Bayesian linear mixed models with polygenic effects

We considered Bayesian estimation of polygenic effects, in particular heritability in relation to a class of linear mixed models implemented in R. Our approach is applicable to both family-based and population-based studies in human genetics with which a genetic relationship matrix can be derived either from family structure or genome-wide data. Using a simulated and a real data, we demonstrate our implementation of the models in the generic statistical software systems JAGS and Stan as well as several R packages. In doing so, we have not only provided facilities in R linking standalone programs such as GCTA and other packages in R but also addressed some technical issues in the analysis. Our experience with a host of general and special software systems will facilitate investigation into more complex models for both human and nonhuman genetics

Mixed modeling with whole genome data

Objective. We consider the need for a modeling framework for related individuals and various sources of variations. The relationships could either be among relatives in families or among unrelated individuals in a general population with cryptic relatedness; both could be refined or derived with whole genome data. As with variations they can include oliogogenes, polygenes, single nucleotide polymorphism (SNP), and covariates.Methods. We describe mixed models as a coherent theoretical framework to accommodate correlations for various types of outcomes in relation to many sources of variations. The framework also extends to consortium meta-analysis involving both population-based and family-based studies.Results. Through examples we show that the framework can be furnished with general statistical packages whose great advantage lies in simplicity and exibility to study both genetic and environmental effects. Areas which require further work are also indicated.Conclusion. Mixed models will play an important role in practical analysis of data on both families and unrelated individuals when whole genome information is available.Peer Reviewe

Alu pair exclusions in the human genome

<p>Abstract</p> <p>Background</p> <p>The human genome contains approximately one million <it>Alu </it>elements which comprise more than 10% of human DNA by mass. <it>Alu </it>elements possess direction, and are distributed almost equally in positive and negative strand orientations throughout the genome. Previously, it has been shown that closely spaced <it>Alu </it>pairs in opposing orientation (inverted pairs) are found less frequently than <it>Alu </it>pairs having the same orientation (direct pairs). However, this imbalance has only been investigated for <it>Alu </it>pairs separated by 650 or fewer base pairs (bp) in a study conducted prior to the completion of the draft human genome sequence.</p> <p>Results</p> <p>We performed a comprehensive analysis of all (> 800,000) full-length <it>Alu </it>elements in the human genome. This large sample size permits detection of small differences in the ratio between inverted and direct <it>Alu </it>pairs (I:D). We have discovered a significant depression in the full-length <it>Alu </it>pair I:D ratio that extends to repeat pairs separated by ≤ 350,000 bp. Within this imbalance bubble (those <it>Alu </it>pairs separated by ≤ 350,000 bp), direct pairs outnumber inverted pairs. Using PCR, we experimentally verified several examples of inverted <it>Alu </it>pair exclusions that were caused by deletions.</p> <p>Conclusions</p> <p>Over 50 million full-length <it>Alu </it>pairs reside within the I:D imbalance bubble. Their collective impact may represent one source of <it>Alu </it>element-related human genomic instability that has not been previously characterized.</p

Common Variants at 10 Genomic Loci Influence Hemoglobin A(1C) Levels via Glycemic and Nonglycemic Pathways

OBJECTIVE Glycated hemoglobin (HbA1c), used to monitor and diagnose diabetes, is influenced by average glycemia over a 2- to 3-month period. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin could also be associated with increased levels of HbA1c. We aimed to identify such genetic factors and investigate the extent to which they influence diabetes classification based on HbA1c levels. RESEARCH DESIGN AND METHODS We studied associations with HbA1c in up to 46,368 nondiabetic adults of European descent from 23 genome-wide association studies (GWAS) and 8 cohorts with de novo genotyped single nucleotide polymorphisms (SNPs). We combined studies using inverse-variance meta-analysis and tested mediation by glycemia using conditional analyses. We estimated the global effect of HbA1c loci using a multilocus risk score, and used net reclassification to estimate genetic effects on diabetes screening. RESULTS Ten loci reached genome-wide significant association with HbA1c, including six new loci near FN3K (lead SNP/P value, rs1046896/P = 1.6 × 10−26), HFE (rs1800562/P = 2.6 × 10−20), TMPRSS6 (rs855791/P = 2.7 × 10−14), ANK1 (rs4737009/P = 6.1 × 10−12), SPTA1 (rs2779116/P = 2.8 × 10−9) and ATP11A/TUBGCP3 (rs7998202/P = 5.2 × 10−9), and four known HbA1c loci: HK1 (rs16926246/P = 3.1 × 10−54), MTNR1B (rs1387153/P = 4.0 × 10−11), GCK (rs1799884/P = 1.5 × 10−20) and G6PC2/ABCB11 (rs552976/P = 8.2 × 10−18). We show that associations with HbA1c are partly a function of hyperglycemia associated with 3 of the 10 loci (GCK, G6PC2 and MTNR1B). The seven nonglycemic loci accounted for a 0.19 (% HbA1c) difference between the extreme 10% tails of the risk score, and would reclassify ∼2% of a general white population screened for diabetes with HbA1c. CONCLUSIONS GWAS identified 10 genetic loci reproducibly associated with HbA1c. Six are novel and seven map to loci where rarer variants cause hereditary anemias and iron storage disorders. Common variants at these loci likely influence HbA1c levels via erythrocyte biology, and confer a small but detectable reclassification of diabetes diagnosis by HbA1c

Rheological Characterization of the Bundling Transition in F-Actin Solutions Induced by Methylcellulose

In many in vitro experiments Brownian motion hampers quantitative data analysis. Therefore, additives are widely used to increase the solvent viscosity. For this purpose, methylcellulose (MC) has been proven highly effective as already small concentrations can significantly slow down diffusive processes. Beside this advantage, it has already been reported that high MC concentrations can alter the microstructure of polymer solutions such as filamentous actin. However, it remains to be shown to what extent the mechanical properties of a composite actin/MC gel depend on the MC concentration. In particular, significant alterations might occur even if the microstructure seems unaffected. Indeed, we find that the viscoelastic response of entangled F-actin solutions depends sensitively on the amount of MC added. At concentrations higher than 0.2% (w/v) MC, actin filaments are reorganized into bundles which drastically changes the viscoelastic response. At small MC concentrations the impact of MC is more subtle: the two constituents, actin and MC, contribute in an additive way to the mechanical response of the composite material. As a consequence, the effect of methylcellulose on actin solutions has to be considered very carefully when MC is used in biochemical experiments

Hundreds of variants clustered in genomic loci and biological pathways affect human height

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

High-Throughput Screen for Identifying Small Molecules That Target Fungal Zinc Homeostasis

Resistance to traditional antifungal drugs has increased significantly over the past three decades, making identification of novel antifungal agents and new targets an emerging priority. Based on the extraordinary zinc requirement of several fungal pathogens and their well-established sensitivity to zinc deprivation, we developed an efficient cell-based screen to identify new antifungal drugs that target the zinc homeostasis machinery. The screen is based on the zinc-regulated transcription factor Zap1 of Saccharomyces cerevisiae, which regulates transcription of genes like the high-affinity zinc transporter ZRT1. We generated a genetically modified strain of S. cerevisae that reports intracellular zinc deficiency by placing the coding sequence of green fluorescent protein (GFP) under the control of the Zap1-regulated ZRT1 promoter. After showing that the GFP fluorescence signal correlates with low intracellular zinc concentrations in this strain, a protocol was developed for screening small-molecule libraries for compounds that induce Zap1-dependent GFP expression. Comparison of control compounds and known modulators of metal metabolism from the library reveals a robust screen (Z′ = 0.74) and validates this approach to the discovery of new classes of antifungal compounds that interfere with the intracellular zinc homeostasis. Given that growth of many pathogenic organisms is significantly impaired by zinc limitation; these results identify new types of antifungal drugs that target critical nutrient acquisition pathways

Inhibition of Neuroblastoma Tumor Growth by Targeted Delivery of MicroRNA-34a Using Anti-Disialoganglioside GD2 Coated Nanoparticles

Neuroblastoma is one of the most challenging malignancies of childhood, being associated with the highest death rate in paediatric oncology, underlining the need for novel therapeutic approaches. Typically, patients with high risk disease undergo an initial remission in response to treatment, followed by disease recurrence that has become refractory to further treatment. Here, we demonstrate the first silica nanoparticle-based targeted delivery of a tumor suppressive, pro-apoptotic microRNA, miR-34a, to neuroblastoma tumors in a murine orthotopic xenograft model. These tumors express high levels of the cell surface antigen disialoganglioside GD2 (GD(2)), providing a target for tumor-specific delivery.Nanoparticles encapsulating miR-34a and conjugated to a GD(2) antibody facilitated tumor-specific delivery following systemic administration into tumor bearing mice, resulted in significantly decreased tumor growth, increased apoptosis and a reduction in vascularisation. We further demonstrate a novel, multi-step molecular mechanism by which miR-34a leads to increased levels of the tissue inhibitor metallopeptidase 2 precursor (TIMP2) protein, accounting for the highly reduced vascularisation noted in miR-34a-treated tumors.These novel findings highlight the potential of anti-GD(2)-nanoparticle-mediated targeted delivery of miR-34a for both the treatment of GD(2)-expressing tumors, and as a basic discovery tool for elucidating biological effects of novel miRNAs on tumor growth

Rare and low-frequency coding variants alter human adult height

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 new 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 variants 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.Cancer Research UK, Wellcome Trust A full list of acknowledgments appears in the Supplementary Note. Part of this work was conducted using the UK Biobank resource