DNAscan: personal computer compatible NGS analysis, annotation and visualisation.

BACKGROUND: Next Generation Sequencing (NGS) is a commonly used technology for studying the genetic basis of biological processes and it underpins the aspirations of precision medicine. However, there are significant challenges when dealing with NGS data. Firstly, a huge number of bioinformatics tools for a wide range of uses exist, therefore it is challenging to design an analysis pipeline. Secondly, NGS analysis is computationally intensive, requiring expensive infrastructure, and many medical and research centres do not have adequate high performance computing facilities and cloud computing is not always an option due to privacy and ownership issues. Finally, the interpretation of the results is not trivial and most available pipelines lack the utilities to favour this crucial step. RESULTS: We have therefore developed a fast and efficient bioinformatics pipeline that allows for the analysis of DNA sequencing data, while requiring little computational effort and memory usage. DNAscan can analyse a whole exome sequencing sample in 1 h and a 40x whole genome sequencing sample in 13 h, on a midrange computer. The pipeline can look for single nucleotide variants, small indels, structural variants, repeat expansions and viral genetic material (or any other organism). Its results are annotated using a customisable variety of databases and are available for an on-the-fly visualisation with a local deployment of the gene.iobio platform. DNAscan is implemented in Python. Its code and documentation are available on GitHub: https://github.com/KHP-Informatics/DNAscan . Instructions for an easy and fast deployment with Docker and Singularity are also provided on GitHub. CONCLUSIONS: DNAscan is an extremely fast and computationally efficient pipeline for analysis, visualization and interpretation of NGS data. It is designed to provide a powerful and easy-to-use tool for applications in biomedical research and diagnostic medicine, at minimal computational cost. Its comprehensive approach will maximise the potential audience of users, bringing such analyses within the reach of non-specialist laboratories, and those from centres with limited funding available.

A genome-wide association study for extremely high intelligence.

We used a case-control genome-wide association (GWA) design with cases consisting of 1238 individuals from the top 0.0003 (~170 mean IQ) of the population distribution of intelligence and 8172 unselected population-based controls. The single-nucleotide polymorphism heritability for the extreme IQ trait was 0.33 (0.02), which is the highest so far for a cognitive phenotype, and significant genome-wide genetic correlations of 0.78 were observed with educational attainment and 0.86 with population IQ. Three variants in locus ADAM12 achieved genome-wide significance, although they did not replicate with published GWA analyses of normal-range IQ or educational attainment. A genome-wide polygenic score constructed from the GWA results accounted for 1.6% of the variance of intelligence in the normal range in an unselected sample of 3414 individuals, which is comparable to the variance explained by GWA studies of intelligence with substantially larger sample sizes. The gene family plexins, members of which are mutated in several monogenic neurodevelopmental disorders, was significantly enriched for associations with high IQ. This study shows the utility of extreme trait selection for genetic study of intelligence and suggests that extremely high intelligence is continuous genetically with normal-range intelligence in the population.

Multi-polygenic score approach to trait prediction.

A primary goal of polygenic scores, which aggregate the effects of thousands of trait-associated DNA variants discovered in genome-wide association studies (GWASs), is to estimate individual-specific genetic propensities and predict outcomes. This is typically achieved using a single polygenic score, but here we use a multi-polygenic score (MPS) approach to increase predictive power by exploiting the joint power of multiple discovery GWASs, without assumptions about the relationships among predictors. We used summary statistics of 81 well-powered GWASs of cognitive, medical and anthropometric traits to predict three core developmental outcomes in our independent target sample: educational achievement, body mass index (BMI) and general cognitive ability. We used regularized regression with repeated cross-validation to select from and estimate contributions of 81 polygenic scores in a UK representative sample of 6710 unrelated adolescents. The MPS approach predicted 10.9% variance in educational achievement, 4.8% in general cognitive ability and 5.4% in BMI in an independent test set, predicting 1.1%, 1.1%, and 1.6% more variance than the best single-score predictions. As other relevant GWA analyses are reported, they can be incorporated in MPS models to maximize phenotype prediction. The MPS approach should be useful in research with modest sample sizes to investigate developmental, multivariate and gene-environment interplay issues and, eventually, in clinical settings to predict and prevent problems using personalized interventions.

New insights into the pharmacogenomics of antidepressant response from the GENDEP and STAR*D studies: rare variant analysis and high-density imputation.

Genome-wide association studies have generally failed to identify polymorphisms associated with antidepressant response. Possible reasons include limited coverage of genetic variants that this study tried to address by exome genotyping and dense imputation. A meta-analysis of Genome-Based Therapeutic Drugs for Depression (GENDEP) and Sequenced Treatment Alternatives to Relieve Depression (STAR*D) studies was performed at the single-nucleotide polymorphism (SNP), gene and pathway levels. Coverage of genetic variants was increased compared with previous studies by adding exome genotypes to previously available genome-wide data and using the Haplotype Reference Consortium panel for imputation. Standard quality control was applied. Phenotypes were symptom improvement and remission after 12 weeks of antidepressant treatment. Significant findings were investigated in NEWMEDS consortium samples and Pharmacogenomic Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) for replication. A total of 7062 950 SNPs were analyzed in GENDEP (n=738) and STAR*D (n=1409). rs116692768 (P=1.80e-08, ITGA9 (integrin α9)) and rs76191705 (P=2.59e-08, NRXN3 (neurexin 3)) were significantly associated with symptom improvement during citalopram/escitalopram treatment. At the gene level, no consistent effect was found. At the pathway level, the Gene Ontology (GO) terms GO: 0005694 (chromosome) and GO: 0044427 (chromosomal part) were associated with improvement (corrected P=0.007 and 0.045, respectively). The association between rs116692768 and symptom improvement was replicated in PGRN-AMPS (P=0.047), whereas rs76191705 was not. The two SNPs did not replicate in NEWMEDS. ITGA9 codes for a membrane receptor for neurotrophins and NRXN3 is a transmembrane neuronal adhesion receptor involved in synaptic differentiation. Despite their meaningful biological rationale for being involved in antidepressant effect, replication was partial. Further studies may help in clarifying their role.

Widespread covariation of early environmental exposures and trait-associated polygenic variation.

Although gene-environment correlation is recognized and investigated by family studies and recently by SNP-heritability studies, the possibility that genetic effects on traits capture environmental risk factors or protective factors has been neglected by polygenic prediction models. We investigated covariation between trait-associated polygenic variation identified by genome-wide association studies (GWASs) and specific environmental exposures, controlling for overall genetic relatedness using a genomic relatedness matrix restricted maximum-likelihood model. In a UK-representative sample (n = 6,710), we find widespread covariation between offspring trait-associated polygenic variation and parental behavior and characteristics relevant to children's developmental outcomes-independently of population stratification. For instance, offspring genetic risk for schizophrenia was associated with paternal age (R2 = 0.002; P = 1e-04), and offspring education-associated variation was associated with variance in breastfeeding (R2 = 0.021; P = 7e-30), maternal smoking during pregnancy (R2 = 0.008; P = 5e-13), parental smacking (R2 = 0.01; P = 4e-15), household income (R2 = 0.032; P = 1e-22), watching television (R2 = 0.034; P = 5e-47), and maternal education (R2 = 0.065; P = 3e-96). Education-associated polygenic variation also captured covariation between environmental exposures and children's inattention/hyperactivity, conduct problems, and educational achievement. The finding that genetic variation identified by trait GWASs partially captures environmental risk factors or protective factors has direct implications for risk prediction models and the interpretation of GWAS findings.

Plasma protein biomarkers of Alzheimer's disease endophenotypes in asymptomatic older twins: early cognitive decline and regional brain volumes.

There is great interest in blood-based markers of Alzheimer's disease (AD), especially in its pre-symptomatic stages. Therefore, we aimed to identify plasma proteins whose levels associate with potential markers of pre-symptomatic AD. We also aimed to characterise confounding by genetics and the effect of genetics on blood proteins in general. Panel-based proteomics was performed using SOMAscan on plasma samples from TwinsUK subjects who are asymptomatic for AD, measuring the level of 1129 proteins. Protein levels were compared with 10-year change in CANTAB-paired associates learning (PAL; n = 195), and regional brain volumes (n = 34). Replication of proteins associated with regional brain volumes was performed in 254 individuals from the AddNeuroMed cohort. Across all the proteins measured, genetic factors were found to explain ~26% of the variability in blood protein levels on average. The plasma level of the mitogen-activated protein kinase (MAPK) MAPKAPK5 protein was found to positively associate with the 10-year change in CANTAB-PAL in both the individual and twin difference context. The plasma level of protein MAP2K4 was found to suggestively associate negatively (Q < 0.1) with the volume of the left entorhinal cortex. Future studies will be needed to assess the specificity of MAPKAPK5 and MAP2K4 to eventual conversion to AD.

Pharmacogenetics of pemetrexed combination therapy in lung cancer: pathway analysis reveals novel toxicity associations.

Identification of polymorphisms that influence pemetrexed tolerability could lead to individualised treatment regimens and improve quality of life. Twenty-eight polymorphisms within eleven candidate genes were genotyped using the Illumina Human Exome v1.1 BeadChip and tested for their association with the clinical outcomes of non-small cell lung cancer and mesothelioma patients receiving pemetrexed/platinum doublet chemotherapy (n=136). GGH rs11545078 was associated with a reduced incidence of grade ⩾3 toxicity within the first four cycles of therapy (odds ratio (OR) 0.25, P=0.018), as well as reduced grade ⩾3 haematological toxicity (OR 0.13, P=0.048). DHFR rs1650697 conferred an increased risk of grade ⩾3 toxicity (OR 2.14, P=0.034). Furthermore, FOLR3 rs61734430 was associated with an increased likelihood of disease progression at mid-treatment radiological evaluation (OR 4.05, P=0.023). Polymorphisms within SLC19A1 (rs3788189, rs1051298 and rs914232) were associated with overall survival. This study confirms previous pharmacogenetic associations and identifies novel markers of pemetrexed toxicity.

In vitro differentiation of villous trophoblasts from pregnancies complicated by intrauterine growth restriction with and without pre-eclampsia.

Highly purified (>99.99%) primary villous cytotrophoblasts from uncomplicated pregnancies and pregnancies complicated with intrauterine growth restriction (IUGR) alone, IUGR with pre-eclampsia (IUGR-PE) and PE alone were cultured for 5days and the extent of differentiation into syncytiotrophoblasts measured in terms of syncytialisation and secretion of chorionic gonadotropin (hCG) and placental lactogen (hPL). Three separate phenotypes were observed: (1) normal and IUGR-PE cells showed low syncytialisation and secretion of hCG and hPL, (2) IUGR cells showed the highest levels of syncytialisation and secretion and (3) PE cells showed high syncytialisation but low secretion. These results strongly suggest IUGR, IUGR-PE and PE to be distinct conditions in which villous cytotrophoblasts are either exposed to different environments or are genetically different.

A local-scale in situ approach for stressor identification of biologically impaired aquatic systems.

We propose a formal causal evaluation inference process that uses a local-scale in situ approach and provides statistically defensible correlative conclusions for the biological impairment in 303(d)-listed water bodies. Fish assemblage, water chemistry, and aquatic habitat data from 66 locations in the Limberlost watershed, Indiana, were collected in August 2003 to evaluate the condition of the watershed. Anthropogenic stressors were identified from a numerical classification analysis of fish assemblage data using a biological integrity response gradient. Physical and chemical stressors were evaluated relative to the biological integrity response gradient using the Kruskal Wallis analysis of variance by ranks test. Three water chemistry variables (sodium, chloride, and barium) were associated with biological integrity. The percent run habitat, channel morphology score, and Qualitative Habitat Evaluation Index score varied with the degree of dredging. We observed a negative response between the number of species and the biological integrity gradient and a positive response with the percent tolerant individuals. This local-scale in situ approach to physical and chemical data analysis, combined with concurrent biological data collection, provided correlative relations to the impaired condition with localized environmental stressors.

Genes and hypertension.

The combination of investigation of rare Mendelian forms of hypertension, candidate gene studies, comparative mapping and genome-wide screening in both animal models and man has led to significant progress in determining new mechanisms of blood pressure control. In this review, the newly discovered blood pressure/cardiovascular genes, WNK kinases and angiotensin converting enzyme 2 and the development of a new anti-hypertensive agent PST2238 are discussed. Major genes causing essential hypertension have yet to be discovered, however, there are now over 20 published genome-wide screens for blood pressure controlling genes. Several regions demonstrate suggestive linkage to the trait and there is some overlap of regions between the different studies. It is hoped that new blood pressure genes will ultimately be discovered using this method. Pharmacogenetic studies in hypertension have only been initiated recently, some are described in this paper. Small studies upon single candidate genes, suggest that the contribution of genetics to the inter-individual variation in blood pressure response to anti-hypertensive therapy, is small, approximately 3-5%. Recently micro-arrays with multiple polymorphisms in multiple genes have been used. After accounting for the additive affects of multiple blood pressure loci, an individual's genetic profile appeared to explain up to 50% of the variation in blood pressure response to therapy. Knowledge of the genetic variants that cause hypertension and influence response to anti-hypertensive therapy will ultimately provide a greater understanding of the molecular mechanisms underlying blood pressure control.