TEMR: Trans-ethnic mendelian randomization method using large-scale GWAS summary datasets.

Area of Science:

• Genetics
• Epidemiology
• Statistical genetics

Background:

• Genome-wide association study (GWAS) datasets are predominantly from European ancestries, limiting causal inference precision in other ethnicities via Mendelian randomization (MR).
• Underrepresented populations in genetic studies face challenges in identifying precise causal relationships due to limited sample sizes.

Purpose of the Study:

• To introduce Trans-Ethnic Mendelian Randomization (TEMR), a novel method to enhance statistical power and estimation precision in MR for underrepresented populations.
• To leverage trans-ethnic GWAS summary datasets for more robust causal effect estimations.

Main Methods:

• TEMR incorporates trans-ethnic genetic correlation coefficients within a conditional likelihood-based inference framework.
• The method produces calibrated p-values, significantly improving MR statistical power.
• Simulations were conducted to compare TEMR's performance against existing MR methods.

Main Results:

• TEMR demonstrated superior precision and statistical power in causal effect estimation for target populations in simulations.
• Application of TEMR identified 17 novel causal relationships between 16 blood biomarkers and five diseases (hypertension, ischemic stroke, type 2 diabetes, schizophrenia, major depression disorder) in East Asian, African, and Hispanic/Latino populations.
• These findings were derived using European descent GWAS data, highlighting TEMR's utility in trans-ethnic inference.

Conclusions:

• TEMR effectively improves MR power and precision for underrepresented ethnic groups by utilizing trans-ethnic GWAS data.
• The method successfully identified novel causal biomarker-disease associations, advancing our understanding of disease etiology in diverse populations.
• TEMR offers a valuable tool for genetic epidemiology, enabling more inclusive and accurate causal inference.