Evidence map›Paper›PMID 38559152›Full record

ArticlebioRxiv : the preprint server for biology2024

Analyzing bivariate cross-trait genetic architecture in GWAS summary statistics with the BIGA cloud computing platform.

Yujue Li, Fei Xue, Bingxuan Li, Yilin Yang, Zirui Fan, Juan Shu, Xiaochen Yang, Xiyao Wang, Jinjie Lin, Carlos Copana and 1 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. 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

11 authors.

Yujue LiDepartment of Statistics, Purdue University, West Lafayette, IN 47907, USA.
Fei XueDepartment of Statistics, Purdue University, West Lafayette, IN 47907, USA.
Bingxuan LiDepartment of Computer Science, Purdue University, West Lafayette, IN 47907, USA.
Yilin YangDepartment of Computer and Information Science and Electrical and Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA.
Zirui FanDepartment of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA.
Juan ShuDepartment of Statistics, Purdue University, West Lafayette, IN 47907, USA.
Xiaochen YangDepartment of Statistics, Purdue University, West Lafayette, IN 47907, USA.
Xiyao WangDepartment of Computer Science, Purdue University, West Lafayette, IN 47907, USA.
Jinjie LinYale School of Management, Yale University, New Haven, CT 06511, USA.
Carlos CopanaDepartment of Statistics, Purdue University, West Lafayette, IN 47907, USA.
Bingxin ZhaoDepartment of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA.ORCID 0000-0002-0979-7891

Funding

Mapping the Causal Genetic-Imaging-Clinical Pathway for Alzheimer's DiseaseRF1AG082938 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Bingxin Zhao, Hongtu Zhu · 2023 to 2026
$3.6M
Shared neural basis and genetic architecture of psychiatric disordersR01MH136055 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI Bingxin Zhao · 2025 to 2026
$1.3M
NIA NIH HHS RF1 AG082938NIMH NIH HHS R01 MH136055
6 · The paper itself

Abstract

As large-scale biobanks provide increasing access to deep phenotyping and genomic data, genome-wide association studies (GWAS) are rapidly uncovering the genetic architecture behind various complex traits and diseases. GWAS publications typically make their summary-level data (GWAS summary statistics) publicly available, enabling further exploration of genetic overlaps between phenotypes gathered from different studies and cohorts. However, systematically analyzing high-dimensional GWAS summary statistics for thousands of phenotypes can be both logistically challenging and computationally demanding. In this paper, we introduce BIGA (https://bigagwas.org/), a website that aims to offer unified data analysis pipelines and processed data resources for cross-trait genetic architecture analyses using GWAS summary statistics. We have developed a framework to implement statistical genetics tools on a cloud computing platform, combined with extensive curated GWAS data resources. Through BIGA, users can upload data, submit jobs, and share results, providing the research community with a convenient tool for consolidating GWAS data and generating new insights.

Indexed as

Cloud computingCross-trait analysisGWASOnline platform

Identifiers

PMID38559152
PMCPMC10979906

What OpenQuestion 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 OpenQuestion graph.