Zheng Li
@zlisph
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Ph.D. candidate of Biostatistics at the University of Michigan.
Ann Arbor, MI
Joined August 2018
Finally, we conclude by highlighting the key analytical challenges that remain unsolved.
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Next, we discuss recent work to improve fine-mapping methods by integrating additional information, such as functional annotations, multiple traits, multiple genetic ancestries and gene expression studies.
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Following that, we provide an overview of the statistical modeling framework and how different methods can be understood within such framework as making distinct modeling assumptions about variant effect sizes and using different algorithms for inference.
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We begin with a description of the general fine-mapping workflow and basic statistical outputs such as PIP and credible sets.
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Excited to share a review paper we have written on statistical fine-mapping!.
New online! Towards improved fine-mapping of candidate causal variants
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RT @biorxiv_genetic: LoGicAl: Local Ancestry and Genotype Calling Uncertainty-aware Ancestry-specific Allele Frequency Estimation from Admi….
biorxiv.org
In genetic epidemiology and population genetic research of admixed populations, the allele frequencies across ancestral contributing populations, i.e., ancestry-specific allele frequencies, are a...
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RT @biorxiv_genetic: A reference panel for linkage disequilibrium and genotype imputation using whole-genome sequencing data from 2,680 par….
biorxiv.org
India is the most populous country globally, yet genetic studies involving Indian individuals remain limited. The Indian population is composed of many founder groups and has a mixed genetic ancest...
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RT @GeneticsSociety: By modeling cross-ancestry correlations & accounting for LD variations across ancestries, @AJHGNew's latest article de….
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RT @naturemethods: Our April issue is live! 🌧️🌷. On the cover, Spotiphy projects scRNA-seq and histological image d….
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RT @MengjieChen6: Many settle for pseudo-bulk DE in single-cell genomics due to analysis challenges. If you're aiming for true single-cell….
link.springer.com
Background Differential expression analysis is pivotal in single-cell transcriptomics for unraveling cell-type–specific responses to stimuli. While numerous methods are available to identify differ...
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RT @razoralign: VINTAGE: An alternative framework for transcriptome-wide association studies to detect and decipher gene-trait associations….
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RT @NikolayOskolkov: I am giving a seminar discussing recent criticism of using UMAP in All of Us paper in Nature ….
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RT @shang_lulu: Excited to share our latest work: Celina, a statistical method to identify cell type-specific spatially variable genes in s….
nature.com
Nature Communications - The authors develop Celina to detect cell type-specific spatially variable genes (ct-SVGs) in spatial transcriptomics. These ct-SVGs exhibit distinct spatial expression...
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RT @MariosGeorgakis: Single-cell Mendelian randomization (MR) and colocalization using cis-eQTLs from brain snRNAseq🧬❗️. 👉coloc revealed 25….
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RT @RongFan8: Spatial Omics enthusiasts!! Spatial Proteomics is the Method of the Year 2024 .🤩🤩🤩.
nature.com
Spatial proteomics is our pick for Method of the Year 2024. The cover illustrates cellular neighborhoods within tumors, revealing the diverse and complex...
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RT @MariosGeorgakis: GWAS is typically run for cross-sectional traits and not longitudinal trait trajectories📈🧬. 👉New paper describes an ap….
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RT @PurdueBiolSci: Welcome Professor Boran Gao!.Gao will work in @PurdueBiolSci and @PurdueStats He develops statistical methods and compu….
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RT @JSheltzer: Let’s play a little game. Let’s say that you’re the CSO at a cancer pharma company, and you have to choose a target to go….
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