Evidence map›Paper›PMID 28980263›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2017

Detecting Multiethnic Rare Variants.

Weiwei Ouyang, Xiaofeng Zhu, Huaizhen Qin

Abstract read
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Weiwei OuyangDepartment of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street, Suite 1610, New Orleans, LA, 70112, USA.
Xiaofeng ZhuDepartment of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.
Huaizhen QinDepartment of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street, Suite 1610, New Orleans, LA, 70112, USA. hqin2@tulane.edu.

Funding

Statistical methods for analyzing high-throughput genotype dataR01HG003054 · NHGRI · LOYOLA UNIVERSITY CHICAGO · PI ZHU, XIAOFENG · 2005 to 2017
$3.6M
NHGRI NIH HHS R01 HG003054
6 · The paper itself

Abstract

Genome-wide association studies have identified many common genetic variants which are associated with certain diseases. The identified common variants, however, explain only a small portion of the heritability of a complex disease phenotype. The missing heritability motivated researchers to test the hypothesis that rare variants influence common diseases. Next-generation sequencing technologies have made the studies of rare variants practicable. Quite a few statistical tests have been developed for exploiting the cumulative effect of a set of rare variants on a phenotype. The best-known sequence kernel association tests (SKATs) were developed for rare variants analysis of homogeneous genomes. In this chapter, we illustrate applications of the SKATs and offer several caveats regarding them. In particular, we address how to modify the SKATs to integrate local allele ancestries and calibrate the cryptic relatedness and population structure of admixed genomes.

Indexed as

Genetic VariationAllelesGene FrequencyGenetic Association StudiesGenotypeHigh-Throughput Nucleotide SequencingHumansLinear ModelsModels, GeneticPedigreePhenotypeSoftwareAdmixed populationCommon disease–rare variants hypothesisCryptic relatednessFamily designsGlobal ancestryHomogeneous populationLinear mixed-effect modelsLocal ancestryNext-generation sequencingPopulation structureSib pair designsUnrelated individuals

Identifiers

PMID28980263
PMCPMC6350776

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.