Literature and Music Reviews

Panel Chair: Lubna Javeed, Collin Colleg

Informed Conditioning on Clinical Covariates Increases Power in Case-Control Association Studies

Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low–BMI cases are larger than those estimated from high–BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1×10−9). The improvement varied across diseases with a 16% median increase in χ2 test statistics and a commensurate increase in power. This suggests that applying our method to existing and future association studies of these diseases may identify novel disease loci

A Scandcleft randomised trials of primary surgery for unilateral cleft lip and palate:1. Planning and management

<p><b>Background and aims:</b> Longstanding uncertainty surrounds the selection of surgical protocols for the closure of unilateral cleft lip and palate, and randomised trials have only rarely been performed. This paper is an introduction to three randomised trials of primary surgery for children born with complete unilateral cleft lip and palate (UCLP). It presents the protocol developed for the trials in CONSORT format, and describes the management structure that was developed to achieve the long-term engagement and commitment required to complete the project.</p> <p><b>Method:</b> Ten established national or regional cleft centres participated. Lip and soft palate closure at 3–4 months, and hard palate closure at 12 months served as a common method in each trial. Trial 1 compared this with hard palate closure at 36 months. Trial 2 compared it with lip closure at 3–4 months and hard and soft palate closure at 12 months. Trial 3 compared it with lip and hard palate closure at 3–4 months and soft palate closure at 12 months. The primary outcomes were speech and dentofacial development, with a series of perioperative and longer-term secondary outcomes.</p> <p><b>Results:</b> Recruitment of 448 infants took place over a 9-year period, with 99.8% subsequent retention at 5 years.</p> <p><b>Conclusion:</b> The series of reports that follow this introductory paper include comparisons at age 5 of surgical outcomes, speech outcomes, measures of dentofacial development and appearance, and parental satisfaction. The outcomes recorded and the numbers analysed for each outcome and time point are described in the series.</p> <p><b>Trial registration:</b> ISRCTN29932826.</p

Dual Host-Virus Arms Races Shape an Essential Housekeeping Protein

Ann Demogines is with UT Austin, Jonathan Abraham is with Harvard Medical School, Hyeryun Choe is with Harvard Medical School, Michael Farzan is with Harvard Medical School, Sara L. Sawyer is with UT Austin.Transferrin Receptor (TfR1) is the cell-surface receptor that regulates iron uptake into cells, a process that is fundamental to life. However, TfR1 also facilitates the cellular entry of multiple mammalian viruses. We use evolutionary and functional analyses of TfR1 in the rodent clade, where two families of viruses bind this receptor, to mechanistically dissect how essential housekeeping genes like TFR1 successfully balance the opposing selective pressures exerted by host and virus. We find that while the sequence of rodent TfR1 is generally conserved, a small set of TfR1 residue positions has evolved rapidly over the speciation of rodents. Remarkably, all of these residues correspond to the two virus binding surfaces of TfR1. We show that naturally occurring mutations at these positions block virus entry while simultaneously preserving iron-uptake functionalities, both in rodent and human TfR1. Thus, by constantly replacing the amino acids encoded at just a few residue positions, TFR1 divorces adaptation to ever-changing viruses from preservation of key cellular functions. These dynamics have driven genetic divergence at the TFR1 locus that now enforces species-specific barriers to virus transmission, limiting both the cross-species and zoonotic transmission of these viruses.This work was supported by grants from the Norman Hackerman Advanced Research Program (003658-0250-2009 to SLS), the National Institutes of Health (R01-GM-093086 to SLS and R01-AI-74871 to HC), the New England Regional Center for Excellence/Biodefense and Emerging Infectious Disease (U54 AI057159 to HC and MF), and the New England Primate Research Center (RR000168 to MF). AD is supported by an American Cancer Society Postdoctoral Fellowship. JA is a Howard Hughes Medical Institute Gilliam Fellow. SLS holds a Career Award in the Biomedical Sciences from the Burroughs Wellcome Fund and is an Alfred P. Sloan Research Fellow in Computational and Evolutionary Molecular Biology. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Biological Sciences, School o

Summary statistics across all datasets.

<p>The sum of each of the test statistics across all of the SNPs in each of the diseases. LTPub vs LogR is the % increase of LTPub compared to LogR. It has a median value of 16%. Type 2 diabetes (T2D), prostate cancer (PC), lung cancer (LC), breast cancer (BC), rheumatoid arthritis (RA), end-stage kidney disease (ESKD), and age-related macular degeneration (AMD).</p

Average χ² statistics for LT versus other approaches in simulated data.

<p>For each statistic we display average results across 1,000,000 simulations, for various effect sizes <i>γ</i>. All statistics are χ²(1 dof). Logistic regression with an interaction term (G+GxE) values been converted from χ²(2 dof) to the equivalent χ²(1 dof) value. At an effect size of 0 all statistics give the expected value under the null. OR LBMI is the odds ratio computed from cases with BMI = 24. OR HBMI is the odds ratio for cases with BMI = 35.</p

Inferred covariates and effect sizes on the liability scale.

<p>LT model is the liability threshold model for each disease with parameters estimated using the LTPub method. For diseases with multiple covariates, models with all covariates and each covariate separately are given. %Variance Explained is the fraction of variance explained on the liability scale in the study data for each of the covariates in each of the diseases when all covariates are used in the model, and is specific to the distribution of covariates in each particular study. BMI30 is a binary variable, which is 1 if an individual's BMI is greater than 30 and 0 otherwise. Type 2 diabetes (T2D), prostate cancer (PC), lung cancer (LC), breast cancer (BC), rheumatoid arthritis (RA), end-stage kidney disease (ESKD), and age-related macular degeneration (AMD).</p