Alexey A. Shadrin

This study identifies 14 novel opioid use disorder loci and 7 shared loci with schizophrenia, bipolar disorder, and major depression using genome-wide association summary statistics.

The paper identifies abundant genetic pleiotropy across neuroimaging modalities using a multivariate genome-wide association study in two cohorts.

This paper performs a large meta-analysis of GWAS in dementia with Lewy bodies, identifying sex-specific genetic risk signals and highlighting the need for larger cohorts.

The paper evaluates the replication of genome-wide association loci discovered using MOSTest across different cohorts and finds higher replication yields and rates compared to min-P.

The paper introduces Meta-MOSTest, an extension of MOSTest for meta-analysis of multivariate GWAS, showing it maintains statistical power and type I error control across multiple cohorts.

This study identifies genetic resilience factors for Alzheimer's disease by constructing polygenic resilience scores that reduce genetic risk penetrance and correlate with polygenic risk scores in unaffected individuals.

The paper identifies shared genetic variants between self-reported mental health and brain functional connectivity in the UK Biobank using multivariate statistical methods.

The paper identifies 173 genetic loci associated with hippocampal formation volume and finds genetic overlap with eight brain disorders.

This study uses a multivariate genetic approach to analyze personality and cognitive traits, identifying pleiotropy and improving polygenic prediction.

This study conducts a GWAS on MRI-derived body composition measures, identifying genetic loci and genes associated with body composition and cardiometabolic diseases, with liver fat showing strong genetic associations.

This study identifies 856 genetic loci associated with sulcal depth through a genome-wide association study using UK Biobank data, showing higher heritability and locus yield compared to cortical thickness and surface area.

The paper uses a multivariate GWAS approach to identify 780 genetic loci associated with cortical morphology, showing a 10-fold increase in loci compared to univariate methods.

This study identifies genetic overlap between Alzheimer's disease and depression using GWAS data and biostatistical tools, finding shared variants and brain morphology associations.

This study identifies 42 genetic loci associated with thalamus and its nuclei volumes and finds genetic overlaps with cortical regions and ten brain disorders.

The paper introduces YTTRIUM, a QC method for diffusion MRI data, showing it improves brain age prediction accuracy in UK Biobank data.

This study identifies seven novel genetic loci for Alzheimer's disease using a large GWAS and highlights microglia and immune pathways as key contributors to disease pathology.

The paper extends a Gaussian mixture model to account for linkage disequilibrium and heterozygosity in GWAS summary statistics, improving estimates of genetic quantities like heritability.

The paper identifies genetic loci associated with brainstem volumes and finds genetic overlaps with psychiatric and neurological disorders.

The paper introduces MOSTest, a multivariate statistical method for GWAS, identifying 347 genetic loci associated with brain morphology in UK Biobank data.

The paper introduces AI-MiXeR, a method to estimate variant counts and effect sizes across functional annotations using GWAS summary statistics, revealing phenotype-specific polygenicity and effect size differences.

The paper estimates polygenicity and discoverability of causal SNPs for human traits using a univariate Gaussian mixture model applied to GWAS summary statistics.

The paper estimates brain age gaps using MRI and machine learning in individuals with and without brain disorders, finding heritability and genetic overlap with common disorders.

This study identifies shared genetic loci between multiple system atrophy and inflammatory bowel disease using conjFDR, highlighting immune and gut-related molecular mechanisms.

MiXeR quantifies polygenic overlap between complex traits using GWAS summary statistics, revealing shared causal variants beyond genetic correlation.

The paper presents a computational framework to parameterize large-scale mechanistic models for predicting drug response in cancer cell lines using exome and transcriptome data, achieving higher accuracy than statistical approaches.

The paper estimates residual inflation in GWAS summary statistics caused by cryptic relatedness and shows correction reduces significant loci for height and schizophrenia.