Estimating SNP-Based Heritability and Genetic Correlation in Case-Control Studies Directly and with Summary Statistics

Omer Weissbrod*, Jonathan Flint, Saharon Rosset

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Methods that estimate SNP-based heritability and genetic correlations from genome-wide association studies have proven to be powerful tools for investigating the genetic architecture of common diseases and exposing unexpected relationships between disorders. Many relevant studies employ a case-control design, yet most methods are primarily geared toward analyzing quantitative traits. Here we investigate the validity of three common methods for estimating SNP-based heritability and genetic correlation between diseases. We find that the phenotype-correlation-genotype-correlation (PCGC) approach is the only method that can estimate both quantities accurately in the presence of important non-genetic risk factors, such as age and sex. We extend PCGC to work with arbitrary genetic architectures and with summary statistics that take the case-control sampling into account, and we demonstrate that our new method, PCGC-s, accurately estimates both SNP-based heritability and genetic correlations and can be applied to large datasets without requiring individual-level genotypic or phenotypic information. Finally, we use PCGC-s to estimate the genetic correlation between schizophrenia and bipolar disorder and demonstrate that previous estimates are biased, partially due to incorrect handling of sex as a strong risk factor.

Original languageEnglish
Pages (from-to)89-99
Number of pages11
JournalAmerican Journal of Human Genetics
Issue number1
StatePublished - 5 Jul 2018


  • GWAS
  • ascertainment
  • case-control studies
  • genetic correlation
  • heritability


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