Sina Rüeger

OTTERS is a TWAS framework that uses summary-level eQTL reference data to estimate eQTL weights and perform omnibus TWAS, showing improved power and applicability.

Finnish-enriched low-frequency variants, often coding, underlie many GWAS signals across 1,932 register endpoints in 218,792 Finns; a 15-disease meta-analysis with Estonian and UK biobanks adds 30 unreported associations.

The paper improves genotype imputation in underrepresented populations by adding population-specific SNPs to existing reference panels, showing improved accuracy in a Tanzanian cohort.

This study identifies 12,111 SNPs associated with height from 5.4 million individuals, explaining most common SNP-based heritability and showing ancestry-specific prediction accuracy.

This study identifies 277 T2D loci using diverse populations and improves fine-mapping to assess causal genes and mechanisms, enhancing clinical translation.

The paper uses blood eQTL meta-analysis to explore the polygenic architecture of complex traits, finding that trans-eQTLs are more informative for identifying causal genes than cis-eQTLs.

This study applies an improved GWAS strategy to age-related diseases, identifying new genetic loci by using multiple reference panels, analyzing the X chromosome, and testing non-additive models.

The paper investigates how human genetic variation influences Epstein-Barr virus (EBV) sequence diversity by analyzing paired viral and human genomic data from HIV-coinfected individuals.

This paper introduces TWMR, a Mendelian Randomization method that identifies gene-trait associations by integrating GWAS and eQTL data, revealing 3,913 novel associations across 43 human phenotypes.

The paper improves summary statistics imputation by addressing ancestry admixture effects, showing benefits when reference panel size exceeds 1000.

This study identifies 139 common and 4 rare genetic variants associated with type 2 diabetes through GWAS and integrates gene expression and epigenomic data to highlight potential functional genes and regulatory mechanisms.

This study identifies genetic risk loci for narcolepsy, showing enrichment in immune cells and suggesting autoimmune modulation via T cell receptor chains and influenza-A infection.

The paper identifies gene targets for brain-related traits using transcriptomic and methylomic data from blood, finding strong correlations between genetic effects in brain and blood tissues.

This study evaluates summary statistics imputation for discovering new height-associated loci, finding it more effective than genotype imputation in distinguishing true associations from null results.