Recently, two common sequence variants on 9p21, tagged by rs10757278-G and rs10811661-T, were reported to be associated with coronary artery disease (CAD) and type 2 diabetes (T2D), respectively. We proceeded to further investigate the contributions of these variants to arterial diseases and T2D. Here we report that rs10757278-G is associated with, in addition to CAD, abdominal aortic aneurysm (AAA; odds ratio (OR) = 1.31, P = 1.2 x 10(-12)) and intracranial aneurysm (OR = 1.29, P = 2.5 x 10(-6)), but not with T2D. This variant is the first to be described that affects the risk of AAA and intracranial aneurysm in many populations. The association of rs10811661-T to T2D replicates in our samples, but the variant does not associate with any of the five arterial diseases examined. These findings extend our insight into the role of the sequence variant tagged by rs10757278-G and show that it is not confined to atherosclerotic diseases.

Summary Height is a highly heritable, classic polygenic trait with ∼700 common associated variants identified so far through genome-wide association studies. Here, we report 83 height-associated coding variants with lower minor allele frequencies (range of 0.1-4.8%) and effects of up to 2 cm/allele (e.g. in IHH, STC2, AR and CRISPLD2), >10 times the average effect of common variants. In functional follow-up studies, rare height-increasing alleles of STC2 (+1-2 cm/allele) compromised proteolytic inhibition of PAPP-A and increased cleavage of IGFBP-4 in vitro, resulting in higher bioavailability of insulin-like growth factors. These 83 height-associated variants overlap genes mutated in monogenic growth disorders and highlight new biological candidates (e.g. ADAMTS3, IL11RA, NOX4) and pathways (e.g. proteoglycan/glycosaminoglycan synthesis) involved in growth. Our results demonstrate that sufficiently large sample sizes can uncover rare and low-frequency variants of moderate to large effect associated with polygenic human phenotypes, and that these variants implicate relevant genes and pathways.

The Electronic Medical Records and Genomics (eMERGE) Network is a National Human Genome Research Institute (NHGRI)-funded consortium engaged in the development of methods and best-practices for utilizing the Electronic Medical Record (EMR) as a tool for genomic research. Now in its sixth year, its second funding cycle and comprising nine research groups and a coordinating center, the network has played a major role in validating the concept that clinical data derived from EMRs can be used successfully for genomic research. Current work is advancing knowledge in multiple disciplines at the intersection of genomics and healthcare informatics, particularly electronic phenotyping, genome-wide association studies, genomic medicine implementation and the ethical and regulatory issues associated with genomics research and returning results to study participants. Here we describe the evolution, accomplishments, opportunities and challenges of the network since its inception as a five-group consortium focused on genotype-phenotype associations for genomic discovery to its current form as a nine-group consortium pivoting towards implementation of genomic medicine.

More than 70 mutations in the two structural genes for type I procollagen (COL1A1 and COL1A2) have been found in probands with osteogenesis imperfecta, a heritable disease of children characterized by fragility of bone and other tissues rich in type I collagen. The mutations include deletions, insertions, RNA splicing mutations, and single-base substitutions that convert a codon for glycine to a codon for an amino acid with a bulkier side chain. With a few exceptions, the most severe phenotypes of the disease are explained largely by synthesis of structurally defective pro alpha chains of type I procollagen that either interfere with the folding of the triple helix or with self-assembly of collagen into fibrils. The results emphasize the extent to which the zipperlike folding of the collagen triple helix and the self-assembly of collagen fibrils depend on the principle of nucleated growth whereby a few subunits form a nucleus and the nucleus is then propagated to generate a large structure with a precisely defined architecture. The principle of nucleated growth is a highly efficient mechanism for the assembly of large structures, but biological systems that depend extensively on nucleated growth are highly vulnerable to mutations that cause synthesis of structurally abnormal but partially functional subunits. Recently, several mutations in three other collagen genes (COL2A1, COL3A1, and COL4A5) have been found in probands with genetic diseases involving tissues rich in these collagens. Most of the probands have rare genetic diseases but a few appear to have phenotypes that are difficult to distinguish from more common disorders such as osteoarthritis, osteoporosis, and aortic aneurysms. Therefore, the results suggest that mutations in procollagen genes may cause a wide spectrum of both rare and common human diseases.

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Importance The causal direction and magnitude of the association between telomere length and incidence of cancer and non-neoplastic diseases is uncertain owing to the susceptibility of observational studies to confounding and reverse causation. Objective To conduct a Mendelian randomization study, using germline genetic variants as instrumental variables, to appraise the causal relevance of telomere length for risk of cancer and non-neoplastic diseases. Data Sources Genomewide association studies (GWAS) published up to January 15, 2015. Study Selection GWAS of noncommunicable diseases that assayed germline genetic variation and did not select cohort or control participants on the basis of preexisting diseases. Of 163 GWAS of noncommunicable diseases identified, summary data from 103 were available. Data Extraction and Synthesis Summary association statistics for single nucleotide polymorphisms (SNPs) that are strongly associated with telomere length in the general population. Main Outcomes and Measures Odds ratios (ORs) and 95% confidence intervals (CIs) for disease per standard deviation (SD) higher telomere length due to germline genetic variation. Results Summary data were available for 35 cancers and 48 non-neoplastic diseases, corresponding to 420 081 cases (median cases, 2526 per disease) and 1 093 105 controls (median, 6789 per disease). Increased telomere length due to germline genetic variation was generally associated with increased risk for site-specific cancers. The strongest associations (ORs [95% CIs] per 1-SD change in genetically increased telomere length) were observed for glioma, 5.27 (3.15-8.81); serous low-malignant-potential ovarian cancer, 4.35 (2.39-7.94); lung adenocarcinoma, 3.19 (2.40-4.22); neuroblastoma, 2.98 (1.92-4.62); bladder cancer, 2.19 (1.32-3.66); melanoma, 1.87 (1.55-2.26); testicular cancer, 1.76 (1.02-3.04); kidney cancer, 1.55 (1.08-2.23); and endometrial cancer, 1.31 (1.07-1.61). Associations were stronger for rarer cancers and at tissue sites with lower rates of stem cell division. There was generally little evidence of association between genetically increased telomere length and risk of psychiatric, autoimmune, inflammatory, diabetic, and other non-neoplastic diseases, except for coronary heart disease (OR, 0.78 [95% CI, 0.67-0.90]), abdominal aortic aneurysm (OR, 0.63 [95% CI, 0.49-0.81]), celiac disease (OR, 0.42 [95% CI, 0.28-0.61]) and interstitial lung disease (OR, 0.09 [95% CI, 0.05-0.15]). Conclusions and Relevance It is likely that longer telomeres increase risk for several cancers but reduce risk for some non-neoplastic diseases, including cardiovascular diseases.

Background: Abdominal aortic aneurysms are a common disorder with an incompletely understood etiology. We used Illumina and Affymetrix microarray platforms to generate global gene expression profiles for both aneurysmal (AAA) and non-aneurysmal abdominal aorta, and identified genes that were significantly differentially expressed between cases and controls.

Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, non-coding variants from which pinpointing causal genes remains challenging. Here, we combined data from 718,734 individuals to discover rare and low-frequency (MAF<5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which eight in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2, ZNF169) newly implicated in human obesity, two (MC4R, KSR2) previously observed in extreme obesity, and two variants in GIPR. Effect sizes of rare variants are ~10 times larger than of common variants, with the largest effect observed in carriers of an MC4R stop-codon (p.Tyr35Ter, MAF=0.01%), weighing ~7kg more than non-carriers. Pathway analyses confirmed enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically-supported therapeutic targets to treat obesity.