Expressive

rs11621975 - LINC02320

Magnitude 4.5 · 2 studies on file

Reported associations

  • Genome-wide association study evaluating single-nucleotide polymorphisms and outcomes in patients with advanced stage serous ovarian or primary peritoneal cancer: An NRG Oncology/Gynecologic Oncology Group study☆,☆☆ - Unknown journal (n.d.) · Unknown authors · PubMed 28935272

    ABSTRACT: Objective This study evaluated single nucleotide polymorphisms (SNPs) associated with progression free (PFS) and overall survival (OS) in patients with advanced stage serous EOC. Methods Patients enrolled in GOG-172 and 182 who provided specimens for translational research and consent were included. Germline DNA was evaluated with the Illumina's HumanOMNI1-Quad beadchips and scanned using Illumina's iScan optical imaging system. SNPs with allele frequency > 0.05 and genotyping rate > 0.98 were included. Analysis of SNPs for PFS and OS was done using Cox regression. Statistical significance was determined using Bonferroni corrected p-values with genomic control adjustment. Results The initial GWAS analysis included 1,124,677 markers in 396 patients. To obtain the final data se

  • A saturated map of common genetic variants associated with human height - Unknown journal (n.d.) · Unknown authors · PubMed 36224396

    ABSTRACT: Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes. Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation

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