Evidence map›Paper›PMID 38486307›Full record

ReviewHuman genomics2024

Statistical methods for assessing the effects of de novo variants on birth defects.

Yuhan Xie, Ruoxuan Wu, Hongyu Li, Weilai Dong, Geyu Zhou, Hongyu Zhao

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Human genomics, 2024. 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. Article
  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

6 authors.

Yuhan XieDepartment of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, 06520, USA.
Ruoxuan WuDepartment of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, 06520, USA.
Hongyu LiDepartment of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, 06520, USA.
Weilai DongDepartment of Genetics, Yale School of Medicine, New Haven, CT, 06520, USA.
Geyu ZhouDepartment of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, 06520, USA.
Hongyu ZhaoDepartment of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, 06520, USA. hongyu.zhao@yale.edu.

Funding

Novel statistical methods and tools to integrate multiple endophenotypes and functional annotation data to study the roles of rare variants in complex human diseases using sequencing dataR01GM134005 · NIGMS · YALE UNIVERSITY · PI WU, BAOLIN, ZHAO, HONGYU · 2020 to 2023
$1.6M
Statistical Methods for Analyzing Birth Defects CohortsR03HD100883 · NICHD · YALE UNIVERSITY · PI ZHAO, HONGYU · 2021 to 2022
$335k
NICHD NIH HHS R03 HD100883NIGMS NIH HHS R01 GM134005NIH HHS R01GM134005-01A1NIH HHS R03HD100883-01A1
6 · The paper itself

Abstract

With the development of next-generation sequencing technology, de novo variants (DNVs) with deleterious effects can be identified and investigated for their effects on birth defects such as congenital heart disease (CHD). However, statistical power is still limited for such studies because of the small sample size due to the high cost of recruiting and sequencing samples and the low occurrence of DNVs. DNV analysis is further complicated by genetic heterogeneity across diseased individuals. Therefore, it is critical to jointly analyze DNVs with other types of genomic/biological information to improve statistical power to identify genes associated with birth defects. In this review, we discuss the general workflow, recent developments in statistical methods, and future directions for DNV analysis.

Indexed as

Genetic HeterogeneityGenomicsHigh-Throughput Nucleotide SequencingHumansSample SizeWorkflowBirth defectsDe novo variantsIntegrative analysis

Identifiers

PMID38486307
PMCPMC10938830

What Socratic 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 Socratic graph.