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GPA-Tree: Statistical Approach for Functional-Annotation-Tree-Guided Prioritization of GWAS Results

2021-06-12 · Aastha Khatiwada, Bethany J. Wolf, Ayse Selen Yilmaz, Paula S. Ramos, Maciej Pietrzak, Andrew Lawson, Kelly J. Hunt, Hang J. Kim, Dongjun Chung

Motivation: In spite of great success of genome-wide association studies (GWAS), multiple challenges still remain. First, complex traits are often associated with many single nucleotide polymorphisms (SNPs), each with small or moderate effect sizes. Second, our understanding of the functional mechanisms through which genetic variants are associated with complex traits is still limited. To address these challenges, we propose GPA-Tree and it simultaneously implements association mapping and identifies key combinations of functional annotations related to risk-associated SNPs by combining a decision tree algorithm with a hierarchical modeling framework. Results: First, we implemented simulation studies to evaluate the proposed GPA-Tree method and compared its performance with existing statistical approaches. The results indicate that GPA-Tree outperforms existing statistical approaches in detecting risk-associated SNPs and identifying the true combinations of functional annotations with high accuracy. Second, we applied GPA-Tree to a systemic lupus erythematosus (SLE) GWAS and functional annotation data including GenoSkyline and GenoSkylinePlus. The results from GPA-Tree highlight the dysregulation of blood immune cells, including but not limited to primary B, memory helper T, regulatory T, neutrophils and CD8+ memory T cells in SLE. These results demonstrate that GPA-Tree can be a powerful tool that improves association mapping while facilitating understanding of the underlying genetic architecture of complex traits and potential mechanisms linking risk-associated SNPs with complex traits.

📄 PDF Abstract BibTeX arXiv:2106.06877

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Automatic Extraction and Evaluation of Arabic LFG Resources

2012-05-01 · LREC 2012 5 · Mohammed Attia, Khaled Shaalan, Lamia Tounsi, Josef van Genabith

This paper presents the results of an approach to automatically acquire large-scale, probabilistic Lexical-Functional Grammar (LFG) resources for Arabic from the Penn Arabic Treebank (ATB). Our starting point is the earl…

POS

FedTreeLoRA: Reconciling Statistical and Functional Heterogeneity in Federated LoRA Fine-Tuning

2026-02-27 · Jieming Bian, Lei Wang, Letian Zhang, Jie Xu arxiv

Federated Learning (FL) with Low-Rank Adaptation (LoRA) has become a standard for privacy-preserving LLM fine-tuning. However, existing personalized methods predominantly operated under a restrictive Flat-Model Assumptio…

Federated Learning

Croatian Dependency Treebank 2.0: New Annotation Guidelines for Improved Parsing

2014-05-01 · LREC 2014 5 · {\v{Z}}eljko Agi{\'c}, Da{\v{s}}a Berovi{\'c}, Danijela Merkler, Marko Tadi{\'c}

We present a new version of the Croatian Dependency Treebank. It constitutes a slight departure from the previously closely observed Prague Dependency Treebank syntactic layer annotation guidelines as we introduce a new …

Dependency Parsing

Sprucing up the trees -- Error detection in treebanks

2018-08-01 · COLING 2018 8 · Ines Rehbein, Josef Ruppenhofer

We present a method for detecting annotation errors in manually and automatically annotated dependency parse trees, based on ensemble parsing in combination with Bayesian inference, guided by active learning. We evaluate…

Active Learning Bayesian Inference Domain Adaptation Named Entity Recognition (NER)

Annotation Trees: LDC's customizable, extensible, scalable, annotation infrastructure

2012-05-01 · LREC 2012 5 · Jonathan Wright, Kira Griffitt, Joe Ellis, Stephanie Strassel 외

In recent months, LDC has developed a web-based annotation infrastructure centered around a tree model of annotations and a Ruby on Rails application called the LDC User Interface (LUI). The effort aims to centralize all…

Reading Comprehension