Author

Maria Teresa Landi

National Institutes of Health

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Same name, identified by ORCID

Biology Disciplines

Click to filter author papers

Genetics· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:statistical model· 3 Assay:GWAS summary statistics· 2 Organism:human· 2 Research Focus:GWAS / association mapping· 1 Research Focus:polygenic risk· 1 Research Focus:variant effect prediction· 1 Assay:whole-exome sequencing· 1

Frequent Co-Authors

Jianxin ShiiD· 2 shared Neil E. CaporasoiD· 2 shared Nilanjan ChatterjeeiD· 2 shared Xing Hua· 2 shared Adèle C. GreeniD· 1 shared Adeline SeowiD· 1 shared Alan R. SandersiD· 1 shared Alessia ViscontiiD· 1 shared

Activity by Year

2016-2018 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersBioinformatics

Jianxin ShiORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

GeneticsNature CommunicationsPeer-reviewedNov 8, 2018

Novel pleiotropic risk loci for melanoma and nevus density implicate multiple biological pathways

David L. Duffy, Gu Zhu, Xin Li…·Nicholas Orr

This paper identifies novel pleiotropic risk loci for melanoma and nevus density, implicating multiple biological pathways and gene expression under MITF regulation.

117 citationstop 2%

humanGWAS summary statistics

𝕏 PostBluesky

BioinformaticsThe American Journal of Human GeneticsPeer-reviewedMar 1, 2016

MEGSA: A powerful and flexible framework for analyzing mutual exclusivity of tumor mutations

Xing Hua, Paula L. Hyland, Jing Huang…·Jianxin Shi

The paper introduces MEGSA, a framework for analyzing mutual exclusivity of tumor mutations, identifying novel gene sets and associations in cancer types like AML and breast cancer.

DOI 10.1016/j.ajhg.2015.12.021

humanwhole-exome sequencing

𝕏 PostBluesky

GeneticsbioRxivPreprintJan 10, 2016

Winners curse correction and variable thresholding improve performance of polygenic risk modeling based on summary-level data from genome-wide association studies

Jianxin Shi, Ju-Hyun Park, Jubao Duan…·Nilanjan Chatterjee

This paper improves polygenic risk scores by applying winners curse correction and variable thresholds to GWAS summary data, enhancing prediction accuracy for some diseases.

DOI 10.1101/034082

GWAS summary statisticsstatistical model

𝕏 PostBluesky