Generalization and fine mapping of red blood cell trait genetic associations to multi-ethnic populations: The PAGE study

Red blood cell (RBC) traits provide insight into a wide range of physiological states and exhibit moderate to high heritability, making them excellent candidates for genetic studies to inform underlying biologic mechanisms. Previous RBC trait genome-wide association studies were performed primarily in European- or Asian-ancestry populations, missing opportunities to inform understanding of RBC genetic architecture in diverse populations and reduce intervals surrounding putative functional SNPs through fine-mapping. Here, we report the first fine-mapping of 6 correlated (Pearson's r range: |0.04-0.92|) RBC traits in up to 19 036 African Americans and 19 562 Hispanic/Latino participants of the Population Architecture using Genomics and Epidemiology consortium. Trans-ethnic meta-analysis of race/ethnic- and study-specific estimates for approximately 11 000 SNPs flanking 13 previously identified association signals as well as 150 000 additional array-wide SNPs was performed using inverse-variance meta-analysis after adjusting for study and clinical covariates. Approximately half of previously reported index SNP-RBC trait associations generalized to the trans-ethnic study population (p < 1.7 × 10 −4 ); previously unreported independent association signals within the ABO region reinforce the potential for multiple functional variants affecting the same locus. Trans-ethnic fine-mapping did not reveal additional signals at the HFE locus independent of the known functional variants. Finally, we identified a potential novel association in the Hispanic/Latino study population at the HECTD4/RPL6 locus for RBC count (p = 1.9 × 10 −7 ). The identification of a previously unknown association, generalization of a large proportion of known association signals, and refinement of known association signals all exemplify the benefits of genetic studies in diverse populations. © 2018 Wiley Periodicals, Inc

Genetics of coronary artery calcification among African Americans, a meta-analysis

Background: Coronary heart disease (CHD) is the major cause of death in the United States. Coronary artery calcification (CAC) scores are independent predictors of CHD. African Americans (AA) have higher rates of CHD but are less well-studied in genomic studies. We assembled the largest AA data resource currently available with measured CAC to identify associated genetic variants.Methods: We analyzed log transformed CAC quantity (ln(CAC + 1)), for association with ~2.5 million single nucleotide polymorphisms (SNPs) and performed an inverse-variance weighted meta-analysis on results for 5,823 AA from 8 studies. Heritability was calculated using family studies. The most significant SNPs among AAs were evaluated in European Ancestry (EA) CAC data; conversely, the significance of published SNPs for CAC/CHD in EA was queried within our AA meta-analysis.Results: Heritability of CAC was lower in AA (~30%) than previously reported for EA (~50%). No SNP reached genome wide significance (p < 5E-08). Of 67 SNPs with p < 1E-05 in AA there was no evidence of association in EA CAC data. Four SNPs in regions previously implicated in CAC/CHD (at 9p21 and PHACTR1) in EA reached

Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations

Most genome-wide association and fine-mapping studies to date have been conducted in individuals of European descent, and genetic studies of populations of Hispanic/Latino and African ancestry are limited. In addition, these populations have more complex linkage disequilibrium structure. In order to better define the genetic architecture of these understudied populations, we leveraged >100,000 phased sequences available from deep-coverage whole genome sequencing through the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) program to impute genotypes into admixed African and Hispanic/Latino samples with genome-wide genotyping array data. We demonstrated that using TOPMed sequencing data as the imputation reference panel improves genotype imputation quality in these populations, which subsequently enhanced gene-mapping power for complex traits. For rare variants with minor allele frequency (MAF) 86%. Subsequent association analyses of TOPMed reference panel-imputed genotype data with hematological traits (hemoglobin (HGB), hematocrit (HCT), and white blood cell count (WBC)) in ~21,600 African-ancestry and ~21,700 Hispanic/Latino individuals identified associations with two rare variants in the HBB gene (rs33930165 with higher WBC [p = 8.8x10-15] in African populations, rs11549407 with lower HGB [p = 1.5x10-12] and HCT [p = 8.8x10-10] in Hispanics/Latinos). By comparison, neither variant would have been genome-wide significant if either 1000 Genomes Project Phase 3 or Haplotype Reference Consortium reference panels had been used for imputation. Our findings highlight the utility of the TOPMed imputation reference panel for identification of novel rare variant associations not previously detected in similarly sized genome-wide studies of under-represented African and Hispanic/Latino populations

Design of phase measurement system using Hybrid Dual D-FIFO-FF synchronizer and PWM based duty cycle computation

Phase measurement system (PMS)s are playing the major role in real time communication applications. The digital PMS measures the exact value of phase compare to the analog PMS devices. However, the conventional digital PMSs failed to measure the accurate phase value and consumed the higher hardware resources like area, delay and power. Therefore, this work is focused on implementation of Hybrid Digital-PMS (HD-PMS) module for efficient phase value estimation. Initially, Hybrid Dual Data-First-In-First-Out-Flip Flop (D-FIFO-FF) synchronizer is introduced to improve the synthesis the data inputs, which synchronizes the data inputs with variable clock frequencies. Then, Phase Detector (PD) is used to identify the phase difference between data input1, data input 2. The PD is developed using XOR logical operation, which is high speed and area efficient. Then, data input1, data input 2 frequencies are changed by modifying the duty cycles through Pulse Width Modulation (PWM) controller. Here, PWM is used to change the duty cycles of data inputs using phase difference generated from PD. Finally, Phase Value Computation (PVC) module is introduced to estimate the digital phase levels using arithmetical operations using duty cycle levels. The simulations conducted using XILINX-ISE 14.7 reveled that, the proposed HD-PMS consumed lower hardware resources like look-up-tables (LUT), LUT-FFs, slice registers, path delays and power consumptions as compared to other PMS approaches

Regression verification using impact summaries

Abstract. Regression verification techniques are used to prove equivalence of closely related program versions. Existing regression verification techniques leverage the similarities between program versions to help improve analysis scalability by using abstraction and decomposition techniques. These techniques are sound but not complete. In this work, we propose an alternative technique to improve scalability of regression verification that leverages change impact information to partition program execution behaviors. Program behaviors in each version are partitioned into (a) behaviors impacted by the changes and (b) behaviors not impacted (unimpacted) by the changes. Our approach uses a combination of static analysis and symbolic execution to generate summaries of program behaviors impacted by the differences. We show in this work that checking equivalence of behaviors in two program versions reduces to checking equivalence of just the impacted behaviors. We prove that our approach is both sound and complete for sequential programs, with respect to the depth bound of symbolic execution; furthermore, our approach can be used with existing approaches to better leverage the similarities between program versions and improve analysis scalability. We evaluate our technique on a set of sequential C artifacts and present preliminary results.