rs11805391 - MICOS10

Magnitude 2.2 · 3 studies on file

Reported associations

• Combining cross-sectional and longitudinal genomic approaches to identify determinants of cognitive and physical decline - Unknown journal (n.d.) · Unknown authors · PubMed 40374629

  ABSTRACT: Large-scale genomic studies focusing on the genetic contribution to human aging have mostly relied on cross-sectional data. With the release of longitudinally curated aging phenotypes by the UK Biobank (UKBB), it is now possible to study aging over time at genome-wide scale. In this work, we evaluated the suitability of competing models of change in realistic simulation settings, performed genome-wide association scans on simulation-validated measures of age-related deweekcline, and followed up with LD-score regression and Mendelian Randomization (MR) analyses. Focusing on global cognitive and physical function, we observed marked differences between baseline function (θ) and accelerated decline (Δ). Both outcomes showed distinct heritability levels (e.g., 31.38% versus 3.15%

• 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

• Genome-wide association studies of thyroid-related hormones, dysfunction, and autoimmunity among 85,421 Chinese pregnancies - Unknown journal (n.d.) · Unknown authors · PubMed 39266554

  ABSTRACT: Maintaining normal thyroid function is crucial in pregnancy, yet thyroid dysfunction and the presence of thyroid peroxidase antibodies (TPOAb) affect 0.5% to 18% of pregnant women. Here, we conducted a genome-wide association study (GWAS) of eight thyroid traits, including two thyroid-related hormones, four thyroid dysfunctions, and two thyroid autoimmunity measurements among 85,421 Chinese pregnant women to investigate the genetic basis of thyroid function during pregnancy. Our study identified 176 genetic loci, including 125 previously unknown genome-wide associations. Joint epidemiological and Mendelian randomization analyses revealed significant associations between the gestational thyroid phenotypes and gestational complications, birth outcomes, and later-age health outcomes

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