Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Kyle Kai-How Farh

dblp:196/4117 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › genomics › genome-wide association study
causal variant prioritization
0.512021
PICS2: next-generation fine mapping via probabilistic identification of causal SNPs · Bioinform. 2021
Bioinformatics and computational biology › statistical genetics › fine-mapping
genetic fine-mapping
0.512021
PICS2: next-generation fine mapping via probabilistic identification of causal SNPs · Bioinform. 2021
Bioinformatics and computational biology › statistical genetics
post-GWAS analysis
0.512021
PICS2: next-generation fine mapping via probabilistic identification of causal SNPs · Bioinform. 2021

Methods — techniques the papers use, named apart from their topics

probabilistic inference · 0.5LD reference data · 0.5
YearPublicationVenuePosition
2021 PICS2: next-generation fine mapping via probabilistic identification of causal SNPs
abstract
SUMMARY: The Probabilistic Identification of Causal SNPs (PICS) algorithm and web application was developed as a fine-mapping tool to determine the likelihood that each single nucleotide polymorphism (SNP) in LD with a reported index SNP is a true causal polymorphism. PICS is notable for its ability to identify candidate causal SNPs within a locus using only the index SNP, which are widely available from published GWAS, whereas other methods require full summary statistics or full genotype data. However, the original PICS web application operates on a single SNP at a time, with slow performance, severely limiting its usability. We have developed a next-generation PICS tool, PICS2, which enables performance of PICS analyses of large batches of index SNPs with much faster performance. Additional updates and extensions include use of LD reference data generated from 1000 Genomes phase 3; annotation of variant consequences; annotation of GTEx eQTL genes and downloadable PICS SNPs from GTEx eQTLs; the option of generating PICS probabilities from experimental summary statistics; and generation of PICS SNPs from all SNPs of the GWAS catalog, automatically updated weekly. These free and easy-to-use resources will enable efficient determination of candidate loci for biological studies to investigate the true causal variants underlying disease processes. AVAILABILITY AND IMPLEMENTATION: PICS2 is available at https://pics2.ucsf.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Kimberly E. Taylor, K. Mark Ansel, Alexander Marson, Lindsey A. Criswell, Kyle Kai-How Farh
Bioinform.5