Evidence mapPaperPMID 33236465Full record

ArticleHuman brain mapping2021

Super-variants identification for brain connectivity.

Ting Li, Jianchang Hu, Shiying Wang, Heping Zhang

Open access · goldAbstract read
In one paragraph

Article in Human brain mapping, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.6field-weighted citation impact, top 15% of its field
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

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Genetic underpinnings of brain structural connectome for young adults.Journal of the American Statistical Association · 2023
    Article
  6. Article
  7. 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

4 authors at 1 institution in 1 country.

Ting LiDepartment of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA.ORCID 0000-0002-3880-8609
Jianchang HuDepartment of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA.
Shiying WangDepartment of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA.
Heping ZhangDepartment of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA.ORCID 0000-0002-0688-4076
Yale University · US

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Analysis of Big Data Squared in Biomedical StudiesR01MH116527 · NIMH · YALE UNIVERSITY · PI ZHANG, HEPING · 2018 to 2022
$2.3M
Analysis of Genomic and Complex DataR01HG010171 · NHGRI · YALE UNIVERSITY · PI ZHANG, HEPING · 2019 to 2022
$1.4M
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NCATS NIH HHS UL1 TR001863NHGRI NIH HHS R01 HG010171NIMH NIH HHS R01 MH116527
6 · The paper itself

Abstract

Identifying genetic biomarkers for brain connectivity helps us understand genetic effects on brain function. The unique and important challenge in detecting associations between brain connectivity and genetic variants is that the phenotype is a matrix rather than a scalar. We study a new concept of super-variant for genetic association detection. Similar to but different from the classic concept of gene, a super-variant is a combination of alleles in multiple loci but contributing loci can be anywhere in the genome. We hypothesize that the super-variants are easier to detect and more reliable to reproduce in their associations with brain connectivity. By applying a novel ranking and aggregation method to the UK Biobank databases, we discovered and verified several replicable super-variants. Specifically, we investigate a discovery set with 16,421 subjects and a verification set with 2,882 subjects, where they are formed according to release date, and the verification set is used to validate the genetic associations from the discovery phase. We identified 12 replicable super-variants on Chromosomes 1, 3, 7, 8, 9, 10, 12, 15, 16, 18, and 19. These verified super-variants contain single nucleotide polymorphisms that locate in 14 genes which have been reported to have association with brain structure and function, and/or neurodevelopmental and neurodegenerative disorders in the literature. We also identified novel loci in genes RSPO2 and TMEM74 which may be upregulated in brain issues. These findings demonstrate the validity of the super-variants and its capability of unifying existing results as well as discovering novel and replicable results.

Indexed as

BrainConnectomeGenetic Association StudiesNerve NetAdultDatabases, FactualDatasets as TopicHumansPolymorphism, Single Nucleotidebrian connectivityGWASUK Biobank

Identifiers

PMID33236465
PMCPMC7927294
OpenAlexW3107087560

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.