Where does the carbon go? Long-term effects of forest management on the carbon budget of a temperate-forest water-supply watershed.

While forest management commonly seeks to increase carbon (C) capture and sequestration, in some settings, a high density of C storage may be detrimental to other land uses and ecosystem services. We study a forested, drinking-water-supply watershed to determine the effects of forest management on C storage with the implicit understanding that greater storage of C will lead to increased quantity of carbon exported hydrologically into a source-water reservoir. Using a custom implementation of CBM-CFS3, a Canadian model to simulate C transformations and movement in forested systems, and a custom forest disturbance and management model, we simulate various management scenarios and their C outcomes. The largest forest C pool, mineral soils, is very slow to change and manipulating DOC export through this pool would likely not be feasible within human management timescales. Other pools, in which C has lower residence time and from which C is more readily mobilized, are a more promising area for future research into hydrologic DOC export under varying management regimes. Our findings indicate that management activities can serve to reduce forest C storage, but further research is required to connect these outcomes to hydrologic export.

On the cross-population generalizability of gene expression prediction models.

The genetic control of gene expression is a core component of human physiology. For the past several years, transcriptome-wide association studies have leveraged large datasets of linked genotype and RNA sequencing information to create a powerful gene-based test of association that has been used in dozens of studies. While numerous discoveries have been made, the populations in the training data are overwhelmingly of European descent, and little is known about the generalizability of these models to other populations. Here, we test for cross-population generalizability of gene expression prediction models using a dataset of African American individuals with RNA-Seq data in whole blood. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic simulations of both populations and gene expression, we show that accurate cross-population generalizability of transcriptome prediction only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTLs showed patterns similar to real-world data. Therefore, generating RNA-Seq data in diverse populations is a critical step towards multi-ethnic utility of gene expression prediction.

Integrative genomic analysis in African American children with asthma finds three novel loci associated with lung function.

Bronchodilator (BD) drugs are commonly prescribed for treatment and management of obstructive lung function present with diseases such as asthma. Administration of BD medication can partially or fully restore lung function as measured by pulmonary function tests. The genetics of baseline lung function measures taken before BD medication have been extensively studied, and the genetics of the BD response itself have received some attention. However, few studies have focused on the genetics of post-BD lung function. To address this gap, we analyzed lung function phenotypes in 1103 subjects from the Study of African Americans, Asthma, Genes, and Environment, a pediatric asthma case-control cohort, using an integrative genomic analysis approach that combined genotype, locus-specific genetic ancestry, and functional annotation information. We integrated genome-wide association study (GWAS) results with an admixture mapping scan of three pulmonary function tests (forced expiratory volume in 1 s [FEV1 ], forced vital capacity [FVC], and FEV1 /FVC) taken before and after albuterol BD administration on the same subjects, yielding six traits. We identified 18 GWAS loci, and five additional loci from admixture mapping, spanning several known and novel lung function candidate genes. Most loci identified via admixture mapping exhibited wide variation in minor allele frequency across genotyped global populations. Functional fine-mapping revealed an enrichment of epigenetic annotations from peripheral blood mononuclear cells, fetal lung tissue, and lung fibroblasts. Our results point to three novel potential genetic drivers of pre- and post-BD lung function: ADAMTS1, RAD54B, and EGLN3.

Sequencing and imputation in GWAS: Cost-effective strategies to increase power and genomic coverage across diverse populations.

A key aim for current genome-wide association studies (GWAS) is to interrogate the full spectrum of genetic variation underlying human traits, including rare variants, across populations. Deep whole-genome sequencing is the gold standard to fully capture genetic variation, but remains prohibitively expensive for large sample sizes. Array genotyping interrogates a sparser set of variants, which can be used as a scaffold for genotype imputation to capture a wider set of variants. However, imputation quality depends crucially on reference panel size and genetic distance from the target population. Here, we consider sequencing a subset of GWAS participants and imputing the rest using a reference panel that includes both sequenced GWAS participants and an external reference panel. We investigate how imputation quality and GWAS power are affected by the number of participants sequenced for admixed populations (African and Latino Americans) and European population isolates (Sardinians and Finns), and identify powerful, cost-effective GWAS designs given current sequencing and array costs. For populations that are well-represented in existing reference panels, we find that array genotyping alone is cost-effective and well-powered to detect common- and rare-variant associations. For poorly represented populations, sequencing a subset of participants is often most cost-effective, and can substantially increase imputation quality and GWAS power.

Genome-wide association study reveals a novel locus for asthma with severe exacerbations in diverse populations.

BACKGROUND: Severe asthma exacerbations are a major cause of asthma morbidity and increased healthcare costs. Several studies have shown racial and ethnic differences in asthma exacerbation rates. We aimed to identify genetic variants associated with severe exacerbations in two high-risk populations for asthma. METHODS: A genome-wide association study of asthma in children and youth with severe exacerbations was performed in 1283 exacerbators and 2027 controls without asthma of Latino ancestry. Independent suggestive variants (P ≤ 5 × 10-6 ) were selected for replication in 448 African Americans exacerbators and 595 controls. Case-only analyses were performed comparing the exacerbators with additional 898 Latinos and 524 African Americans asthma patients without exacerbations, while adjusting by treatment category as a proxy of asthma severity. We analyzed the functionality of associated variants with in silico methods and by correlating genotypes with methylation levels in whole blood in a subset of 473 Latinos. RESULTS: We identified two genome-wide significant associations for susceptibility to asthma with severe exacerbations, including a novel locus located at chromosome 2p21 (rs4952375, odds ratio = 1.39, P = 3.8 × 10-8 ), which was also associated with asthma exacerbations in a case-only analysis (odds ratio = 1.25, P = 1.95 × 10-3 ). This polymorphism is an expression quantitative trait locus of the long intergenic non-protein coding RNA 1913 (LINC01913) in lung tissues (P = 1.3 × 10-7 ) and influences methylation levels of the protein kinase domain-containing cytoplasmic (PKDCC) gene in whole-blood cells (P = 9.8 × 10-5 ). CONCLUSION: We identified a novel susceptibility locus for severe asthma exacerbations in Hispanic/Latino and African American youths with functional effects in gene expression and methylation status of neighboring genes.

Whole-Genome Sequencing Identifies Novel Functional Loci Associated with Lung Function in Puerto Rican Youth.

Rationale: Puerto Ricans have the highest childhood asthma prevalence in the United States (23.6%); however, the etiology is uncertain.Objectives: In this study, we sought to uncover the genetic architecture of lung function in Puerto Rican youth with and without asthma who were recruited from the island (n = 836).Methods: We used admixture-mapping and whole-genome sequencing data to discover genomic regions associated with lung function. Functional roles of the prioritized candidate SNPs were examined with chromatin immunoprecipitation sequencing, RNA sequencing, and expression quantitative trait loci data.Measurements and Main Results: We discovered a genomic region at 1q32 that was significantly associated with a 0.12-L decrease in the lung volume of exhaled air (95% confidence interval, -0.17 to -0.07; P = 6.62 × 10-8) with each allele of African ancestry. Within this region, two SNPs were expression quantitative trait loci of TMEM9 in nasal airway epithelial cells and MROH3P in esophagus mucosa. The minor alleles of these SNPs were associated with significantly decreased lung function and decreased TMEM9 gene expression. Another admixture-mapping peak was observed on chromosome 5q35.1, indicating that each Native American ancestry allele was associated with a 0.15-L increase in lung function (95% confidence interval, 0.08-0.21; P = 5.03 × 10-6). The region-based association tests identified four suggestive windows that harbored candidate rare variants associated with lung function.Conclusions: We identified common and rare genetic variants that may play a critical role in lung function among Puerto Rican youth. We independently validated an inflammatory pathway that could potentially be used to develop more targeted treatments and interventions for patients with asthma.

OPENMENDEL: a cooperative programming project for statistical genetics.

Statistical methods for genome-wide association studies (GWAS) continue to improve. However, the increasing volume and variety of genetic and genomic data make computational speed and ease of data manipulation mandatory in future software. In our view, a collaborative effort of statistical geneticists is required to develop open source software targeted to genetic epidemiology. Our attempt to meet this need is called the OPENMENDEL project (https://openmendel.github.io). It aims to (1) enable interactive and reproducible analyses with informative intermediate results, (2) scale to big data analytics, (3) embrace parallel and distributed computing, (4) adapt to rapid hardware evolution, (5) allow cloud computing, (6) allow integration of varied genetic data types, and (7) foster easy communication between clinicians, geneticists, statisticians, and computer scientists. This article reviews and makes recommendations to the genetic epidemiology community in the context of the OPENMENDEL project.

Ambient air pollution, asthma drug response, and telomere length in African American youth.

BACKGROUND: Telomere length (TL) can serve as a potential biomarker for conditions associated with chronic oxidative stress and inflammation, such as asthma. Air pollution can induce oxidative stress. Understanding the relationship between TL, asthma, and air pollution is important for identifying risk factors contributing to unhealthy aging in children. OBJECTIVES: We sought to investigate associations between exposures to ambient air pollutants and TL in African American children and adolescents and to examine whether African ancestry, asthma status, and steroid medication use alter the association. METHODS: Linear regression was used to examine associations between absolute telomere length (aTL) and estimated annual average residential ozone (O3) and fine particulate matter with a diameter of 2.5 µm or less (PM2.5) exposures in a cross-sectional analysis of 1072 children in an existing asthma case-control study. African ancestry, asthma status, and use of steroid medications were examined as effect modifiers. RESULTS: Participants' aTLs were measured by using quantitative PCR. A 1-ppb and 1 µg/m3 increase in annual average exposure to O3 and PM2.5 were associated with a decrease in aTL of 37.1 kilo-base pair (kb; 95% CI, -66.7 to -7.4 kb) and 57.1 kb (95% CI, -118.1 to 3.9 kb), respectively. African ancestry and asthma were not effect modifiers; however, exposure to steroid medications modified the relationships between TL and pollutants. Past-year exposure to O3 and PM2.5 was associated with shorter TLs in patients without steroid use. CONCLUSION: Exposure to air pollution was associated with shorter TLs in nonasthmatic children and adolescents. This was not the case for asthmatic children as a group, but those receiving steroid medication had less shortening than those not using steroids. Reduced exposure to air pollution in childhood might help to preserve TL.

Whole-Genome Sequencing of Pharmacogenetic Drug Response in Racially Diverse Children with Asthma.

RATIONALE: Albuterol, a bronchodilator medication, is the first-line therapy for asthma worldwide. There are significant racial/ethnic differences in albuterol drug response. OBJECTIVES: To identify genetic variants important for bronchodilator drug response (BDR) in racially diverse children. METHODS: We performed the first whole-genome sequencing pharmacogenetics study from 1,441 children with asthma from the tails of the BDR distribution to identify genetic association with BDR. MEASUREMENTS AND MAIN RESULTS: We identified population-specific and shared genetic variants associated with BDR, including genome-wide significant (P < 3.53 × 10-7) and suggestive (P < 7.06 × 10-6) loci near genes previously associated with lung capacity (DNAH5), immunity (NFKB1 and PLCB1), and ß-adrenergic signaling (ADAMTS3 and COX18). Functional analyses of the BDR-associated SNP in NFKB1 revealed potential regulatory function in bronchial smooth muscle cells. The SNP is also an expression quantitative trait locus for a neighboring gene, SLC39A8. The lack of other asthma study populations with BDR and whole-genome sequencing data on minority children makes it impossible to perform replication of our rare variant associations. Minority underrepresentation also poses significant challenges to identify age-matched and population-matched cohorts of sufficient sample size for replication of our common variant findings. CONCLUSIONS: The lack of minority data, despite a collaboration of eight universities and 13 individual laboratories, highlights the urgent need for a dedicated national effort to prioritize diversity in research. Our study expands the understanding of pharmacogenetic analyses in racially/ethnically diverse populations and advances the foundation for precision medicine in at-risk and understudied minority populations.

Iterative hard thresholding for model selection in genome-wide association studies.

A genome-wide association study (GWAS) correlates marker and trait variation in a study sample. Each subject is genotyped at a multitude of SNPs (single nucleotide polymorphisms) spanning the genome. Here, we assume that subjects are randomly collected unrelateds and that trait values are normally distributed or can be transformed to normality. Over the past decade, geneticists have been remarkably successful in applying GWAS analysis to hundreds of traits. The massive amount of data produced in these studies present unique computational challenges. Penalized regression with the ℓ1 penalty (LASSO) or minimax concave penalty (MCP) penalties is capable of selecting a handful of associated SNPs from millions of potential SNPs. Unfortunately, model selection can be corrupted by false positives and false negatives, obscuring the genetic underpinning of a trait. Here, we compare LASSO and MCP penalized regression to iterative hard thresholding (IHT). On GWAS regression data, IHT is better at model selection and comparable in speed to both methods of penalized regression. This conclusion holds for both simulated and real GWAS data. IHT fosters parallelization and scales well in problems with large numbers of causal markers. Our parallel implementation of IHT accommodates SNP genotype compression and exploits multiple CPU cores and graphics processing units (GPUs). This allows statistical geneticists to leverage commodity desktop computers in GWAS analysis and to avoid supercomputing. AVAILABILITY: Source code is freely available at https://github.com/klkeys/IHT.jl.

Gene expression in African Americans, Puerto Ricans and Mexican Americans reveals ancestry-specific patterns of genetic architecture.

We explored ancestry-related differences in the genetic architecture of whole-blood gene expression using whole-genome and RNA sequencing data from 2,733 African Americans, Puerto Ricans and Mexican Americans. We found that heritability of gene expression significantly increased with greater proportions of African genetic ancestry and decreased with higher proportions of Indigenous American ancestry, reflecting the relationship between heterozygosity and genetic variance. Among heritable protein-coding genes, the prevalence of ancestry-specific expression quantitative trait loci (anc-eQTLs) was 30% in African ancestry and 8% for Indigenous American ancestry segments. Most anc-eQTLs (89%) were driven by population differences in allele frequency. Transcriptome-wide association analyses of multi-ancestry summary statistics for 28 traits identified 79% more gene-trait associations using transcriptome prediction models trained in our admixed population than models trained using data from the Genotype-Tissue Expression project. Our study highlights the importance of measuring gene expression across large and ancestrally diverse populations for enabling new discoveries and reducing disparities.

Epigenome-wide association study of lung function in Latino children and youth with asthma.

INTRODUCTION: DNA methylation studies have associated methylation levels at different CpG sites or genomic regions with lung function. Moreover, genetic ancestry has been associated with lung function in Latinos. However, no epigenome-wide association study (EWAS) of lung function has been performed in this population. Here, we aimed to identify DNA methylation patterns associated with lung function in pediatric asthma among Latinos. RESULTS: We conducted an EWAS in whole blood from 250 Puerto Rican and 148 Mexican American children and young adults with asthma. A total of five CpGs exceeded the genome-wide significance threshold of p = 1.17 × 10-7 in the combined analyses from Puerto Ricans and Mexican Americans: cg06035600 (MAP3K6, p = 6.13 × 10-8) showed significant association with pre-bronchodilator Tiffeneau-Pinelli index, the probes cg00914963 (TBC1D16, p = 1.04 × 10-7), cg16405908 (MRGPRE, p = 2.05 × 10-8), and cg07428101 (MUC2, p = 5.02 × 10-9) were associated with post-bronchodilator forced vital capacity (FVC), and cg20515679 (KCNJ6) with post-bronchodilator Tiffeneau-Pinelli index (p = 1.13 × 10-8). However, these markers did not show significant associations in publicly available data from Europeans (p > 0.05). A methylation quantitative trait loci analysis revealed that methylation levels at these CpG sites were regulated by genetic variation in Latinos and the Biobank-based Integrative Omics Studies (BIOS) consortium. Additionally, two differentially methylated regions in REXOC and AURKC were associated with pre-bronchodilator Tiffeneau-Pinelli index (adjusted p < 0.05) in Puerto Ricans and Mexican Americans. Moreover, we replicated some of the previous differentially methylated signals associated with lung function in non-Latino populations. CONCLUSIONS: We replicated previous associations of epigenetic markers with lung function in whole blood and identified novel population-specific associations shared among Latino subgroups.

A genome-wide study of DNA methylation in white blood cells and asthma in Latino children and youth.

Latinos are heavily affected with childhood asthma. Little is known about epigenetic mechanisms of asthma in Latino youth. We conducted a meta-analysis of two epigenome-wide association studies (EWAS) of asthma, using DNA from white blood cells (WBCs) from 1,136 Latino children and youth aged 6 to 20 years. Genes near the top CpG sites in this EWAS were examined in a pathway enrichment analysis, and we then assessed whether our results replicated those from publicly available data from three independent EWAS conducted in non-Latino populations. We found that DNA methylation profiles differed between subjects with and without asthma. After adjustment for covariates and multiple testing, two CpGs were differentially methylated at a false discovery rate (FDR)-adjusted P < 0.1, and 193 CpG sites were differentially methylated at FDR-adjusted P < 0.2. The two top CpGs are near genes relevant to inflammatory signalling, including CAMK1D (Calcium/Calmodulin Dependent Protein Kinase ID) and TIGIT (T Cell Immunoreceptor With Ig And ITIM Domains). Moreover, 25 genomic regions were differentially methylated between subjects with and without asthma, at Sidák-corrected P < 0.10. An enrichment analysis then identified the TGF-beta pathway as most relevant to asthma in our analysis, and we replicated some of the top signals from publicly available EWAS datasets in non-Hispanic populations. In conclusion, we have identified novel epigenetic markers of asthma in WBCs from Latino children and youth, while also replicating previous results from studies conducted in non-Latinos.

Native American Ancestry and Air Pollution Interact to Impact Bronchodilator Response in Puerto Rican Children with Asthma.

Objective: Asthma is the most common chronic disease in children. Short-acting bronchodilator medications are the most commonly prescribed asthma treatment worldwide, regardless of disease severity. Puerto Rican children display the highest asthma morbidity and mortality of any US population. Alarmingly, Puerto Rican children with asthma display poor bronchodilator drug response (BDR). Reduced BDR may explain, in part, the increased asthma morbidity and mortality observed in Puerto Rican children with asthma. Gene-environment interactions may explain a portion of the heritability of BDR. We aimed to identify gene-environment interactions associated with BDR in Puerto Rican children with asthma. Setting: Genetic, environmental, and psycho-social data from the Genes-environments and Admixture in Latino Americans (GALA II) case-control study. Participants: Our discovery dataset consisted of 658 Puerto Rican children with asthma; our replication dataset consisted of 514 Mexican American children with asthma. Main Outcome Measures: We assessed the association of pairwise interaction models with BDR using ViSEN (Visualization of Statistical Epistasis Networks). Results: We identified a non-linear interaction between Native American genetic ancestry and air pollution significantly associated with BDR in Puerto Rican children with asthma. This interaction was robust to adjustment for age and sex but was not significantly associated with BDR in our replication population. Conclusions: Decreased Native American ancestry coupled with increased air pollution exposure was associated with increased BDR in Puerto Rican children with asthma. Our study acknowledges BDR's phenotypic complexity, and emphasizes the importance of integrating social, environmental, and biological data to further our understanding of complex disease.

Iterative hard thresholding in genome-wide association studies: Generalized linear models, prior weights, and double sparsity.

BACKGROUND: Consecutive testing of single nucleotide polymorphisms (SNPs) is usually employed to identify genetic variants associated with complex traits. Ideally one should model all covariates in unison, but most existing analysis methods for genome-wide association studies (GWAS) perform only univariate regression. RESULTS: We extend and efficiently implement iterative hard thresholding (IHT) for multiple regression, treating all SNPs simultaneously. Our extensions accommodate generalized linear models, prior information on genetic variants, and grouping of variants. In our simulations, IHT recovers up to 30% more true predictors than SNP-by-SNP association testing and exhibits a 2-3 orders of magnitude decrease in false-positive rates compared with lasso regression. We also test IHT on the UK Biobank hypertension phenotypes and the Northern Finland Birth Cohort of 1966 cardiovascular phenotypes. We find that IHT scales to the large datasets of contemporary human genetics and recovers the plausible genetic variants identified by previous studies. CONCLUSIONS: Our real data analysis and simulation studies suggest that IHT can (i) recover highly correlated predictors, (ii) avoid over-fitting, (iii) deliver better true-positive and false-positive rates than either marginal testing or lasso regression, (iv) recover unbiased regression coefficients, (v) exploit prior information and group-sparsity, and (vi) be used with biobank-sized datasets. Although these advances are studied for genome-wide association studies inference, our extensions are pertinent to other regression problems with large numbers of predictors.

Lung Function in African American Children with Asthma Is Associated with Novel Regulatory Variants of the KIT Ligand KITLG/SCF and Gene-By-Air-Pollution Interaction.

Baseline lung function, quantified as forced expiratory volume in the first second of exhalation (FEV1), is a standard diagnostic criterion used by clinicians to identify and classify lung diseases. Using whole-genome sequencing data from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine project, we identified a novel genetic association with FEV1 on chromosome 12 in 867 African American children with asthma (P = 1.26 × 10-8, ß = 0.302). Conditional analysis within 1 Mb of the tag signal (rs73429450) yielded one major and two other weaker independent signals within this peak. We explored statistical and functional evidence for all variants in linkage disequilibrium with the three independent signals and yielded nine variants as the most likely candidates responsible for the association with FEV1 Hi-C data and expression QTL analysis demonstrated that these variants physically interacted with KITLG (KIT ligand, also known as SCF), and their minor alleles were associated with increased expression of the KITLG gene in nasal epithelial cells. Gene-by-air-pollution interaction analysis found that the candidate variant rs58475486 interacted with past-year ambient sulfur dioxide exposure (P = 0.003, ß = 0.32). This study identified a novel protective genetic association with FEV1, possibly mediated through KITLG, in African American children with asthma. This is the first study that has identified a genetic association between lung function and KITLG, which has established a role in orchestrating allergic inflammation in asthma.

Proximal Distance Algorithms: Theory and Practice.

Proximal distance algorithms combine the classical penalty method of constrained minimization with distance majorization. If f(x) is the loss function, and C is the constraint set in a constrained minimization problem, then the proximal distance principle mandates minimizing the penalized loss f ( x ) + ρ 2 dist ( x , C ) 2 and following the solution x ρ to its limit as ρ tends to ∞. At each iteration the squared Euclidean distance dist(x,C)2 is majorized by the spherical quadratic ‖x- P C (x k )‖2, where P C (x k ) denotes the projection of the current iterate x k onto C. The minimum of the surrogate function f ( x ) + ρ 2 ‖ x - P C ( x k ) ‖ 2 is given by the proximal map prox ρ -1f [P C (x k )]. The next iterate x k+1 automatically decreases the original penalized loss for fixed ρ. Since many explicit projections and proximal maps are known, it is straightforward to derive and implement novel optimization algorithms in this setting. These algorithms can take hundreds if not thousands of iterations to converge, but the simple nature of each iteration makes proximal distance algorithms competitive with traditional algorithms. For convex problems, proximal distance algorithms reduce to proximal gradient algorithms and therefore enjoy well understood convergence properties. For nonconvex problems, one can attack convergence by invoking Zangwill's theorem. Our numerical examples demonstrate the utility of proximal distance algorithms in various high-dimensional settings, including a) linear programming, b) constrained least squares, c) projection to the closest kinship matrix, d) projection onto a second-order cone constraint, e) calculation of Horn's copositive matrix index, f) linear complementarity programming, and g) sparse principal components analysis. The proximal distance algorithm in each case is competitive or superior in speed to traditional methods such as the interior point method and the alternating direction method of multipliers (ADMM). Source code for the numerical examples can be found at https://github.com/klkeys/proxdist.

Influence of pathway topology and functional class on the molecular evolution of human metabolic genes.

Metabolic networks comprise thousands of enzymatic reactions functioning in a controlled manner and have been shaped by natural selection. Thanks to the genome data, the footprints of adaptive (positive) selection are detectable, and the strength of purifying selection can be measured. This has made possible to know where, in the metabolic network, adaptive selection has acted and where purifying selection is more or less strong and efficient. We have carried out a comprehensive molecular evolutionary study of all the genes involved in the human metabolism. We investigated the type and strength of the selective pressures that acted on the enzyme-coding genes belonging to metabolic pathways during the divergence of primates and rodents. Then, we related those selective pressures to the functional and topological characteristics of the pathways. We have used DNA sequences of all enzymes (956) of the metabolic pathways comprised in the HumanCyc database, using genome data for humans and five other mammalian species. We have found that the evolution of metabolic genes is primarily constrained by the layer of the metabolism in which the genes participate: while genes encoding enzymes of the inner core of metabolism are much conserved, those encoding enzymes participating in the outer layer, mediating the interaction with the environment, are evolutionarily less constrained and more plastic, having experienced faster functional evolution. Genes that have been targeted by adaptive selection are endowed by higher out-degree centralities than non-adaptive genes, while genes with high in-degree centralities are under stronger purifying selection. When the position along the pathway is considered, a funnel-like distribution of the strength of the purifying selection is found. Genes at bottom positions are highly preserved by purifying selection, whereas genes at top positions, catalyzing the first steps, are open to evolutionary changes. These results show how functional and topological characteristics of metabolic pathways contribute to shape the patterns of evolutionary pressures driven by natural selection and how pathway network structure matters in the evolutionary process that shapes the evolution of the system.

BioSimulator.jl: Stochastic simulation in Julia.

BACKGROUND AND OBJECTIVES: Biological systems with intertwined feedback loops pose a challenge to mathematical modeling efforts. Moreover, rare events, such as mutation and extinction, complicate system dynamics. Stochastic simulation algorithms are useful in generating time-evolution trajectories for these systems because they can adequately capture the influence of random fluctuations and quantify rare events. We present a simple and flexible package, BioSimulator.jl, for implementing the Gillespie algorithm, τ-leaping, and related stochastic simulation algorithms. The objective of this work is to provide scientists across domains with fast, user-friendly simulation tools. METHODS: We used the high-performance programming language Julia because of its emphasis on scientific computing. Our software package implements a suite of stochastic simulation algorithms based on Markov chain theory. We provide the ability to (a) diagram Petri Nets describing interactions, (b) plot average trajectories and attached standard deviations of each participating species over time, and (c) generate frequency distributions of each species at a specified time. RESULTS: BioSimulator.jl's interface allows users to build models programmatically within Julia. A model is then passed to the simulate routine to generate simulation data. The built-in tools allow one to visualize results and compute summary statistics. Our examples highlight the broad applicability of our software to systems of varying complexity from ecology, systems biology, chemistry, and genetics. CONCLUSION: The user-friendly nature of BioSimulator.jl encourages the use of stochastic simulation, minimizes tedious programming efforts, and reduces errors during model specification.

Genetic Determinants of Telomere Length in African American Youth.

Telomere length (TL) is associated with numerous disease states and is affected by genetic and environmental factors. However, TL has been mostly studied in adult populations of European or Asian ancestry. These studies have identified 34 TL-associated genetic variants recently used as genetic proxies for TL. The generalizability of these associations to pediatric populations and racially diverse populations, specifically of African ancestry, remains unclear. Furthermore, six novel variants associated with TL in a population of European children have been identified but not validated. We measured TL from whole blood samples of 492 healthy African American youth (children and adolescents between 8 and 20 years old) and performed the first genome-wide association study of TL in this population. We were unable to replicate neither the 34 reported genetic associations found in adults nor the six genetic associations found in European children. However, we discovered a novel genome-wide significant association between TL and rs1483898 on chromosome 14. Our results underscore the importance of examining genetic associations with TL in diverse pediatric populations such as African Americans.