Expressive

rs113166754 - CDC5L - SUPT3H

Magnitude 2.2 · 4 studies on file

Reported associations

  • A longitudinal genome-wide association study of bone mineral density mean and variability in the UK Biobank. - Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA (2023) · He D, Liu H, Wei W, Zhao Y, Cai Q, Shi S, Chu X, Qin X, Zhang N, Xu P, Zhang F · PubMed 37500982

    Bone mineral density (BMD) is an essential predictor of osteoporosis and fracture. We conducted a genome-wide trajectory analysis of BMD and analyzed the BMD change. This study aimed to identify the genetic architecture and potential biomarkers of BMD. Our analysis included 141,261 white participants from the UK Biobank with heel BMD phenotype data. We used a genome-wide trajectory analysis tool, TrajGWAS, to conduct a genome-wide association study (GWAS) of BMD. Then, we validated our findings in previously reported BMD genetic associations and performed replication analysis in the Asian participants. Finally, gene-set enrichment analysis (GSEA) of the identified candidate genes was conducted using the FUMA platform. A total of 52 genes associated with BMD trajectory mean were identified,

  • Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis - Unknown journal (n.d.) · Unknown authors · PubMed 28869591

    ABSTRACT: Osteoporosis is a common disease diagnosed primarily by measurement of bone mineral density (BMD). We undertook a genome-wide association study in 142,487 individuals from the UK Biobank to identify loci associated with BMD estimated by quantitative ultrasound of the heel ("eBMD"). We identified 307 conditionally independent SNPs attaining genome-wide significance at 203 loci, explaining approximately 12% of the phenotypic variance. These included 153 novel loci, and several rare variants with large effect sizes. To investigate underlying mechanisms we undertook: 1) bioinformatic, functional genomic annotation and human osteoblast expression studies; 2) gene function prediction; 3) skeletal phenotyping of 120 knockout mice with deletions of genes adjacent to lead independent

  • Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging - Unknown journal (n.d.) · Unknown authors · PubMed 38580839

    ABSTRACT: Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants' T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes

  • Identification of 613 new loci associated with heel bone mineral density and a polygenic risk score for bone mineral density, osteoporosis and fracture - Unknown journal (n.d.) · Unknown authors · PubMed 30048462

    ABSTRACT: Low bone mineral density (BMD) leads to osteoporosis, and is a risk factor for bone fractures, including stress fractures. Using data from UK Biobank, a genome-wide association study identified 1,362 independent SNPs that clustered into 899 loci of which 613 are new. These data were used to train a genetic algorithm using 22,886 SNPs as predictors and showing a correlation with heel bone mineral density of 0.415. Combining this genetic algorithm with height, weight, age and sex resulted in a correlation with heel bone mineral density of 0.496. Individuals with low scores (2.2% of total) showed a change in BMD of -1.16 T-score units, an increase in risk for osteoporosis of 17.4 fold and an increase in risk for fracture of 1.87 fold. Genetic predictors could assist in the identific

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