statistics in biosciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 citations in OpenAlex.
- CERENKOV3: Clustering and molecular network-derived features improve computational prediction of functional noncoding SNPs.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2020Article
- CERENKOV2: improved detection of functional noncoding SNPs using data-space geometric features.BMC bioinformatics · 2019Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Although genome-wide association studies (GWAS) have been successful at finding thousands of disease-associated genetic variants (GVs), identifying causal variants and elucidating the mechanisms by which genotypes influence phenotypes are critical open questions. A key challenge is that a large percentage of disease-associated GVs are potential regulatory variants located in noncoding regions, making them difficult to interpret. Recent research efforts focus on going beyond annotating GVs by integrating functional annotation data with GWAS to prioritize GVs. However, applicability of these approaches is challenged by high dimensionality and heterogeneity of functional annotation data. Furthermore, existing methods often assume global associations of GVs with annotation data. This strong assumption is susceptible to violations for GVs involved in many complex diseases. To address these issues, we develop a general regression framework, named
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.