Biology subjects

Fiziev, P.

Publications and source records attributed to Fiziev, P..

4 recordsLinked to original sources

bioRxiv2025

Flexibly Modeling Rare Variant Pathogenicity Improves Gene Discovery for Complex Traits

Rare variant burden tests can directly identify genes that influence complex traits, but their power is limited by our ability to separate functional from benign alleles. We introduce FlexRV, an approach that greatly improves the power to detect gene-based associations in rare variant aggregation tests by modelling nonlinear relationships between functional annotations and phenotype. Across 62 quantitative and 44 disease traits in the UK Biobank, we show that FlexRV outperforms previous approaches such as DeepRVAT, STAAR, and Regenie, discovering 51% more quantitative and 102% more disease trait associations than the widely used Regenie method. Compared to discoveries from other methods, gene-phenotype associations identified by FlexRV replicated at a higher rate in the independent All of Us cohort and were more highly enriched at genes nominated by common variant genome-wide association studies. We explore the genetic architecture of complex traits using FlexRV burden tests, finding nearly equal contributions from missense and loss of function variants to rare variant burden heritability. FlexRV weights can also be incorporated into rare variant polygenic scores, improving their ability to identify individuals with extreme phenotypes. Our study illustrates the benefits of modelling nonlinear relationships between annotated variant effects and their downstream phenotypes in rare variant studies.

genetics↗

bioRxiv2023

The landscape of tolerated genetic variation in humans and primates

Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole genome sequencing data for 809 individuals from 233 primate species, and identified 4.3 million common protein-altering variants with orthologs in human. We show that these variants can be inferred to have non-deleterious effects in human based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases. One Sentence SummaryDeep learning classifier trained on 4.3 million common primate missense variants predicts variant pathogenicity in humans.

genomics↗

bioRxiv2022

A framework for summarizing chromatin state annotations within and identifying differential annotations across groups of samples

MotivationGenome-wide maps of epigenetic modifications are powerful resources for non-coding genome annotation. Maps of multiple epigenetics marks have been integrated into cell or tissue type-specific chromatin state annotations for many cell or tissue types. With the increasing availability of multiple chromatin state maps for biologically similar samples, there is a need for methods that can effectively summarize the information about chromatin state annotations within groups of samples and identify differences across groups of samples at a high resolution. ResultsWe developed CSREP, which takes as input chromatin state annotations for a group of samples and then probabilistically estimates the state at each genomic position and derives a representative chromatin state map for the group. CSREP uses an ensemble of multi-class logistic regression classifiers to predict the chromatin state assignment of each sample given the state maps from all other samples. The difference of CSREPs probability assignments for two groups can be used to identify genomic locations with differential chromatin state patterns. Using groups of chromatin state maps of a diverse set of cell and tissue types, we demonstrate the advantages of using CSREP to summarize chromatin state maps and identify biologically relevant differences between groups at a high resolution. Availability and implementationThe CSREP source code is openly available under http://github.com/ernstlab/csrep. Contact: jason.ernst@ucla.edu

bioinformatics↗

bioRxiv2017

Modeling Spatio-temporal Dynamics of Chromatin Marks

To model spatial changes of chromatin mark peaks over time we developed and applied ChromTime, a computational method that predicts regions for which peaks either expand or contract significantly or hold steady between time points. Predicted expanding and contracting peaks can mark regulatory regions associated with transcription factor binding and gene expression changes. Spatial dynamics of peaks provided information about gene expression changes beyond localized signal density changes. ChromTime detected asymmetric expansions and contractions, which for some marks associated with the direction of transcription. ChromTime facilitates the analysis of time course chromatin data in a range of biological systems.

bioinformatics↗