A QSAR–ICE–SSD Model Prediction of the PNECs for Per- and Polyfluoroalkyl Substances and Their Ecological Risks in an Area of Electroplating Factories

Per- and polyfluoroalkyl substances (PFASs) are a class of highly fluorinated aliphatic compounds that are persistent and bioaccumulate, posing a potential threat to the aquatic environment. The electroplating industry is considered to be an important source of PFASs. Due to emerging PFASs and many alternatives, the acute toxicity data for PFASs and their alternatives are relatively limited. In this study, a QSAR–ICE–SSD composite model was constructed by combining quantitative structure-activity relationship (QSAR), interspecies correlation estimation (ICE), and species sensitivity distribution (SSD) models in order to obtain the predicted no-effect concentrations (PNECs) of selected PFASs. The PNECs for the selected PFASs ranged from 0.254 to 6.27 mg/L. The ΣPFAS concentrations ranged from 177 to 983 ng/L in a river close to an electroplating industry in Shenzhen. The ecological risks associated with PFASs in the river were below 2.97 × 10−4.

Potential for Interspecies Toxicity Estimation in Soil Invertebrates

Interspecies correlation estimation (ICE) models are linear regressions that predict toxicity to a species with few data using a known toxicity value in a surrogate species. ICE models are well established for estimating toxicity to fish and aquatic invertebrates but have not been generally developed or applied to soil organisms. To facilitate the development of ICE models for soil invertebrates, a database of single chemical toxicity values was compiled from knowledgebases and reports that included 853 records encompassing 192 chemicals and 12 species. Most toxicity data for single chemicals tested in soil media were for species of earthworms, with only limited data for other species and taxa. ICE models were developed for eleven separate species pairs as least squares log-linear regressions of acute toxicity values of the same chemicals tested in both the surrogate and predicted species of soil organisms. Model uncertainty was assessed using leave one out cross-validation as the fold difference between a predicted and measured toxicity value. ICE models showed high accuracy within order (e.g., earthworm to earthworm), but less prediction accuracy in the two across-taxa models (Arthropoda to Annelida and the inverse). This study provides a proof-of-concept demonstration that ICE models can be developed for soil invertebrates.

Estimating genetic correlation jointly using individual-level and summary-level GWAS data

With the increasing accessibility of individual-level data from genome wide association studies, it is now common for researchers to have individual-level data of some traits in one specific population. For some traits, we can only access public released summary-level data due to privacy and safety concerns. The current methods to estimate genetic correlation can only be applied when the input data type of the two traits of interest is either both individual-level or both summary-level. When researchers have access to individual-level data for one trait and summary-level data for the other, they have to transform the individual-level data to summary-level data first and then apply summary data-based methods to estimate the genetic correlation. This procedure is computationally and statistically inefficient and introduces information loss. We introduce GENJI (Genetic correlation EstimatioN Jointly using Individual-level and summary data), a method that can estimate within-population or transethnic genetic correlation based on individual-level data for one trait and summary-level data for another trait. Through extensive simulations and analyses of real data on within-population and transethnic genetic correlation estimation, we show that GENJI produces more reliable and efficient estimation than summary data-based methods. Besides, when individual-level data are available for both traits, GENJI can achieve comparable performance than individual-level data-based methods. Downstream applications of genetic correlation can benefit from more accurate estimates. In particular, we show that more accurate genetic correlation estimation facilitates the predictability of cross-population polygenic risk scores.

Improved Estimation of Phenotypic Correlations Using Summary Association Statistics

Estimating the phenotypic correlations between complex traits and diseases based on their genome-wide association summary statistics has been a useful technique in genetic epidemiology and statistical genetics inference. Two state-of-the-art strategies, Z-score correlation across null-effect single nucleotide polymorphisms (SNPs) and LD score regression intercept, were widely applied to estimate phenotypic correlations. Here, we propose an improved Z-score correlation strategy based on SNPs with low minor allele frequencies (MAFs), and show how this simple strategy can correct the bias generated by the current methods. The low MAF estimator improves phenotypic correlation estimation, thus it is beneficial for methods and applications using phenotypic correlations inferred from summary association statistics.

Estimating genetic correlation jointly using individual-level and summary-level GWAS data

With the increasing accessibility of individual-level data from genome wide association studies, it is now common for researchers to have individual-level data of some traits in one specific population. For some traits, we can only access public released summary-level data due to privacy and safety concerns. The current methods to estimate genetic correlation can only be applied when the input data type of the two traits of interest is either both individual-level or both summary-level. When researchers have access to individual-level data for one trait and summary-level data for the other, they have to transform the individual-level data to summary-level data first and then apply summary data-based methods to estimate the genetic correlation. This procedure is computationally and statistically inefficient and introduces information loss. We introduce GENJI (Genetic correlation EstimatioN Jointly using Individual-level and summary data), a method that can estimate within-population or transethnic genetic correlation based on individual-level data for one trait and summary-level data for another trait. Through extensive simulations and analyses of real data on within-population and transethnic genetic correlation estimation, we show that GENJI produces more reliable and efficient estimation than summary data-based methods. Besides, when individual-level data are available for both traits, GENJI can achieve comparable performance than individual-level data-based methods. Downstream applications of genetic correlation can benefit from more accurate estimates. In particular, we show that more accurate genetic correlation estimation facilitates the predictability of cross-population polygenic risk scores.

Influence of living in the same home on biomonitored levels of consumer product chemicals

Background Individuals living in the same home may share exposures from direct contact with sources or indirectly through contamination of the home environment. Objective We investigated the influence of sharing a home on urine levels of ten phenolic chemicals present in some consumer products. Methods We used data from Silent Spring Institute’s Detox Me Action Kit (DMAK), a crowdsourced biomonitoring program in the US. Of the 726 DMAK participants, 185 lived in the same home with one or more other DMAK participants (n = 137 pairs, up to six participants in a home). The concentration distributions included values below the detection limit so we used statistical methods that account for left-censored data, including non-parametric correlation estimation and hierarchical Bayesian regression models. Results Concentrations were significantly positively correlated between pair-members sharing a home for nine of the ten chemicals. Concentrations of 2,5-dichlorophenol were the most strongly correlated between pair-members (tau = 0.46), followed by benzophenone-3 (tau = 0.31) and bisphenol A (tau = 0.21). The relative contribution of personal product use reported product use of other household members (up to 5 others), and the residual contribution from a shared household, including exposures not asked about, varied by chemical. Paraben concentrations were largely influenced by personal behaviors whereas dichlorophenol and bisphenol concentrations were largely influenced by shared home exposures not related to reported behaviors. Significance Measuring the influence of personal and household practices on biomonitoring exposures helps pinpoint major sources of exposure and highlights chemical-specific intervention strategies to reduce them.