ArticleAmerican journal of human genetics2018
Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood.
Article in American journal of human genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 124 papers, 4 of them syntheses that pooled it.
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Who cites it
124 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- The Genetic Architecture of the Human Corpus Callosum and its Subregions.Nature communications · 2025Pooled it
- Shared genetic risk factors and causal association between psoriasis and coronary artery disease.Nature communications · 2022Pooled it
- Phenotypic and genetic analysis of a wellbeing factor score in the UK Biobank and the impact of childhood maltreatment and psychiatric illness.Translational psychiatry · 2022Pooled it
- Genetic investigation of fibromuscular dysplasia identifies risk loci and shared genetics with common cardiovascular diseases.Nature communications · 2021Pooled it
- Genetic Regulation of DNA Methylation and Its Mediating Role in Blood Pressure: A Genome-Wide Twin Study.MedComm · 2026Article
- Estimating genetic correlation jointly using individual-level and summary-level GWAS data.Nature communications · 2026Article
- Multi-tissue integrated Mendelian randomization method identifies disease risk genes.Briefings in bioinformatics · 2026Article
- Article
- Shared genetic architecture of psychiatric disorders and ocular diseases: Evidence from genome-wide analyses.IBRO neuroscience reports · 2026Article
- GWAS of extended prescription analgesic use identifies genetic loci in chronic pain.Nature communications · 2026Article
- Representation in genetic studies affects inference about genetic architecture.bioRxiv : the preprint server for biology · 2026Article
- Shared genetic and neuroimmune architecture links type 1 diabetes with neurocognitive traits.Nature communications · 2026Article
- Empowering genetic discoveries and cardiovascular risk assessment by predicting electrocardiograms from genotype.NPJ digital medicine · 2026Article
- Phenome-wide screening of the putative causal determinants of bipolar affective disorder using genetic data.PloS one · 2026Article
- Insomnia Increases Susceptibility to Sciatica: Evidence from Mendelian Randomization and Integrative Genetic Analyses.Journal of pain research · 2026Article
- Facial skin aging is a potential osteoporosis-associated marker: evidence from both observational and genetic studies.Journal of bone and mineral metabolism · 2026Article
- Sex Differences in the role of Additive Genetic Variants in Autism: A Systematic Review.medRxiv : the preprint server for health sciences · 2025Article
- Genome-annotated bivariate analysis of shared heritability in autoimmune hepatitis with psychiatric comorbidity.Scientific reports · 2025Article
- Utilising genomic association data for causal inference in anorexia nervosa.Mammalian genome : official journal of the International Mammalian Genome Society · 2025Review
- Computationally efficient methods for estimating phenome-wide coheritability of multi-type phenotypes using biobank data.Communications biology · 2025Article
64 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e., linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual sample and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on ∼150,000 individuals give a higher accuracy than LDSC estimates based on ∼400,000 individuals (from combined meta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-datasets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if sample sizes are lesser.
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