Identifying schizophrenia patients who carry pathogenic genetic copy number variants using standard clinical assessment: retrospective cohort study.

BACKGROUND: Copy number variants (CNVs) play a significant role in disease pathogenesis in a small subset of individuals with schizophrenia (~2.5%). Chromosomal microarray testing is a first-tier genetic test for many neurodevelopmental disorders. Similar testing could be useful in schizophrenia. AIMS: To determine whether clinically identifiable phenotypic features could be used to successfully model schizophrenia-associated (SCZ-associated) CNV carrier status in a large schizophrenia cohort. METHOD: Logistic regression and receiver operating characteristic (ROC) curves tested the accuracy of readily identifiable phenotypic features in modelling SCZ-associated CNV status in a discovery data-set of 1215 individuals with psychosis. A replication analysis was undertaken in a second psychosis data-set (n = 479). RESULTS: In the discovery cohort, specific learning disorder (OR = 8.12; 95% CI 1.16-34.88, P = 0.012), developmental delay (OR = 5.19; 95% CI 1.58-14.76, P = 0.003) and comorbid neurodevelopmental disorder (OR = 5.87; 95% CI 1.28-19.69, P = 0.009) were significant independent variables in modelling positive carrier status for a SCZ-associated CNV, with an area under the ROC (AUROC) of 74.2% (95% CI 61.9-86.4%). A model constructed from the discovery cohort including developmental delay and comorbid neurodevelopmental disorder variables resulted in an AUROC of 83% (95% CI 52.0-100.0%) for the replication cohort. CONCLUSIONS: These findings suggest that careful clinical history taking to document specific neurodevelopmental features may be informative in screening for individuals with schizophrenia who are at higher risk of carrying known SCZ-associated CNVs. Identification of genomic disorders in these individuals is likely to have clinical benefits similar to those demonstrated for other neurodevelopmental disorders.

Effects of complement gene-set polygenic risk score on brain volume and cortical measures in patients with psychotic disorders and healthy controls.

Multiple genome-wide association studies of schizophrenia have reported associations between genetic variants within the MHC region and disease risk, an association that has been partially accounted for by alleles of the complement component 4 (C4) gene. Following on previous findings of association between both C4 and other complement-related variants and memory function, we tested the hypothesis that polygenic scores calculated based on identified schizophrenia risk alleles within the "complement" system would be broadly associated with memory function and associated brain structure. We tested this using a polygenic risk score (PRS) calculated for complement genes, but excluding C4 variants. Higher complement-based PRS scores were observed to be associated with lower memory scores for the sample as a whole (N = 620, F change = 8.25; p = .004). A significant association between higher PRS and lower hippocampal volume was also observed (N = 216, R2 change = 0.016, p = .015). However, after correcting for further testing of association with the more general indices of cortical thickness, surface area or total brain volume, none of which were associated with complement, the association with hippocampal volume became non-significant. A post-hoc analysis of hippocampal subfields suggested an association between complement PRS and several hippocampal subfields, findings that appeared to be particularly driven by the patient sample. In conclusion, our study yielded suggestive evidence of association between complement-based schizophrenia PRS and variation in memory function and hippocampal volume.

A modifier of Huntington's disease onset at the MLH1 locus.

Huntington's disease (HD) is a dominantly inherited neurodegenerative disease caused by an expanded CAG repeat in HTT. Many clinical characteristics of HD such as age at motor onset are determined largely by the size of HTT CAG repeat. However, emerging evidence strongly supports a role for other genetic factors in modifying the disease pathogenesis driven by mutant huntingtin. A recent genome-wide association analysis to discover genetic modifiers of HD onset age provided initial evidence for modifier loci on chromosomes 8 and 15 and suggestive evidence for a locus on chromosome 3. Here, genotyping of candidate single nucleotide polymorphisms in a cohort of 3,314 additional HD subjects yields independent confirmation of the former two loci and moves the third to genome-wide significance at MLH1, a locus whose mouse orthologue modifies CAG length-dependent phenotypes in a Htt-knock-in mouse model of HD. Both quantitative and dichotomous association analyses implicate a functional variant on ∼32% of chromosomes with the beneficial modifier effect that delays HD motor onset by 0.7 years/allele. Genomic DNA capture and sequencing of a modifier haplotype localize the functional variation to a 78 kb region spanning the 3'end of MLH1 and the 5'end of the neighboring LRRFIP2, and marked by an isoleucine-valine missense variant in MLH1. Analysis of expression Quantitative Trait Loci (eQTLs) provides modest support for altered regulation of MLH1 and LRRFIP2, raising the possibility that the modifier affects regulation of both genes. Finally, polygenic modification score and heritability analyses suggest the existence of additional genetic modifiers, supporting expanded, comprehensive genetic analysis of larger HD datasets.

Population-based identity-by-descent mapping combined with exome sequencing to detect rare risk variants for schizophrenia.

Genome-wide association studies (GWASs) are highly effective at identifying common risk variants for schizophrenia. Rare risk variants are also important contributors to schizophrenia etiology but, with the exception of large copy number variants, are difficult to detect with GWAS. Exome and genome sequencing, which have accelerated the study of rare variants, are expensive so alternative methods are needed to aid detection of rare variants. Here we re-analyze an Irish schizophrenia GWAS dataset (n = 3,473) by performing identity-by-descent (IBD) mapping followed by exome sequencing of individuals identified as sharing risk haplotypes to search for rare risk variants in coding regions. We identified 45 rare haplotypes (>1 cM) that were significantly more common in cases than controls. By exome sequencing 105 haplotype carriers, we investigated these haplotypes for functional coding variants that could be tested for association in independent GWAS samples. We identified one rare missense variant in PCNT but did not find statistical support for an association with schizophrenia in a replication analysis. However, IBD mapping can prioritize both individual samples and genomic regions for follow-up analysis but genome rather than exome sequencing may be more effective at detecting risk variants on rare haplotypes.

Effects of MiR-137 genetic risk score on brain volume and cortical measures in patients with schizophrenia and controls.

Multiple genome-wide association studies of schizophrenia have implicated genetic variants within the gene encoding microRNA-137. As risk variants within or regulated by MIR137 have been implicated in memory performance, we investigated the additive effects of schizophrenia-associated risk variants in genes empirically regulated by MIR137 on brain regions associated with memory function. A polygenic risk score (PRS) was calculated (at a p = 0.05 threshold), using this empirically regulated MIR137 gene set, to investigate associations between this PRS and structural brain measures. These measures included total brain volume, cortical thickness, cortical surface area, and hippocampal volume, in a sample of 216 individuals consisting of healthy participants (n = 171) and patients with psychosis (n = 45). We did not observe a significant association between MIR137 PRS and these cortical thickness, surface area or hippocampal volume measures linked to memory function; a significant association between increasing PRS and decreasing total brain volume, independent of diagnosis status (R2 = 0.008, Beta = -0.09, p = 0.029), was observed. This did not survive correction for multiple testing. In conclusion, our study yielded only suggestive evidence that risk variants interacting with MIR137 impacts on cortical structure.

Shared genetic contribution to Ischaemic Stroke and Alzheimer's Disease.

OBJECTIVE: Increasing evidence suggests epidemiological and pathological links between Alzheimer's disease (AD) and Ischaemic Stroke (IS). We investigated the evidence that shared genetic factors underpin the two diseases. METHODS: Using genome wide association study (GWAS) data from METASTROKE+ (15,916 IS cases and 68,826 controls) and IGAP (17,008 AD cases and 37,154 controls), we evaluated known associations with AD and IS. On the subset of data for which we could obtain compatible genotype-level data (4,610 IS cases, 1,281 AD cases and 14,320 controls), we estimated the genome-wide genetic correlation (rG) between AD and IS, and the three subtypes (cardioembolic, small vessel, large vessel), using genome-wide SNP data. We then performed a meta-analysis and pathway analysis in the combined AD and small vessel stroke datasets to identify the SNPs and molecular pathways through which disease risk may be conferred. RESULTS: We found evidence of a shared genetic contribution between AD and small vessel stroke (rG(SE)=0.37(0.17); p=0.011). Conversely, there was no evidence to support shared genetic factors in AD and IS overall, or with the other stroke subtypes. Of the known GWAS associations with IS or AD, none reached significance for association with the other trait (or stroke subtypes). A meta-analysis of AD IGAP and METASTROKE+ small vessel stroke GWAS data highlighted a region (ATP5H/KCTD2/ICT1), associated with both diseases (p=1.8x10-8 ). A pathway analysis identified four associated pathways, involving cholesterol transport and immune response. INTERPRETATION: Our findings indicate shared genetic susceptibility to AD and small vessel stroke and highlight potential causal pathways and loci. This article is protected by copyright. All rights reserved.

Common polygenic variation enhances risk prediction for Alzheimer's disease.

The identification of subjects at high risk for Alzheimer's disease is important for prognosis and early intervention. We investigated the polygenic architecture of Alzheimer's disease and the accuracy of Alzheimer's disease prediction models, including and excluding the polygenic component in the model. This study used genotype data from the powerful dataset comprising 17 008 cases and 37 154 controls obtained from the International Genomics of Alzheimer's Project (IGAP). Polygenic score analysis tested whether the alleles identified to associate with disease in one sample set were significantly enriched in the cases relative to the controls in an independent sample. The disease prediction accuracy was investigated in a subset of the IGAP data, a sample of 3049 cases and 1554 controls (for whom APOE genotype data were available) by means of sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and positive and negative predictive values. We observed significant evidence for a polygenic component enriched in Alzheimer's disease (P = 4.9 × 10(-26)). This enrichment remained significant after APOE and other genome-wide associated regions were excluded (P = 3.4 × 10(-19)). The best prediction accuracy AUC = 78.2% (95% confidence interval 77-80%) was achieved by a logistic regression model with APOE, the polygenic score, sex and age as predictors. In conclusion, Alzheimer's disease has a significant polygenic component, which has predictive utility for Alzheimer's disease risk and could be a valuable research tool complementing experimental designs, including preventative clinical trials, stem cell selection and high/low risk clinical studies. In modelling a range of sample disease prevalences, we found that polygenic scores almost doubles case prediction from chance with increased prediction at polygenic extremes.

Cognitive analysis of schizophrenia risk genes that function as epigenetic regulators of gene expression.

Epigenetic mechanisms are an important heritable and dynamic means of regulating various genomic functions, including gene expression, to orchestrate brain development, adult neurogenesis, and synaptic plasticity. These processes when perturbed are thought to contribute to schizophrenia pathophysiology. A core feature of schizophrenia is cognitive dysfunction. For genetic disorders where cognitive impairment is more severe such as intellectual disability, there are a disproportionally high number of genes involved in the epigenetic regulation of gene transcription. Evidence now supports some shared genetic aetiology between schizophrenia and intellectual disability. GWAS have identified 108 chromosomal regions associated with schizophrenia risk that span 350 genes. This study identified genes mapping to those loci that have epigenetic functions, and tested the risk alleles defining those loci for association with cognitive deficits. We developed a list of 350 genes with epigenetic functions and cross-referenced this with the GWAS loci. This identified eight candidate genes: BCL11B, CHD7, EP300, EPC2, GATAD2A, KDM3B, RERE, SATB2. Using a dataset of Irish psychosis cases and controls (n = 1235), the schizophrenia risk SNPs at these loci were tested for effects on IQ, working memory, episodic memory, and attention. Strongest associations were for rs6984242 with both measures of IQ (P = 0.001) and episodic memory (P = 0.007). We link rs6984242 to CHD7 via a long range eQTL. These associations were not replicated in independent samples. Our study highlights that a number of genes mapping to risk loci for schizophrenia may function as epigenetic regulators of gene expression but further studies are required to establish a role for these genes in cognition. © 2016 Wiley Periodicals, Inc.

Estimation and partitioning of polygenic variation captured by common SNPs for Alzheimer's disease, multiple sclerosis and endometriosis.

Common diseases such as endometriosis (ED), Alzheimer's disease (AD) and multiple sclerosis (MS) account for a significant proportion of the health care burden in many countries. Genome-wide association studies (GWASs) for these diseases have identified a number of individual genetic variants contributing to the risk of those diseases. However, the effect size for most variants is small and collectively the known variants explain only a small proportion of the estimated heritability. We used a linear mixed model to fit all single nucleotide polymorphisms (SNPs) simultaneously, and estimated genetic variances on the liability scale using SNPs from GWASs in unrelated individuals for these three diseases. For each of the three diseases, case and control samples were not all genotyped in the same laboratory. We demonstrate that a careful analysis can obtain robust estimates, but also that insufficient quality control (QC) of SNPs can lead to spurious results and that too stringent QC is likely to remove real genetic signals. Our estimates show that common SNPs on commercially available genotyping chips capture significant variation contributing to liability for all three diseases. The estimated proportion of total variation tagged by all SNPs was 0.26 (SE 0.04) for ED, 0.24 (SE 0.03) for AD and 0.30 (SE 0.03) for MS. Further, we partitioned the genetic variance explained into five categories by a minor allele frequency (MAF), by chromosomes and gene annotation. We provide strong evidence that a substantial proportion of variation in liability is explained by common SNPs, and thereby give insights into the genetic architecture of the diseases.

A genome-wide study shows a limited contribution of rare copy number variants to Alzheimer's disease risk.

We assessed the role of rare copy number variants (CNVs) in Alzheimer's disease (AD) using intensity data from 3260 AD cases and 1290 age-matched controls from the genome-wide association study (GWAS) conducted by the Genetic and Environmental Risk for Alzheimer's disease Consortium (GERAD). We did not observe a significant excess of rare CNVs in cases, although we did identify duplications overlapping APP and CR1 which may be pathogenic. We looked for an excess of CNVs in loci which have been highlighted in previous AD CNV studies, but did not replicate previous findings. Through pathway analyses, we observed suggestive evidence for biological overlap between single nucleotide polymorphisms and CNVs in AD susceptibility. We also identified that our sample of elderly controls harbours significantly fewer deletions >1 Mb than younger control sets in previous CNV studies on schizophrenia and bipolar disorder (P = 8.9 × 10(-4) and 0.024, respectively), raising the possibility that healthy elderly individuals have a reduced rate of large deletions. Thus, in contrast to diseases such as schizophrenia, autism and attention deficit/hyperactivity disorder, CNVs do not appear to make a significant contribution to the development of AD.

Genetic variation at the CELF1 (CUGBP, elav-like family member 1 gene) locus is genome-wide associated with Alzheimer's disease and obesity.

Deviations from normal body weight are observed prior to and after the onset of Alzheimer's disease (AD). Midlife obesity confers increased AD risk in later life, whereas late-life obesity is associated with decreased AD risk. The role of underweight and weight loss for AD risk is controversial. Based on the hypothesis of shared genetic variants for both obesity and AD, we analyzed the variants identified for AD or obesity from genome-wide association meta-analyses of the GERAD (AD, cases = 6,688, controls = 13,685) and GIANT (body mass index [BMI] as measure of obesity, n = 123,865) consortia. Our cross-disorder analysis of genome-wide significant 39 obesity SNPs and 23 AD SNPs in these two large data sets revealed that: (1) The AD SNP rs10838725 (pAD = 1.1 × 10(-08)) at the locus CELF1 is also genome-wide significant for obesity (pBMI = 7.35 × 10(-09) ). (2) Four additional AD risk SNPs were nominally associated with obesity (rs17125944 at FERMT2, pBMI = 4.03 × 10(-05), pBMI corr = 2.50 × 10(-03) ; rs3851179 at PICALM; pBMI = 0.002, rs2075650 at TOMM40/APOE, pBMI = 0.024, rs3865444 at CD33, pBMI = 0.024). (3) SNPs at two of the obesity risk loci (rs4836133 downstream of ZNF608; pAD = 0.002 and at rs713586 downstream of RBJ/DNAJC27; pAD = 0.018) were nominally associated with AD risk. Additionally, among the SNPs used for confirmation in both studies the AD risk allele of rs1858973, with an AD association just below genome-wide significance (pAD = 7.20 × 10(-07)), was also associated with obesity (SNP at IQCK/GPRC5B; pBMI = 5.21 × 10(-06) ; pcorr = 3.24 × 10(-04)). Our first GWAS based cross-disorder analysis for AD and obesity suggests that rs10838725 at the locus CELF1 might be relevant for both disorders.

Vitamin B12 status and folic acid supplementation influence mitochondrial heteroplasmy levels in mice.

One-carbon metabolism is a complex network of metabolic reactions that are essential for cellular function including DNA synthesis. Vitamin B12 and folate are micronutrients that are utilized in this pathway and their deficiency can result in the perturbation of one-carbon metabolism and subsequent perturbations in DNA replication and repair. This effect has been well characterized in nuclear DNA but to date, mitochondrial DNA (mtDNA) has not been investigated extensively. Mitochondrial variants have been associated with several inherited and age-related disease states; therefore, the study of factors that impact heteroplasmy are important for advancing our understanding of the mitochondrial genome's impact on human health. Heteroplasmy studies require robust and efficient mitochondrial DNA enrichment to carry out in-depth mtDNA sequencing. Many of the current methods for mtDNA enrichment can introduce biases and false-positive results. Here, we use a method that overcomes these limitations and have applied it to assess mitochondrial heteroplasmy in mouse models of altered one-carbon metabolism. Vitamin B12 deficiency was found to cause increased levels of mitochondrial DNA heteroplasmy across all tissues that were investigated. Folic acid supplementation also contributed to elevated mitochondrial DNA heteroplasmy across all mouse tissues investigated. Heteroplasmy analysis of human data from the Framingham Heart Study suggested a potential sex-specific effect of folate and vitamin B12 status on mitochondrial heteroplasmy. This is a novel relationship that may have broader consequences for our understanding of one-carbon metabolism, mitochondrial-related disease and the influence of nutrients on DNA mutation rates.

SNPs associated with cerebrospinal fluid phospho-tau levels influence rate of decline in Alzheimer's disease.

Alzheimer's Disease (AD) is a complex and multifactorial disease. While large genome-wide association studies have had some success in identifying novel genetic risk factors for AD, case-control studies are less likely to uncover genetic factors that influence progression of disease. An alternative approach to identifying genetic risk for AD is the use of quantitative traits or endophenotypes. The use of endophenotypes has proven to be an effective strategy, implicating genetic risk factors in several diseases, including anemia, osteoporosis and heart disease. In this study we identify a genetic factor associated with the rate of decline in AD patients and present a methodology for identification of other such factors. We have used an established biomarker for AD, cerebrospinal fluid (CSF) tau phosphorylated at threonine 181 (ptau(181)) levels as an endophenotype for AD, identifying a SNP, rs1868402, in the gene encoding the regulatory sub-unit of protein phosphatase B, associated with CSF ptau(181) levels in two independent CSF series (P(combined) = 1.17 x 10(-05)). We show no association of rs1868402 with risk for AD or age at onset, but detected a very significant association with rate of progression of disease that is consistent in two independent series (P(combined) = 1.17 x 10(-05)). Our analyses suggest that genetic variants associated with CSF ptau(181) levels may have a greater impact on rate of progression, while genetic variants such as APOE4, that are associated with CSF Aß(42) levels influence risk and onset but not the rate of progression. Our results also suggest that drugs that inhibit or decrease tau phosphorylation may slow cognitive decline in individuals with very mild dementia or delay the appearance of memory problems in elderly individuals with low CSF Aß(42) levels. Finally, we believe genome-wide association studies of CSF tau/ptau(181) levels should identify novel genetic variants which will likely influence rate of progression of AD.

Mito-SiPE is a sequence-independent and PCR-free mtDNA enrichment method for accurate ultra-deep mitochondrial sequencing.

The analysis of somatic variation in the mitochondrial genome requires deep sequencing of mitochondrial DNA. This is ordinarily achieved by selective enrichment methods, such as PCR amplification or probe hybridization. These methods can introduce bias and are prone to contamination by nuclear-mitochondrial sequences (NUMTs), elements that can introduce artefacts into heteroplasmy analysis. We isolated intact mitochondria using differential centrifugation and alkaline lysis and subjected purified mitochondrial DNA to a sequence-independent and PCR-free method to obtain ultra-deep (>80,000X) sequencing coverage of the mitochondrial genome. This methodology avoids false-heteroplasmy calls that occur when long-range PCR amplification is used for mitochondrial DNA enrichment. Previously published methods employing mitochondrial DNA purification did not measure mitochondrial DNA enrichment or utilise high coverage short-read sequencing. Here, we describe a protocol that yields mitochondrial DNA and have quantified the increased level of mitochondrial DNA post-enrichment in 7 different mouse tissues. This method will enable researchers to identify changes in low frequency heteroplasmy without introducing PCR biases or NUMT contamination that are incorrectly identified as heteroplasmy when long-range PCR is used.

Genetic evidence for the involvement of lipid metabolism in Alzheimer's disease.

Alzheimer's disease (AD) is the most common cause of dementia in the elderly and presents a great burden to sufferers and to society. The genetics of rare Mendelian forms of AD have been central to our understanding of AD pathogenesis for the past twenty years and now the genetics of the common form of the disease in the elderly is beginning to be unravelled by genome-wide association studies. Four new genes for common AD have been revealed in the past year, CLU, CR1, PICALM and BIN1. Their possible involvement in lipid metabolism and how that relates to AD is discussed here.

Alzheimer's disease genetics: current knowledge and future challenges.

Alzheimer's disease (AD) is highly heritable, but genetically complex. Recently, three large-scale genome-wide association studies have made substantial breakthroughs in disentangling the genetic architecture of the disease. These studies combined include data from over 43 000 independent individuals and provide compelling evidence that variants in four novel susceptibility genes (CLU, PICALM, CR1, BIN1) are associated with disease risk. These findings are tremendously exciting, not only in providing new avenues for exploration, but also highlighting the potential for further gene discovery when larger samples are analysed. Here we discuss progress to date in identifying risk genes for dementia, ways forward and how current findings are refining previous ideas and defining new putative primary disease mechanisms.

No evidence that extended tracts of homozygosity are associated with Alzheimer's disease.

We sought to investigate the contribution of extended runs of homozygosity in a genome-wide association dataset of 1,955 Alzheimer's disease cases and 955 elderly screened controls genotyped for 529,205 autosomal single nucleotide polymorphisms. Tracts of homozygosity may mark regions inherited from a common ancestor and could reflect disease loci if observed more frequently in cases than controls. We found no excess of homozygous tracts in Alzheimer's disease cases compared to controls and no individual run of homozygosity showed association to Alzheimer's disease.

The Differential Influence of Immune, Endocytotic, and Lipid Metabolism Genes on Amyloid Deposition and Neurodegeneration in Subjects at Risk of Alzheimer's Disease.

BACKGROUND: Over 20 single-nucleotide polymorphisms (SNPs) are associated with increased risk of Alzheimer's disease (AD). We categorized these loci into immunity, lipid metabolism, and endocytosis pathways, and associated the polygenic risk scores (PRS) calculated, with AD biomarkers in mild cognitive impairment (MCI) subjects. OBJECTIVE: The aim of this study was to identify associations between pathway-specific PRS and AD biomarkers in patients with MCI and healthy controls. METHODS: AD biomarkers ([18F]Florbetapir-PET SUVR, FDG-PET SUVR, hippocampal volume, CSF tau and amyloid-ß levels) and neurocognitive tests scores were obtained in 258 healthy controls and 451 MCI subjects from the ADNI dataset at baseline and at 24-month follow up. Pathway-related (immunity, lipid metabolism, and endocytosis) and total polygenic risk scores were calculated from 20 SNPs. Multiple linear regression analysis was used to test predictive value of the polygenic risk scores over longitudinal biomarker and cognitive changes. RESULTS: Higher immune risk score was associated with worse cognitive measures and reduced glucose metabolism. Higher lipid risk score was associated with increased amyloid deposition and cortical hypometabolism. Total, immune, and lipid scores were associated with significant changes in cognitive measures, amyloid deposition, and brain metabolism. CONCLUSION: Polygenic risk scores highlights the influence of specific genes on amyloid-dependent and independent pathways; and these pathways could be differentially influenced by lipid and immune scores respectively.

A review of pharmaceutical occurrence and pathways in the aquatic environment in the context of a changing climate and the COVID-19 pandemic.

Active pharmaceutical ingredients (APIs) are increasingly being identified as contaminants of emerging concern (CECs). They have potentially detrimental ecological and human health impacts but most are not currently subject to environmental regulation. Addressing the life cycle of these pharmaceuticals plays a significant role in identifying the potential sources and understanding the environmental impact that pharmaceuticals may have in surface waters. The stability and biological activity of these "micro-pollutants" can lead to a pseudo persistence, with ensuing unknown chronic behavioural and health-related effects. Research that investigates pharmaceuticals predominantly focuses on their occurrence and effect within surface water environments. However, this review will help to collate this information with factors that affect their environmental concentration. This review focuses on six pharmaceuticals (clarithromycin, ciprofloxacin, sulfamethoxazole, venlafaxine, gemfibrozil and diclofenac), chosen because they are heavily consumed globally, have poor removal rates in conventional activated sludge wastewater treatment plants (CAS WWTPs), and are persistent in the aquatic environment. Furthermore, these pharmaceuticals are included in numerous published prioritisation studies and/or are on the Water Framework Directive (WFD) "Watch List" or are candidates for the updated Watch List (WL). This review investigates the concentrations seen in European Union (EU) surface waters and examines factors that influence final concentrations prior to release, thus giving a holistic overview on the source of pharmaceutical surface water pollution. A period of 10 years is covered by this review, which includes research from 2009-2020 examining over 100 published studies, and highlighting that pharmaceuticals can pose a severe risk to surface water environments, with each stage of the lifecycle of the pharmaceutical determining its concentration. This review additionally highlights the necessity to improve education surrounding appropriate use, disposal and waste management of pharmaceuticals, while implementing a source directed and end of pipe approach to reduce pharmaceutical occurrence in surface waters.

Monitoring of emerging contaminants of concern in the aquatic environment: a review of studies showing the application of effect-based measures.

Water scarcity is increasingly a global cause of concern mainly due to widespread changes in climate conditions and increased consumptive water use driven by the exponential increase in population growth. In addition, increased pollution of fresh water sources due to rising production and consumption of pharmaceuticals and organic chemicals will further exacerbate this concern. Although surface water contamination by individual chemicals is often at very low concentration, pharmaceuticals for instance are designed to be efficacious at low concentrations, creating genuine concern for their presence in freshwater sources. Furthermore, the additive impact of multiple compounds may result in toxic or other biological effects that otherwise will not be induced by individual chemicals. Globally, different legislative frameworks have led to pre-emptive efforts which aim to ensure good water ecological status. Reports detailing the use and types of effect-based measures covering specific bioassay batteries that can identify specific mode of actions of chemical pollutants in the aquatic ecosystem to evaluate the real threat of pollutants to aquatic lives and ultimately human lives have recently emerged from monitoring networks such as the NORMAN network. In this review, we critically evaluate some studies within the last decade that have implemented effect-based monitoring of pharmaceuticals and organic chemicals in aquatic fauna, evaluating the occurrence of different chemical pollutants and the impact of these pollutants on aquatic fauna with special focus on pollutants that are contaminants of emerging concern (CEC) in urban wastewater. A critical discussion on studies that have used effect-based measures to assess biological impact of pharmaceutical/organic compound in the aquatic ecosystem and the endpoints measurements employed is presented. The application of effect-based monitoring of chemicals other than assessment of water quality status is also discussed.