An Atlas of Genetic Correlations across Human Diseases and Traits

By Brendan K. Bulik-Sullivan, Hilary K Finucane, V. Anttila, Alexander Gusev, F. Day, Po-Ru Loh, L. Duncan, J. Perry, Nick Patterson, E. Robinson and 3 more. Nature Genetics, 2015

4,075 citations recorded by the source corpus.

Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual-level genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique—cross-trait LD Score regression—for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity, and educational attainment and several diseases. These results highlight the power of genome-wide analyses, as there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.

Subfields: Cognitive Neuroscience, Genetics

View Paper: https://www.semanticscholar.org/paper/bd00643c0a5d7a8771b24b431a5c864c811c2fa1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Sacha Epskamp
2. R. Mõttus
3. G. Costantini
4. D. Liewald
5. D. Wood
6. David M. Condon
7. Elliot M. Tucker-Drob
8. Mark J. Daly

In Genetics

1. Alkes L Price
2. Peter M. Visscher
3. Jian Yang
4. Mark J. Daly
5. George Davey Smith
6. Po-Ru Loh
7. Benjamin M. Neale
8. Zhili Zheng

Citations in the corpus

• A catalog of genetic loci associated with kidney function from analyses of a million individuals (2019, methodology): “We assessed genome-wide genetic correlations (rg) of eGFR associations with each of 23 748 complex traits and diseases (Methods).(28) We observed 37 significant correlations 24 (P<6.”
• Genome-wide analysis identifies 12 loci influencing human reproductive behavior (2016, neutral): “Supplementary Table 39 reports the estimated coefficients from these regressions.”
• Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases (2020, methodology): “To identify independent traits, we first computed genetic correlations between all pairs of traits using cross-trait LD-score regression 57 with LD scores from UK10K 58 .”
• Partitioning heritability by functional annotation using genome-wide association summary statistics (2015, background): “This is consistent with our understanding that the genetic component of educational attainment, which excludes environmental factors and population structure, is highly correlated with IQ [45].”