Evidence map›Paper›PMID 28148922›Full record

ArticleJournal of human genetics2017

Detecting disease association with rare variants in case-parents studies.

Yu-Mei Li, Yang Xiang

Abstract read
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In one paragraph

Article in Journal of human genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Yu-Mei LiSchool of Mathematics and Computational Science, Huaihua University, Hunan, China.
Yang XiangSchool of Mathematics and Computational Science, Huaihua University, Hunan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Major advances in DNA sequencing technology have generated large quantities of sequence data that promote the development of statistical methods for rare variant association analyses. Although many population-based case control methods have been well developed for rare variant analysis, little work focuses on family-based studies. In this paper, we extend the existing methods to test for association of rare variants with case-parents data. We investigated the influence of non-variants and effects of causal variants on max-, multi-marker test, and collapsing method, and proposed an adaptive strategy based on a difference vector. Using simulations we show that the collapsing method is affected profoundly by the number of non-causal variants and different direction effects of causal variants and multi-marker test is most robust to non-causal variants and effects of causal variants. Our selective-difference strategy can improve power especially for collapsing method.

Indexed as

Genetic Association StudiesGenetic Predisposition to DiseaseGenetic VariationParentsComputer SimulationHumansTime Factors

Identifiers

PMID28148922

What OpenQuestion holds

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Registered trials

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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.