The SDSS Quasar Survey: Quasar Luminosity Function from Data Release Three

We determine the number counts and z=0-5 luminosity function for a well-defined, homogeneous sample of quasars from the Sloan Digital Sky Survey (SDSS). We conservatively define the most uniform statistical sample possible, consisting of 15,343 quasars within an effective area of 1622 deg^2 that was derived from a parent sample of 46,420 spectroscopically confirmed broad-line quasars in the 5282 deg^2 of imaging data from SDSS Data Release Three. The sample extends from i=15 to i=19.1 at z3. The number counts and luminosity function agree well with the results of the 2dF QSO Survey, but the SDSS data probe to much higher redshifts than does the 2dF sample. The number density of luminous quasars peaks between redshifts 2 and 3, although uncertainties in the selection function in this range do not allow us to determine the peak redshift more precisely. Our best fit model has a flatter bright end slope at high redshift than at low redshift. For z<2.4 the data are best fit by a redshift-independent slope of beta = -3.1 (Phi(L) propto L^beta). Above z=2.4 the slope flattens with redshift to beta=-2.37 at z=5. This slope change, which is significant at a >5-sigma level, must be accounted for in models of the evolution of accretion onto supermassive black holes.Comment: 57 pages, 21 figures (9 color); minor changes to reflect the version accepted by AJ; higher resolution version available at ftp://ftp.astro.princeton.edu/gtr/dr3qlf/Feb1306

Type 2 Diabetes Modifies the association of Cad Genomic Risk Variants With Subclinical atherosclerosis

BACKGROUND: Individuals with type 2 diabetes (T2D) have an increased risk of coronary artery disease (CAD), but questions remain about the underlying pathology. Identifying which CAD loci are modified by T2D in the development of subclinical atherosclerosis (coronary artery calcification [CAC], carotid intima-media thickness, or carotid plaque) may improve our understanding of the mechanisms leading to the increased CAD in T2D. METHODS: We compared the common and rare variant associations of known CAD loci from the literature on CAC, carotid intima-media thickness, and carotid plaque in up to 29 670 participants, including up to 24 157 normoglycemic controls and 5513 T2D cases leveraging whole-genome sequencing data from the Trans-Omics for Precision Medicine program. We included first-order T2D interaction terms in each model to determine whether CAD loci were modified by T2D. The genetic main and interaction effects were assessed using a joint test to determine whether a CAD variant, or gene-based rare variant set, was associated with the respective subclinical atherosclerosis measures and then further determined whether these loci had a significant interaction test. RESULTS: Using a Bonferroni-corrected significance threshold of CONCLUSIONS: These results highlight T2D as an important modifier of rare variant associations in CAD loci with CAC

A Framework For Detecting Noncoding Rare-Variant associations of Large-Scale Whole-Genome Sequencing Studies

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 toPMed samples. We also analyze five non-lipid toPMed traits

Trans-ethnic Meta-analysis and Functional Annotation Illuminates the Genetic Architecture of Fasting Glucose and Insulin

Knowledge of the genetic basis of the type 2 diabetes (T2D)-related quantitative traits fasting glucose (FG) and insulin (FI) in African ancestry (AA) individuals has been limited. In non-diabetic subjects of AA (n = 20,209) and European ancestry (EA; n = 57,292), we performed trans-ethnic (AA+EA) fine-mapping of 54 established EA FG or FI loci with detailed functional annotation, assessed their relevance in AA individuals, and sought previously undescribed loci through trans-ethnic (AA+EA) meta-analysis. We narrowed credible sets of variants driving association signals for 22/54 EA-associated loci; 18/22 credible sets overlapped with active islet-specific enhancers or transcription factor (TF) binding sites, and 21/22 contained at least one TF motif. Of the 54 EA-associated loci, 23 were shared between EA and AA. Replication with an additional 10,096 AA individuals identified two previously undescribed FI loci, chrX FAM133A (rs213676) and chr5 PELO (rs6450057). Trans-ethnic analyses with regulatory annotation illuminate the genetic architecture of glycemic traits and suggest gene regulation as a target to advance precision medicine for T2D. Our approach to utilize state-of-the-art functional annotation and implement trans-ethnic association analysis for discovery and fine-mapping offers a framework for further follow-up and characterization of GWAS signals of complex trait loc

Genetic diversity fuels gene discovery for tobacco and alcohol use

Tobacco and alcohol use are heritable behaviours associated with 15% and 5.3% of worldwide deaths, respectively, due largely to broad increased risk for disease and injury(1-4). These substances are used across the globe, yet genome-wide association studies have focused largely on individuals of European ancestries(5). Here we leveraged global genetic diversity across 3.4 million individuals from four major clines of global ancestry (approximately 21% non-European) to power the discovery and fine-mapping of genomic loci associated with tobacco and alcohol use, to inform function of these loci via ancestry-aware transcriptome-wide association studies, and to evaluate the genetic architecture and predictive power of polygenic risk within and across populations. We found that increases in sample size and genetic diversity improved locus identification and fine-mapping resolution, and that a large majority of the 3,823 associated variants (from 2,143 loci) showed consistent effect sizes across ancestry dimensions. However, polygenic risk scores developed in one ancestry performed poorly in others, highlighting the continued need to increase sample sizes of diverse ancestries to realize any potential benefit of polygenic prediction.Peer reviewe