Identification of Novel and Rare Variants Associated with Handgrip Strength Using Whole Genome Sequence Data from the NHLBI Trans-Omics in Precision Medicine (TOPMed) Program

Handgrip strength is a widely used measure of muscle strength and a predictor of a range of morbidities including cardiovascular diseases and all-cause mortality. Previous genome-wide association studies of handgrip strength have focused on common variants primarily in persons of European descent. We aimed to identify rare and ancestry-specific genetic variants associated with handgrip strength by conducting whole-genome sequence association analyses using 13,552 participants from six studies representing diverse population groups from the Trans-Omics in Precision Medicine (TOPMed) Program. By leveraging multiple handgrip strength measures performed in study participants over time, we increased our effective sample size by 7-12%. Single-variant analyses identified ten handgrip strength loci among African-Americans: four rare variants, five low-frequency variants, and one common variant. One significant and four suggestive genes were identified associated with handgrip strength when aggregating rare and functional variants; all associations were ancestry-specific. We additionally leveraged the different ancestries available in the UK Biobank to further explore the ancestry-specific association signals from the single-variant association analyses. In conclusion, our study identified 11 new loci associated with handgrip strength with rare and/or ancestry-specific genetic variations, highlighting the added value of whole-genome sequencing in diverse samples. Several of the associations identified using single-variant or aggregate analyses lie in genes with a function relevant to the brain or muscle or were reported to be associated with muscle or age-related traits. Further studies in samples with sequence data and diverse ancestries are needed to confirm these findings

Genetic determinants of telomere length from 109,122 ancestrally diverse whole-genome sequences in TOPMed

Genetic studies on telomere length are important for understanding age-related diseases. Prior GWAS for leukocyte TL have been limited to European and Asian populations. Here, we report the first sequencing-based association study for TL across ancestrally-diverse individuals (European, African, Asian and Hispanic/Latino) from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. We used whole genome sequencing (WGS) of whole blood for variant genotype calling and the bioinformatic estimation of telomere length in n=109,122 individuals. We identified 59 sentinel variants (p-value OBFC1indicated the independent signals colocalized with cell-type specific eQTLs for OBFC1 (STN1). Using a multi-variant gene-based approach, we identified two genes newly implicated in telomere length, DCLRE1B (SNM1B) and PARN. In PheWAS, we demonstrated our TL polygenic trait scores (PTS) were associated with increased risk of cancer-related phenotypes

SBML Level 3: an extensible format for the exchange and reuse of biological models

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution

Evaluating And Improving Stoichiometrically Constrained Models Of Yeast Metabolism For Application To Design Of Metabolic Engineering Strategies

This document presents the results of efforts to apply the Yeast Consensus Reconstruction model of the Saccharomyces cerevisiae metabolic network to develop a metabolic engineering strategy for industrial strain improvement. Following a review of the development of mathematical models of metabolism, it describes an evaluation of the Consensus Reconstruction. We find that the computational reconstruction of this portion of metabolism differs from the biochemistry of this pathway as described in the literature. Our efforts to correct these discrepancies are described in Chapter 4. The updated model improves both the accuracy of the metabolic reconstruction and the prediction of viability and auxotrophy phenotypes, thus demonstrating that literature-based curation is a technique which can be successfully applied to improve the model. Chapter 5 describes an in silico screen for formate-producing yeast mutants. By working to reproduce an in silico screen previously conducted using the iND750 model, we found that the computational prediction of formate-producing yeast mutants is sensitive to implementation details and reaction constraints when using either the iND750 or the Yeast model. Our results suggest that comparative analysis of constraint based models is a useful tool for improving models of the yeast metabolic network. The main text concludes with a summary and discussion of future research opportunities in Chapter 6. MATLAB scripts which enable evaluation of model predictive accuracy and demonstrate model applications such growth simulation and mutant library screening are included as appendices. This work contributes to the ongoing effort to develop systems biotechnology tools which will enable the rational design of new microbial strains, and which may enable broader industrial-scale application of biotechnology. The fields of computational biology and systems biotechnology are young, and abundant opportunity remains to develop and apply this technology to meet human needs

Comparative Analysis of Yeast Metabolic Network Models Highlights Progress, Opportunities for Metabolic Reconstruction

<div><p>We have compared 12 genome-scale models of the <i>Saccharomyces cerevisiae</i> metabolic network published since 2003 to evaluate progress in reconstruction of the yeast metabolic network. We compared the genomic coverage, overlap of annotated metabolites, predictive ability for single gene essentiality with a selection of model parameters, and biomass production predictions in simulated nutrient-limited conditions. We have also compared pairwise gene knockout essentiality predictions for 10 of these models. We found that varying approaches to model scope and annotation reflected the involvement of multiple research groups in model development; that single-gene essentiality predictions were affected by simulated medium, objective function, and the reference list of essential genes; and that predictive ability for single-gene essentiality did not correlate well with predictive ability for our reference list of synthetic lethal gene interactions (R = 0.159). We conclude that the reconstruction of the yeast metabolic network is indeed gradually improving through the iterative process of model development, and there remains great opportunity for advancing our understanding of biology through continued efforts to reconstruct the full biochemical reaction network that constitutes yeast metabolism. Additionally, we suggest that there is opportunity for refining the process of deriving a metabolic model from a metabolic network reconstruction to facilitate mechanistic investigation and discovery. This comparative study lays the groundwork for developing improved tools and formalized methods to quantitatively assess metabolic network reconstructions independently of any particular model application, which will facilitate ongoing efforts to advance our understanding of the relationship between genotype and cellular phenotype.</p></div

Growth simulations demonstrate interplay between network reconstruction and constraints.

<p>A) Optimal biomass flux calculated by flux balance analysis increased linearly with glucose uptake flux for all models when the glucose exchange reaction is the only constrained media component. All model predictions had a 0.8158 correlation with previously reported measured growth rate. B) When glucose and oxygen exchange reactions were both constrained to experimental values, there are high-correlation (black) and low-correlation models (red). C) Restricting flux through a mitochondrial aspartate transport reaction did not affect the predictions for the high correlation models, and improved all remaining correlations to >0.9, with the exception of the Yeast 4 model, which still over-predicted the maximum biomass flux at high glucose:oxygen exchange constraint ratios.</p

Model prediction of single-gene essentiality is sensitive to biomass definition.

<p>Since objective function is a tunable model parameter, we calculated Matthews’ Correlation Coefficients for the sum of all true positive, true negative, false positive and false negative predictions across all conditions using two different objective functions for each model: the biomass definition provided by the model authors, and the biomass function used for the iFF708 model. We found that with the exception of the Yeast 4 model, all model predictions were improved by tuned objective function, independent of refinements to the biochemical network reconstruction. Models are arranged in chronological order across the horizontal axis.</p

Summary statistics of yeast metabolic network models.

<p>General parameters described or used for simulation in this study include: number of genes included as reaction modifiers; number of included genes that are currently annotated by the Saccharomyces Genome Database as “dubious”, or unlikely to encode an expressed protein; number of metabolites; number of dead end metabolites (those metabolites that are either produced by known metabolic reactions of an organism but not consumed, or vice versa); number of reactions; and number of reactions associated with genes. Simulations were conducted for each model using the as-distributed model default biomass objective function and with the biomass objective from the iFF708 model. Reported simulation results are divided into two subcolumns to reflect the use of two different objective functions for each model. Simulation results include the number of blocked reactions for each biomass definition (those reactions which cannot carry flux due to network structural constraints); the Matthews’ Correlation Coefficient (MCC) for model predictions of single gene essentiality across all conditions simulated; and the Matthews’ Correlation Coefficient for model prediction of double gene essentiality (i.e., pairwise synthetic lethal interactions) for simulations using each models’ default biomass definition. Some parameter values differ from previously published values due to differing software implementation and annotation conventions.</p

Change in gene essentiality predictions between model and its nearest ancestor.

<p>When comparing the Matthews Correlation Coefficient for model gene essentiality predictions to the models’ nearest progenitors, we observe that the models may be segregated between those focusing on expanding model scope, and those focused on iterative refining an existing model by plotting the change in MCC between models. Generally, when the stated focus of a model developer is to expand the scope of the yeast metabolic network reconstruction, predictive ability suffers relative to the progenitor model. When the stated focus is to refine and curate a model, predictive ability improves relative to the progenitor model. Thus, our analysis finds that model predictive ability reflects the iterative process of model development. The asterisk near the Yeast 4 comparison indicates that it is an integrative model that not have a single nearest progenitor (we compared it to iFF708 for this analysis).</p

Evaluating model predictions of single-gene essentiality.

<p>Flux balance analysis was conducted to predict whether individual genes were essential for growth using seven different media formulations and two different model biomass objective functions for each model. Gene essentiality predictive performance is summarized in this table by the Matthews’ Correlation Coefficient (MCC). Model predictions were compared to two reference lists of essential genes: one derived from the saccharomyces genome database (SGD-based gene list) and one from Kuepfer et al. (Kuepfer-based gene list). These lists are provided as Supplementary Information. Modeled medium formulations included each model’s default medium, (Medium: Default), a minimal glucose-limited medium (Medium: Min-Glu), a synthetic complete glucose-limited medium based on Kennedy et al. (Medium: SC-Glu), and a synthetic medium based on Kuepfer et al. using glucose (Medium: Kuepfer-Glu), galactose (Medium: Kuepfer-Gal), glycerol (Medium: Kuepfer-Gly), or ethanol (Medium: Kuepfer-Eth) as carbon source. Simulations were conducted using each model’s default biomass definition (Biomass: Default) or the iFF708 model biomass definition (Biomass: iFF). In this heat map, color intensity is based upon positive Matthews’ Correlation Coefficient (MCC) (no parameter combinations lead to negative MCCs for any model), each row is a unique set of model parameters, and models are arranged in chronological order from left to right.</p