Extent to which array genotyping and imputation with large reference panels approximate deep whole-genome sequencing.

Understanding the genetic basis of human diseases and traits is dependent on the identification and accurate genotyping of genetic variants. Deep whole-genome sequencing (WGS), the gold standard technology for SNP and indel identification and genotyping, remains very expensive for most large studies. Here, we quantify the extent to which array genotyping followed by genotype imputation can approximate WGS in studies of individuals of African, Hispanic/Latino, and European ancestry in the US and of Finnish ancestry in Finland (a population isolate). For each study, we performed genotype imputation by using the genetic variants present on the Illumina Core, OmniExpress, MEGA, and Omni 2.5M arrays with the 1000G, HRC, and TOPMed imputation reference panels. Using the Omni 2.5M array and the TOPMed panel, ≥90% of bi-allelic single-nucleotide variants (SNVs) are well imputed (r2 > 0.8) down to minor-allele frequencies (MAFs) of 0.14% in African, 0.11% in Hispanic/Latino, 0.35% in European, and 0.85% in Finnish ancestries. There was little difference in TOPMed-based imputation quality among the arrays with >700k variants. Individual-level imputation quality varied widely between and within the three US studies. Imputation quality also varied across genomic regions, producing regions where even common (MAF > 5%) variants were consistently not well imputed across ancestries. The extent to which array genotyping and imputation can approximate WGS therefore depends on reference panel, genotype array, sample ancestry, and genomic location. Imputation quality by variant or genomic region can be queried with our new tool, RsqBrowser, now deployed on the Michigan Imputation Server.

Integrating transcriptomics, metabolomics, and GWAS helps reveal molecular mechanisms for metabolite levels and disease risk.

Transcriptomics data have been integrated with genome-wide association studies (GWASs) to help understand disease/trait molecular mechanisms. The utility of metabolomics, integrated with transcriptomics and disease GWASs, to understand molecular mechanisms for metabolite levels or diseases has not been thoroughly evaluated. We performed probabilistic transcriptome-wide association and locus-level colocalization analyses to integrate transcriptomics results for 49 tissues in 706 individuals from the GTEx project, metabolomics results for 1,391 plasma metabolites in 6,136 Finnish men from the METSIM study, and GWAS results for 2,861 disease traits in 260,405 Finnish individuals from the FinnGen study. We found that genetic variants that regulate metabolite levels were more likely to influence gene expression and disease risk compared to the ones that do not. Integrating transcriptomics with metabolomics results prioritized 397 genes for 521 metabolites, including 496 previously identified gene-metabolite pairs with strong functional connections and suggested 33.3% of such gene-metabolite pairs shared the same causal variants with genetic associations of gene expression. Integrating transcriptomics and metabolomics individually with FinnGen GWAS results identified 1,597 genes for 790 disease traits. Integrating transcriptomics and metabolomics jointly with FinnGen GWAS results helped pinpoint metabolic pathways from genes to diseases. We identified putative causal effects of UGT1A1/UGT1A4 expression on gallbladder disorders through regulating plasma (E,E)-bilirubin levels, of SLC22A5 expression on nasal polyps and plasma carnitine levels through distinct pathways, and of LIPC expression on age-related macular degeneration through glycerophospholipid metabolic pathways. Our study highlights the power of integrating multiple sets of molecular traits and GWAS results to deepen understanding of disease pathophysiology.

Author Correction: Exome sequencing of Finnish isolates enhances rare-variant association power.

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

Exome sequencing of Finnish isolates enhances rare-variant association power.

Exome-sequencing studies have generally been underpowered to identify deleterious alleles with a large effect on complex traits as such alleles are mostly rare. Because the population of northern and eastern Finland has expanded considerably and in isolation following a series of bottlenecks, individuals of these populations have numerous deleterious alleles at a relatively high frequency. Here, using exome sequencing of nearly 20,000 individuals from these regions, we investigate the role of rare coding variants in clinically relevant quantitative cardiometabolic traits. Exome-wide association studies for 64 quantitative traits identified 26 newly associated deleterious alleles. Of these 26 alleles, 19 are either unique to or more than 20 times more frequent in Finnish individuals than in other Europeans and show geographical clustering comparable to Mendelian disease mutations that are characteristic of the Finnish population. We estimate that sequencing studies of populations without this unique history would require hundreds of thousands to millions of participants to achieve comparable association power.

A powerful subset-based method identifies gene set associations and improves interpretation in UK Biobank.

Tests of association between a phenotype and a set of genes in a biological pathway can provide insights into the genetic architecture of complex phenotypes beyond those obtained from single-variant or single-gene association analysis. However, most existing gene set tests have limited power to detect gene set-phenotype association when a small fraction of the genes are associated with the phenotype and cannot identify the potentially "active" genes that might drive a gene set-based association. To address these issues, we have developed Gene set analysis Association Using Sparse Signals (GAUSS), a method for gene set association analysis that requires only GWAS summary statistics. For each significantly associated gene set, GAUSS identifies the subset of genes that have the maximal evidence of association and can best account for the gene set association. Using pre-computed correlation structure among test statistics from a reference panel, our p value calculation is substantially faster than other permutation- or simulation-based approaches. In simulations with varying proportions of causal genes, we find that GAUSS effectively controls type 1 error rate and has greater power than several existing methods, particularly when a small proportion of genes account for the gene set signal. Using GAUSS, we analyzed UK Biobank GWAS summary statistics for 10,679 gene sets and 1,403 binary phenotypes. We found that GAUSS is scalable and identified 13,466 phenotype and gene set association pairs. Within these gene sets, we identify an average of 17.2 (max = 405) genes that underlie these gene set associations.

Genetic effects on liver chromatin accessibility identify disease regulatory variants.

Identifying the molecular mechanisms by which genome-wide association study (GWAS) loci influence traits remains challenging. Chromatin accessibility quantitative trait loci (caQTLs) help identify GWAS loci that may alter GWAS traits by modulating chromatin structure, but caQTLs have been identified in a limited set of human tissues. Here we mapped caQTLs in human liver tissue in 20 liver samples and identified 3,123 caQTLs. The caQTL variants are enriched in liver tissue promoter and enhancer states and frequently disrupt binding motifs of transcription factors expressed in liver. We predicted target genes for 861 caQTL peaks using proximity, chromatin interactions, correlation with promoter accessibility or gene expression, and colocalization with expression QTLs. Using GWAS signals for 19 liver function and/or cardiometabolic traits, we identified 110 colocalized caQTLs and GWAS signals, 56 of which contained a predicted caPeak target gene. At the LITAF LDL-cholesterol GWAS locus, we validated that a caQTL variant showed allelic differences in protein binding and transcriptional activity. These caQTLs contribute to the epigenomic characterization of human liver and help identify molecular mechanisms and genes at GWAS loci.

Low Calf Circumference is Associated with Prolonged Hospital Stay in Older Patients with Solid Tumors: A Secondary Analysis of a Cohort Study.

BACKGROUND: Older patients with cancer present intense loss of muscle mass (MM). Calf circumference (CC) is a simple measurement that assesses MM. This study analyzed the accuracy and association between low CC and negative outcomes in older patients with solid tumors. METHODS: A secondary analysis of a prospective cohort study of inpatients with cancer was conducted. Low CC was defined as CC ≤34 cm in males and ≤33 cm in females. The CC was adjusted for body mass index by reducing 3 or 7 cm for BMI (in kg/m2) of 25-29.9 and 30-39.9, respectively. Accuracy tests and regression analyses were performed to evaluate the criterion validity of low CC for predicting length of stay (LOS) and readmission. RESULTS: A total of 248 inpatients were evaluated (69.7 [standard deviation (SD) 7.2]; 59.7% men). Among them, 31% had a low CC. A low CC (crude and adjusted for BMI) showed poor performance in predicting LOS and readmission. In the adjusted analysis, older patients with low CC had a 2.45-fold increased risk of LOS ≥ 4 days. CONCLUSION: Low CC did not perform well in predicting negative outcomes in older patients with solid tumors. However, low CC was positively associated with LOS.

Safety Assessment of Alkane Diols as Used in Cosmetics.

The Expert Panel for Cosmetic Ingredient Safety (Panel) assessed the safety of 10 alkane diol ingredients as used in cosmetics. The alkane diols are structurally related to each other as small diols, and most are reported to function in cosmetics as solvents. The Panel reviewed the relevant data for these ingredients, and concluded that seven alkane diols are safe in cosmetics in the present practices of use and concentration described in this safety assessment, but that the available data are insufficient to make a determination of safety for three ingredients, namely 1,4-Butanediol, 2,3-Butanediol, and Octanediol.

Safety Assessment of Zinc Salts as Used in Cosmetics.

The Expert Panel for Cosmetic Ingredient Safety (Panel) assessed the safety of 27 inorganic and organometallic zinc salts as used in cosmetic formulations; these salts are specifically of the 2+ (II) oxidation state cation of zinc. These ingredients included in this report have various reported functions in cosmetics, including hair conditioning agents, skin conditioning agents, cosmetic astringents, cosmetic biocides, preservatives, oral care agents, buffering agents, bulking agents, chelating agents, and viscosity increasing agents. The Panel reviewed the relevant data for these ingredients, and concluded that these 27 ingredients are safe in cosmetics in the present practices of use and concentration described in this safety assessment when formulated to be non-irritating.

The Absence of Parkin Does Not Promote Dopamine or Mitochondrial Dysfunction in PolgAD257A/D257A Mitochondrial Mutator Mice.

Parkinson's disease (PD) is characterized by the progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc). In this study, we generated a transgenic model by crossing germline Parkin-/- mice with PolgAD257A mice, an established model of premature aging and mitochondrial stress. We hypothesized that loss of Parkin-/- in PolgAD257A/D257A mice would exacerbate mitochondrial dysfunction, leading to loss of dopamine neurons and nigral-striatal specific neurobehavioral motor dysfunction. We found that aged Parkin-/-/PolgAD257A/D257A male and female mice exhibited severe behavioral deficits, nonspecific to the nigral-striatal pathway, with neither dopaminergic neurodegeneration nor reductions in striatal dopamine. We saw no difference in expression levels of nuclear-encoded subunits of mitochondrial markers and mitochondrial Complex I and IV activities, although we did observe substantial reductions in mitochondrial-encoded COX41I, indicating mitochondrial dysfunction as a result of PolgAD257A/D257A mtDNA mutations. Expression levels of mitophagy markers LC3I/LC3II remained unchanged between cohorts, suggesting no overt mitophagy defects. Expression levels of the parkin substrates, VDAC, NLRP3, and AIMP2 remained unchanged, suggesting no parkin dysfunction. In summary, we were unable to observe dopaminergic neurodegeneration with corresponding nigral-striatal neurobehavioral deficits, nor Parkin or mitochondrial dysfunction in Parkin-/-/PolgAD257A/D257A mice. These findings support a lack of synergism of Parkin loss on mitochondrial dysfunction in mouse models of mitochondrial deficits.SIGNIFICANCE STATEMENT Producing a mouse model of Parkinson's disease (PD) that is etiologically relevant, recapitulates clinical hallmarks, and exhibits reproducible results is crucial to understanding the underlying pathology and in developing disease-modifying therapies. Here, we show that Parkin-/-/PolgAD257A/D257A mice, a previously reported PD mouse model, fails to reproduce a Parkinsonian phenotype. We show that these mice do not display dopaminergic neurodegeneration nor nigral-striatal-dependent motor deficits. Furthermore, we report that Parkin loss does not synergize with mitochondrial dysfunction. Our results demonstrate that Parkin-/-/PolgAD257A/D257A mice are not a reliable model for PD and adds to a growing body of work demonstrating that Parkin loss does not synergize with mitochondrial dysfunction in mouse models of mitochondrial deficits.

Ancestry-agnostic estimation of DNA sample contamination from sequence reads.

Detecting and estimating DNA sample contamination are important steps to ensure high-quality genotype calls and reliable downstream analysis. Existing methods rely on population allele frequency information for accurate estimation of contamination rates. Correctly specifying population allele frequencies for each individual in early stage of sequence analysis is impractical or even impossible for large-scale sequencing centers that simultaneously process samples from multiple studies across diverse populations. On the other hand, incorrectly specified allele frequencies may result in substantial bias in estimated contamination rates. For example, we observed that existing methods often fail to identify 10% contaminated samples at a typical 3% contamination exclusion threshold when genetic ancestry is misspecified. Such an incomplete screening of contaminated samples substantially inflates the estimated rate of genotyping errors even in deeply sequenced genomes and exomes. We propose a robust statistical method that accurately estimates DNA contamination and is agnostic to genetic ancestry of the intended or contaminating sample. Our method integrates the estimation of genetic ancestry and DNA contamination in a unified likelihood framework by leveraging individual-specific allele frequencies projected from reference genotypes onto principal component coordinates. Our method can also be used for estimating genetic ancestries, similar to LASER or TRACE, but simultaneously accounting for potential contamination. We demonstrate that our method robustly estimates contamination rates and genetic ancestries across populations and contamination scenarios. We further demonstrate that, in the presence of contamination, genetic ancestry inference can be substantially biased with existing methods that ignore contamination, while our method corrects for such biases.

Accuracy of the GLIM Criteria and SGA Compared to PG-SGA for the Diagnosis of Malnutrition and Its Impact on Prolonged Hospitalization: A Prospective Study in Patients with Cancer.

BACKGROUND: Early assessment of malnutrition in cancer patients is essential. This study analyzed the accuracy of the Global Leadership Initiative on Malnutrition (GLIM) and the Subjective Global Assessment (SGA), in diagnosing malnutrition, considering the Patient Generated-SGA (PG-SGA) as a reference, and the impact of malnutrition on hospital days. METHODS: We conducted a prospective cohort study in 183 patients with gastrointestinal, head and neck, and lung cancer. Malnutrition was assessed within 48 h, of hospital admission according to the SGA, PG-SGA, and GLIM. Accuracy tests and regression analysis were performed to assess the criterion validity of the GLIM and SGA for diagnosing malnutrition. RESULTS: Malnutrition was diagnosed in 57.3% (SGA), 86.3% (PG-SGA), and 74.9% (GLIM) of the inpatients. The median of hospitalization was 6 (3-11) days, with 47% hospitalized > 6 day. The SGA presented the best accuracy (AUC = 0.832) than the GLIM (AUC = 0.632) compared to PG-SGA. Patients diagnosed with malnutrition by SGA, GLIM, and PG-SGA remained hospitalized for 2.13, 3.19, and 4.56 day more than well-nourished patients, respectively. CONCLUSION: Compared to PG-SGA, the SGA presents good accuracy and adequate specificity (>80%). Malnutrition evaluated by SGA, PG-SGA, and GLIM was associated with more days of hospitalization.

Leave No Trace? Ecological and anthropogenic determinants of antibiotic resistant bacteria in a recreational alpine environment.

Antibiotic resistant bacteria (ARB) have been detected in remote environments, but the degree to which their presence is due to anthropogenic contamination remains unclear. Here, anthropogenic and ecological determinants of ARB were characterized in remote and highly visited areas of Rocky Mountain National Park in the United States. Soil and water samples were collected from 29 sites once a month for three months and measured for bacteria resistant to seven antibiotics with flow cytometry. A novel index of the likelihood of human presence (HPI) was generated for estimating human impact on ARB abundance. The HPI accounted for 44% of variation in ARB abundance in water samples (p < 0.0001) and 51% of variation in soil samples (p < 0.00001). Human presence index was illustrated as a reliable predictor of ARB abundance despite a tendency to underpredict at higher levels of human impact. Ecological determinants such as temperature, elevation, slope, and aspect were also found to be significantly associated with ARB abundance. These findings suggest that human presence drives the abundance of ARB in Rocky Mountain National Park, but ecological variables play a significant role in their presence and dispersal.

Adiponectin GWAS loci harboring extensive allelic heterogeneity exhibit distinct molecular consequences.

Loci identified in genome-wide association studies (GWAS) can include multiple distinct association signals. We sought to identify the molecular basis of multiple association signals for adiponectin, a hormone involved in glucose regulation secreted almost exclusively from adipose tissue, identified in the Metabolic Syndrome in Men (METSIM) study. With GWAS data for 9,262 men, four loci were significantly associated with adiponectin: ADIPOQ, CDH13, IRS1, and PBRM1. We performed stepwise conditional analyses to identify distinct association signals, a subset of which are also nearly independent (lead variant pairwise r2<0.01). Two loci exhibited allelic heterogeneity, ADIPOQ and CDH13. Of seven association signals at the ADIPOQ locus, two signals colocalized with adipose tissue expression quantitative trait loci (eQTLs) for three transcripts: trait-increasing alleles at one signal were associated with increased ADIPOQ and LINC02043, while trait-increasing alleles at the other signal were associated with decreased ADIPOQ-AS1. In reporter assays, adiponectin-increasing alleles at two signals showed corresponding directions of effect on transcriptional activity. Putative mechanisms for the seven ADIPOQ signals include a missense variant (ADIPOQ G90S), a splice variant, a promoter variant, and four enhancer variants. Of two association signals at the CDH13 locus, the first signal consisted of promoter variants, including the lead adipose tissue eQTL variant for CDH13, while a second signal included a distal intron 1 enhancer variant that showed ~2-fold allelic differences in transcriptional reporter activity. Fine-mapping and experimental validation demonstrated that multiple, distinct association signals at these loci can influence multiple transcripts through multiple molecular mechanisms.

Adipose Tissue Gene Expression Associations Reveal Hundreds of Candidate Genes for Cardiometabolic Traits.

Genome-wide association studies (GWASs) have identified thousands of genetic loci associated with cardiometabolic traits including type 2 diabetes (T2D), lipid levels, body fat distribution, and adiposity, although most causal genes remain unknown. We used subcutaneous adipose tissue RNA-seq data from 434 Finnish men from the METSIM study to identify 9,687 primary and 2,785 secondary cis-expression quantitative trait loci (eQTL; <1 Mb from TSS, FDR < 1%). Compared to primary eQTL signals, secondary eQTL signals were located further from transcription start sites, had smaller effect sizes, and were less enriched in adipose tissue regulatory elements compared to primary signals. Among 2,843 cardiometabolic GWAS signals, 262 colocalized by LD and conditional analysis with 318 transcripts as primary and conditionally distinct secondary cis-eQTLs, including some across ancestries. Of cardiometabolic traits examined for adipose tissue eQTL colocalizations, waist-hip ratio (WHR) and circulating lipid traits had the highest percentage of colocalized eQTLs (15% and 14%, respectively). Among alleles associated with increased cardiometabolic GWAS risk, approximately half (53%) were associated with decreased gene expression level. Mediation analyses of colocalized genes and cardiometabolic traits within the 434 individuals provided further evidence that gene expression influences variant-trait associations. These results identify hundreds of candidate genes that may act in adipose tissue to influence cardiometabolic traits.

ACE2 expression in adipose tissue is associated with cardio-metabolic risk factors and cell type composition-implications for COVID-19.

BACKGROUND: COVID-19 severity varies widely. Although some demographic and cardio-metabolic factors, including age and obesity, are associated with increasing risk of severe illness, the underlying mechanism(s) are uncertain. SUBJECTS/METHODS: In a meta-analysis of three independent studies of 1471 participants in total, we investigated phenotypic and genetic factors associated with subcutaneous adipose tissue expression of Angiotensin I Converting Enzyme 2 (ACE2), measured by RNA-Seq, which acts as a receptor for SARS-CoV-2 cellular entry. RESULTS: Lower adipose tissue ACE2 expression was associated with multiple adverse cardio-metabolic health indices, including type 2 diabetes (T2D) (P = 9.14 × 10-6), obesity status (P = 4.81 × 10-5), higher serum fasting insulin (P = 5.32 × 10-4), BMI (P = 3.94 × 10-4), and lower serum HDL levels (P = 1.92 × 10-7). ACE2 expression was also associated with estimated proportions of cell types in adipose tissue: lower expression was associated with a lower proportion of microvascular endothelial cells (P = 4.25 × 10-4) and higher proportion of macrophages (P = 2.74 × 10-5). Despite an estimated heritability of 32%, we did not identify any proximal or distal expression quantitative trait loci (eQTLs) associated with adipose tissue ACE2 expression. CONCLUSIONS: Our results demonstrate that individuals with cardio-metabolic features known to increase risk of severe COVID-19 have lower background ACE2 levels in this highly relevant tissue. Reduced adipose tissue ACE2 expression may contribute to the pathophysiology of cardio-metabolic diseases, as well as the associated increased risk of severe COVID-19.

The Impact of the COVID-19 Pandemic on Health Care Utilization Among Insured Individuals With Common Chronic Conditions.

OBJECTIVE: The COVID pandemic has had a significant impact on the US health care system. Our primary objective was to understand the impact of the COVID pandemic on non-COVID-related health care utilization among insured individuals with chronic conditions. Our secondary objective was to examine the differential impact by individual characteristics. MAIN DATA SOURCE: Medical and pharmacy claims data for individuals enrolled in a large insurer across the United States. RESEARCH DESIGN: A retrospective and repeated cross-sectional study. Overall and condition-specific health care utilization and cost metrics in (1) March 1 to June 15 and (2) June 16 to September 30, 2020 were compared with the same months during 2016-2019. SUBJECTS: Members of all ages with a diagnosis of diabetes, cardiovascular disease, or chronic kidney disease with commercial or Medicare Advantage insurance. RESULTS: Most non-COVID-related health care utilization decreased drastically on March 1 to June 15, 2020 [odds ratio (OR) range across condition-specific tests: 0.55-0.69; incidence rate ratio (IRR) range for hospitalization/emergency department (ED) visit/outpatient visit: 0.65-0.77] but returned to closer to pre-COVID levels by June 16 to September 30, 2020 [OR range across condition-specific tests: 0.93-1.08; IRR range for hospitalization/ED visit/outpatient visit: 0.77-0.97]. Our study found an enormous increase in telehealth use on March 1 to June 15, 2020 (90-170 times prepandemic levels). A differential impact was observed by age, sex, region of residence, and insurance type. IMPLICATIONS: Further investigation is needed to assess the impact of these changes in health care utilization on long-term health outcomes.

Integrative analysis of gene expression, DNA methylation, physiological traits, and genetic variation in human skeletal muscle.

We integrate comeasured gene expression and DNA methylation (DNAme) in 265 human skeletal muscle biopsies from the FUSION study with >7 million genetic variants and eight physiological traits: height, waist, weight, waist-hip ratio, body mass index, fasting serum insulin, fasting plasma glucose, and type 2 diabetes. We find hundreds of genes and DNAme sites associated with fasting insulin, waist, and body mass index, as well as thousands of DNAme sites associated with gene expression (eQTM). We find that controlling for heterogeneity in tissue/muscle fiber type reduces the number of physiological trait associations, and that long-range eQTMs (>1 Mb) are reduced when controlling for tissue/muscle fiber type or latent factors. We map genetic regulators (quantitative trait loci; QTLs) of expression (eQTLs) and DNAme (mQTLs). Using Mendelian randomization (MR) and mediation techniques, we leverage these genetic maps to predict 213 causal relationships between expression and DNAme, approximately two-thirds of which predict methylation to causally influence expression. We use MR to integrate FUSION mQTLs, FUSION eQTLs, and GTEx eQTLs for 48 tissues with genetic associations for 534 diseases and quantitative traits. We identify hundreds of genes and thousands of DNAme sites that may drive the reported disease/quantitative trait genetic associations. We identify 300 gene expression MR associations that are present in both FUSION and GTEx skeletal muscle and that show stronger evidence of MR association in skeletal muscle than other tissues, which may partially reflect differences in power across tissues. As one example, we find that increased RXRA muscle expression may decrease lean tissue mass.

Safety Assessment of Hydroxyethyl-3,4-Methylenedioxyaniline HCl as Used in Cosmetics.

The Expert Panel for Cosmetic Ingredient Safety (Panel) reviewed the safety of Hydroxyethyl-3,4-Methylenedioxyaniline HCl, which is reported to function as a hair dye ingredient. The Panel reviewed relevant data provided in this safety assessment, and concluded that Hydroxyethyl-3,4-Methylenedioxyaniline HCl is safe for use as a hair dye ingredient in the present practices of use and concentrations described in this report. The Panel cautions that this ingredient should not be used in cosmetic products in which N-nitroso compounds can be formed.

Safety Assessment of Panthenol, Pantothenic Acid, and Derivatives as Used in Cosmetics.

The Expert Panel for Cosmetic Ingredient Safety (Panel) assessed the safety of Panthenol, Pantothenic Acid, and 5 derivatives as used in cosmetics. These ingredients named in this report are reported to function in cosmetics as hair conditioning agents, and Panthenol also is reported to function as a skin-conditioning agent-humectant and a solvent. The Panel reviewed relevant data for these ingredients, and concluded that these 7 ingredients are safe in cosmetics in the present practices of use concentration described in this safety assessment.