2026 Sequencing Strategies for Population and Cancer Epidemiology Studies (SeqSPACE) : Submission #10

serial: '10'
sid: '183392'
uuid: b0acefff-e097-4ae6-8bd2-5e6c6365d619
uri: /egrp/seqspaceabstracts
created: '1781235369'
completed: '1781235369'
changed: '1781235369'
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langcode: en
webform_id: seqspace_abstracts_
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entity_id: '2144'
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data:
  degree_s_: Ph.D.
  email: charlesbreeze@hotmail.com
  first_name: Charles
  last_name: Breeze
  middle_initial: ''
  organization: OEEB/DCEG/NCI
  summary: 'Linkage disequilibrium score regression (LDSC) is an important analytical tool for quantifying heritability and estimating genetic correlations between complex traits. However, the LDSC original implementation relies on an outdated Python 2 framework and deploying the standard command-line tools requires significant setup, data access, and computational expertise, creating a barrier for many researchers. To overcome these limitations, we developed LDscore, a significant technical and accessibility upgraded version of LDSC that allows for rapid analysis of GWAS data. The core advancement is the recoding of the LDSC framework in Python 3, enabling computational optimization and ensuring long-term sustainability. Built on top of this improved foundation, LDscore is implemented as a free, publicly available web application integrated within the popular NCI LDlink framework. LDscore can accelerate scientific research by providing an intuitive graphical interface for heritability estimation, genetic correlation, and LD score calculation, including access to an expanded range of reference populations for online analysis. Notably, our results show that selecting the most appropriate reference population LD panel, even at the subcontinental ancestry group level, is essential for minimizing population stratification bias in heritability estimation. By leveraging cloud computing for superior scalability and eliminating the need for local installation, LDscore adheres to FAIR principles, improving access, traceability, and reproducibility across an expanded set of reference populations, and effectively widens access to researchers worldwide providing support for in-depth genetic analyses.'
  title: 'CRTA Postdoc'
  ttile: 'LDscore: a scalable, Python 3-powered web platform for LD score regression analysis'