Expressive

rs1125 - LATS1

Magnitude 2.2 · 2 studies on file

Reported associations

  • Large-scale machine-learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology - Unknown journal (n.d.) · Unknown authors · PubMed 34077760

    ABSTRACT: Summary Genome-wide association studies (GWASs) require accurate cohort phenotyping, but expert labeling can be costly, time intensive, and variable. Here, we develop a machine learning (ML) model to predict glaucomatous optic nerve head features from color fundus photographs. We used the model to predict vertical cup-to-disc ratio (VCDR), a diagnostic parameter and cardinal endophenotype for glaucoma, in 65,680 Europeans in the UK Biobank (UKB). A GWAS of ML-based VCDR identified 299 independent genome-wide significant (GWS; p ≤ 5 × 10−8) hits in 156 loci. The ML-based GWAS replicated 62 of 65 GWS loci from a recent VCDR GWAS in the UKB for which two ophthalmologists manually labeled images for 67,040 Europeans. The ML-based GWAS also identified 93 novel loci, significantl

  • Genome-wide association study identifies glutamate ionotropic receptor GRIA4 as a risk gene for comorbid nicotine dependence and major depression - Unknown journal (n.d.) · Unknown authors · PubMed 30287806

    ABSTRACT: Smoking and major depression frequently co-occur, at least in part due to shared genetic risk. However, the nature of the shared genetic basis is poorly understood. To detect genetic risk variants for comorbid nicotine dependence (ND) and major depression (MD), we conducted genome-wide association study (GWAS) in two samples of African-American participants (Yale-Penn 1 and 2) using linear mixed model, followed by meta-analysis. 3724 nicotine-exposed subjects were analyzed: 2596 from Yale-Penn-1 and 1128 from Yale-Penn-2. Continuous measures (Fagerström Test for Nicotine Dependence (FTND) scores and DSM-IV MD criteria) rather than disorder status were used to maximize the power of the GWAS. Genotypes were ascertained using the Illumina HumanOmni1-Quad array (Yale-Penn-1 sample)

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