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Diana Chang

Researcher at Cornell University

Publications -  12
Citations -  802

Diana Chang is an academic researcher from Cornell University. The author has contributed to research in topics: Genome-wide association study & Genetic association. The author has an hindex of 10, co-authored 12 publications receiving 661 citations. Previous affiliations of Diana Chang include University of Maryland, College Park.

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Meta-analysis of shared genetic architecture across ten pediatric autoimmune diseases

Yun Li, +66 more
- 08 Sep 2015 - 
TL;DR: Network and protein-interaction analyses demonstrated converging roles for the signaling pathways of type 1, 2 and 17 helper T cells (TH1, TH2 and TH17), JAK-STAT, interferon and interleukin in multiple autoimmune diseases.
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Neutral genomic regions refine models of recent rapid human population growth

TL;DR: Targeted sequencing data is introduced for studying recent human history with minimal confounding by natural selection and the best-fit model fits the data very well, largely due to the observation that assumptions of more ancient demography can impact estimates of recent growth.
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Accounting for eXentricities: analysis of the X chromosome in GWAS reveals X-linked genes implicated in autoimmune diseases.

TL;DR: The results demonstrate the importance of the X chromosome in autoimmunity, reveal the potential of extensive XWAS, even based on existing data, and provide the tools and incentive to properly include the X chromosomes in future studies.
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XWAS: A Software Toolset for Genetic Data Analysis and Association Studies of the X Chromosome

TL;DR: XWAS will provide the tools and incentive for others to incorporate the X chromosome into GWAS and similar studies in any species with an XX/XY system, hence enabling discoveries of novel loci implicated in many diseases and in their sexual dimorphism.
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Population growth inflates the per-individual number of deleterious mutations and reduces their mean effect.

TL;DR: To understand how patterns of human genetic variation have been shaped by the interaction of natural selection and population growth, this study examined the trajectories of mutations with varying selection coefficients, using computer simulations.