Kyle Kai‐How Farh

Single-cell eQTL mapping in 5.4 million PBMCs from 1,925 individuals with matched WGS finds common-variant eQTLs for 83% and rare-variant signals for 47% of 21,404 genes.

The paper identifies immune cell type-specific gene expression regulated by tandem repeats using multi-omics data from large cohorts.

This paper introduces SpliceAI2, a deep learning model that improves identification and quantification of splice-altering variants and their effects on transcripts and disease.

FlexRV improves gene discovery for complex traits by modeling nonlinear relationships between variant annotations and phenotypes, outperforming existing methods in association detection and replication.

Cross-species genome-phenome mapping in primates reveals thousands of protein-coding variants linked to the evolution of complex traits.

This study creates a proteome-wide gain-of-function atlas to map chemotherapy responses and identifies drug combinations to overcome resistance.

The paper maps chromatin accessibility and genetic variation in immune cells, showing how integrating cell-type-specific data improves disease risk understanding.

The paper presents a single-cell multi-omics atlas of human development and uses deep learning to analyze regulatory DNA motifs and their impact on chromatin accessibility and gene expression.

The paper analyzes the genomes of spider monkeys and northern muriquis to assess genetic diversity, load, and divergence, finding no alignment between genetic diversity and endangerment status, higher genetic load in wild populations, and divergence linked to environmental factors.

The paper evaluates the reliability of phylogenetic inference using primate enamel proteins by simulating sequence fragmentation and comparing results to a reference species tree.

The paper compares genomic diversity, inbreeding, and genetic load between insular and mainland populations of three West African primates, finding lower diversity and higher inbreeding in island populations but no evidence of increased deleterious variation.

The paper benchmarks PrimateAI-3D and AlphaMissense on real-world cohorts, finding PrimateAI-3D performs better in rare-disease and biobank settings.

The paper uses single cell RNA sequencing to assess the effects of protein-altering variants in three genes, showing that single cell expression signatures are more effective than bulk assays and computational models for predicting pathogenicity.

The paper uses primate genetic data to predict human variant pathogenicity with a deep learning classifier trained on 4.3 million common missense variants.

This study presents whole-genome data from 233 primate species, revealing patterns of genetic diversity, mutation rates, and recurrent missense mutations across primate clades.

The paper identifies rare variant polygenic risk scores as more effective than common variant polygenic risk scores in identifying individuals with outlier phenotypes in common human diseases and complex traits.

This study uses single-cell chromatin accessibility analysis to identify developmental trajectories and non-coding mutations associated with congenital heart disease.

The paper integrates multi-omic data to identify genetic and microbiome factors associated with severe COVID-19, independent of ancestry and social factors.

The paper maps gene-regulatory dynamics in the developing human cerebral cortex using single-cell data, identifying key transcription factors and disrupted regulatory elements in ASD.

The paper analyzes ACE2 gene sequences in strepsirrhine primates to assess their susceptibility to SARS-CoV-2, finding high variability and potential risk in some species.

This paper develops a high-throughput method to generate host and pathogen genomic data from single nasopharyngeal swabs of COVID-19 patients.