Nicholas Knoblauch

MIRAGE: a Bayesian rare variant association analysis method incorporating functional information of variants

MIRAGE improves rare variant association analysis by incorporating variant functional information, outperforming existing methods in simulations and autism exome-sequencing data.

Benchmarking autosomal recessive disease prevalence estimation from allele frequencies against newborn screening data

The paper benchmarks genetic prevalence estimates of autosomal recessive diseases against newborn screening data, finding concordance influenced by population databases and ancestry matching.

Transcriptome and regulatory maps of decidua-derived stromal cells inform gene discovery in preterm birth

The paper integrates GWAS data with functional genomic annotations of decidual stromal cells to identify loci and target genes associated with gestational duration and preterm birth.

Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics.

This paper introduces a new Mendelian randomization method, CAUSE, that accounts for correlated and uncorrelated pleiotropic effects, showing improved robustness in detecting true causal relationships.

Model-based analysis of positive selection significantly expands the list of cancer driver genes, including RNA methyltransferases

driverMAPS improves cancer driver gene detection by modeling positive selection and mutation patterns, identifying 159 new potential driver genes including RNA methyltransferases.

A statistical framework for mapping risk genes from de novo mutations in whole-genome sequencing studies

This paper introduces a statistical framework to identify risk genes from de novo mutations in whole-genome sequencing studies, finding new autism risk genes.