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Evaluating the genetic correlations across our phenotypes is of essential importance for understanding disease etiology and other potential causality. A new method—published in the journal Nature Genetics—vastly improves estimates of genetic correlations using established resources from genome-wide association studies.

The research team consisting of Zheng Ning, Yudi Pawitan, and Xia Shen at the Department of Medical Epidemiology and Biostatistics has published a new technique, revealing substantially more genetic correlations between human phenotypes.

The authors derived a full likelihood from the joint distribution of genome-wide genetic effects so that genetic correlation can be estimated with good precision. They compared the new method with the state-of-the-art technique and found that the estimation efficiency is substantially improved, equivalent to a 2.5-fold increase in sample sizes of genomic studies.

The discoveries include significant genetic correlations across a number of human behavioral and . Based on the UK Biobank resource, the power of the new method revealed about 35% more genetic correlations across 30 human traits. This includes 57 genetic correlations that could not be identified using the state-of-the-art technique, including the genetic correlations between human behaviors such as sleep duration, risk taking, and , etc. and complex diseases such as diabetes, depression, and cancer.

The new method uses summary-level instead of individual-level data, obtaining comparable estimates. This vastly speeds up statistical inference for genomic big data. The study powers up future high-throughput genomic studies in large human populations, helping scientists understand the connection between various human complex traits.

More information: Zheng Ning et al. High-definition likelihood inference of genetic correlations across human complex traits, Nature Genetics (2020). DOI: 10.1038/s41588-020-0653-y

Journal information: Nature Genetics