Nathan L. Tintle

The mental health of clean-up workers 18 years after the Chernobyl accident

BACKGROUND The psychological aftermath of the Chernobyl accident is regarded as the largest public health problem unleashed by the accident to date. Yet the mental health of the clean-up workers, who faced the greatest radiation exposure and threat to life, has not been systematically evaluated. This study describes the long-term psychological effects of Chernobyl in a sample of clean-up workers in Ukraine. METHOD The cohorts were 295 male clean-up workers sent to Chernobyl between 1986 and 1990 interviewed 18 years after the accident (71% participation rate) and 397 geographically matched controls interviewed as part of the Ukraine World Mental Health (WMS) Survey 16 years after the accident. The World Health Organization (WHO) Composite International Diagnostic Interview (CIDI) was administered. We examined group differences in common psychiatric disorders, suicide ideation and severe headaches, differential effects of disorder on days lost from work, and in the clean-up workers, the relationship of exposure severity to disorder and current trauma and somatic symptoms. Analyses were adjusted for age in 1986 and mental health prior to the accident. RESULTS Relatively more clean-up workers than controls experienced depression (18.0% v. 13.1%) and suicide ideation (9.2% v. 4.1%) after the accident. In the year preceding interview, the rates of depression (14.9% v. 7.1%), post-traumatic stress disorder (PTSD) (4.1% v. 1.0%) and headaches (69.2% v. 12.4%) were elevated. Affected workers lost more work days than affected controls. Exposure level was associated with current somatic and PTSD symptom severity. CONCLUSIONS Long-term mental health consequences of Chernobyl were observed in clean-up workers.

Machine learning and data mining in complex genomic data—a review on the lessons learned in Genetic Analysis Workshop 19

In the analysis of current genomic data, application of machine learning and data mining techniques has become more attractive given the rising complexity of the projects. As part of the Genetic Analysis Workshop 19, approaches from this domain were explored, mostly motivated from two starting points. First, assuming an underlying structure in the genomic data, data mining might identify this and thus improve downstream association analyses. Second, computational methods for machine learning need to be developed further to efficiently deal with the current wealth of data.In the course of discussing results and experiences from the machine learning and data mining approaches, six common messages were extracted. These depict the current state of these approaches in the application to complex genomic data. Although some challenges remain for future studies, important forward steps were taken in the integration of different data types and the evaluation of the evidence. Mining the data for underlying genetic or phenotypic structure and using this information in subsequent analyses proved to be extremely helpful and is likely to become of even greater use with more complex data sets.

Development and assessment of a preliminary randomization-based introductory statistics curriculum

The algebra-based introductory statistics course is the most popular undergraduate course in statistics. While there is a general consensus for the content of the curriculum, the recent Guidelines for Assessment and Instruction in Statistics Education (GAISE) have challenged the pedagogy of this course. Additionally, some arguments have been made that the curriculum should focus on a randomization approach to statistical inference instead of using asymptotic tests. We developed a preliminary version of a randomization based curriculum which we then implemented with 240 students in eight sections of introductory statistics in fall 2009. The Comprehensive Assessment of Outcomes in Statistics (CAOS) assessment test was administered to these students and showed that students learned significantly more about statistical inference using the new curriculum, with comparable learning on most other questions. The assessment results demonstrate that refining content, improving pedagogy and rethinking the consensus curriculum can significantly improve student learning. We will continue to refine both content and pedagogy resulting in improved student learning gains on CAOS items and other assessment measures.

Inference of the Transcriptional Regulatory Network in Staphylococcus aureus by Integration of Experimental and Genomics-Based Evidence

Transcriptional regulatory networks are fine-tuned systems that help microorganisms respond to changes in the environment and cell physiological state. We applied the comparative genomics approach implemented in the RegPredict Web server combined with SEED subsystem analysis and available information on known regulatory interactions for regulatory network reconstruction for the human pathogen Staphylococcus aureus and six related species from the family Staphylococcaceae. The resulting reference set of 46 transcription factor regulons contains more than 1,900 binding sites and 2,800 target genes involved in the central metabolism of carbohydrates, amino acids, and fatty acids; respiration; the stress response; metal homeostasis; drug and metal resistance; and virulence. The inferred regulatory network in S. aureus includes ∼320 regulatory interactions between 46 transcription factors and ∼550 candidate target genes comprising 20% of its genome. We predicted ∼170 novel interactions and 24 novel regulons for the control of the central metabolic pathways in S. aureus. The reconstructed regulons are largely variable in the Staphylococcaceae: only 20% of S. aureus regulatory interactions are conserved across all studied genomes. We used a large-scale gene expression data set for S. aureus to assess relationships between the inferred regulons and gene expression patterns. The predicted reference set of regulons is captured within the Staphylococcus collection in the RegPrecise database (http://regprecise.lbl.gov).

Comparing gene set analysis methods on single-nucleotide polymorphism data from Genetic Analysis Workshop 16

Recently, gene set analysis (GSA) has been extended from use on gene expression data to use on single-nucleotide polymorphism (SNP) data in genome-wide association studies. When GSA has been demonstrated on SNP data, two popular statistics from gene expression data analysis (gene set enrichment analysis [GSEA] and Fishers exact test [FET]) have been used. However, GSEA and FET have shown a lack of power and robustness in the analysis of gene expression data. The purpose of this work is to investigate whether the same issues are also true for the analysis of SNP data. Ultimately, we conclude that GSEA and FET are not optimal for the analysis of SNP data when compared with the SUMSTAT method. In analysis of real SNP data from the Framingham Heart Study, we find that SUMSTAT finds many more gene sets to be significant when compared with other methods. In an analysis of simulated data, SUMSTAT demonstrates high power and better control of the type I error rate. GSA is a promising approach to the analysis of SNP data in GWAS and use of the SUMSTAT statistic instead of GSEA or FET may increase power and robustness.

A genome-wide association study of saturated, mono- and polyunsaturated red blood cell fatty acids in the Framingham Heart Offspring Study.

Most genome-wide association studies have explored relationships between genetic variants and plasma phospholipid fatty acid proportions, but few have examined apparent genetic influences on the membrane fatty acid profile of red blood cells (RBC). Using RBC fatty acid data from the Framingham Offspring Study, we analyzed over 2.5 million single nucleotide polymorphisms (SNPs) for association with 14 RBC fatty acids identifying 191 different SNPs associated with at least 1 fatty acid. Significant associations (p<1×10(-8)) were located within five distinct 1MB regions. Of particular interest were novel associations between (1) arachidonic acid and PCOLCE2 (regulates apoA-I maturation and modulates apoA-I levels), and (2) oleic and linoleic acid and LPCAT3 (mediates the transfer of fatty acids between glycerolipids). We also replicated previously identified strong associations between SNPs in the FADS (chromosome 11) and ELOVL (chromosome 6) regions. Multiple SNPs explained 8-14% of the variation in 3 high abundance (>11%) fatty acids, but only 1-3% in 4 low abundance (<3%) fatty acids, with the notable exception of dihomo-gamma linolenic acid with 53% of variance explained by SNPs. Further studies are needed to determine the extent to which variations in these genes influence tissue fatty acid content and pathways modulated by fatty acids.

Evaluating methods for the analysis of rare variants in sequence data

A number of rare variant statistical methods have been proposed for analysis of the impending wave of next-generation sequencing data. To date, there are few direct comparisons of these methods on real sequence data. Furthermore, there is a strong need for practical advice on the proper analytic strategies for rare variant analysis. We compare four recently proposed rare variant methods (combined multivariate and collapsing, weighted sum, proportion regression, and cumulative minor allele test) on simulated phenotype and next-generation sequencing data as part of Genetic Analysis Workshop 17. Overall, we find that all analyzed methods have serious practical limitations on identifying causal genes. Specifically, no method has more than a 5% true discovery rate (percentage of truly causal genes among all those identified as significantly associated with the phenotype). Further exploration shows that all methods suffer from inflated false-positive error rates (chance that a noncausal gene will be identified as associated with the phenotype) because of population stratification and gametic phase disequilibrium between noncausal SNPs and causal SNPs. Furthermore, observed true-positive rates (chance that a truly causal gene will be identified as significantly associated with the phenotype) for each of the four methods was very low (<19%). The combination of larger than anticipated false-positive rates, low true-positive rates, and only about 1% of all genes being causal yields poor discriminatory ability for all four methods. Gametic phase disequilibrium and population stratification are important areas for further research in the analysis of rare variant data.

Omega-6 fatty acid biomarkers and incident type 2 diabetes: Pooled analysis of individual-level data for 39 740 adults from 20 prospective cohort studies

BACKGROUND The metabolic effects of omega-6 polyunsaturated fatty acids (PUFAs) remain contentious, and little evidence is available regarding their potential role in primary prevention of type 2 diabetes. We aimed to assess the associations of linoleic acid and arachidonic acid biomarkers with incident type 2 diabetes. METHODS We did a pooled analysis of new, harmonised, individual-level analyses for the biomarkers linoleic acid and its metabolite arachidonic acid and incident type 2 diabetes. We analysed data from 20 prospective cohort studies from ten countries (Iceland, the Netherlands, the USA, Taiwan, the UK, Germany, Finland, Australia, Sweden, and France), with biomarkers sampled between 1970 and 2010. Participants included in the analyses were aged 18 years or older and had data available for linoleic acid and arachidonic acid biomarkers at baseline. We excluded participants with type 2 diabetes at baseline. The main outcome was the association between omega-6 PUFA biomarkers and incident type 2 diabetes. We assessed the relative risk of type 2 diabetes prospectively for each cohort and lipid compartment separately using a prespecified analytic plan for exposures, covariates, effect modifiers, and analysis, and the findings were then pooled using inverse-variance weighted meta-analysis. FINDINGS Participants were 39 740 adults, aged (range of cohort means) 49-76 years with a BMI (range of cohort means) of 23·3-28·4 kg/m2, who did not have type 2 diabetes at baseline. During a follow-up of 366 073 person-years, we identified 4347 cases of incident type 2 diabetes. In multivariable-adjusted pooled analyses, higher proportions of linoleic acid biomarkers as percentages of total fatty acid were associated with a lower risk of type 2 diabetes overall (risk ratio [RR] per interquintile range 0·65, 95% CI 0·60-0·72, p<0·0001; I2=53·9%, pheterogeneity=0·002). The associations between linoleic acid biomarkers and type 2 diabetes were generally similar in different lipid compartments, including phospholipids, plasma, cholesterol esters, and adipose tissue. Levels of arachidonic acid biomarker were not significantly associated with type 2 diabetes risk overall (RR per interquintile range 0·96, 95% CI 0·88-1·05; p=0·38; I2=63·0%, pheterogeneity<0·0001). The associations between linoleic acid and arachidonic acid biomarkers and the risk of type 2 diabetes were not significantly modified by any prespecified potential sources of heterogeneity (ie, age, BMI, sex, race, aspirin use, omega-3 PUFA levels, or variants of the FADS gene; all pheterogeneity≥0·13). INTERPRETATION Findings suggest that linoleic acid has long-term benefits for the prevention of type 2 diabetes and that arachidonic acid is not harmful. FUNDING Funders are shown in the appendix.

Assessing Methods for Assigning SNPs to Genes in Gene-Based Tests of Association Using Common Variants

Gene-based tests of association are frequently applied to common SNPs (MAF>5%) as an alternative to single-marker tests. In this analysis we conduct a variety of simulation studies applied to five popular gene-based tests investigating general trends related to their performance in realistic situations. In particular, we focus on the impact of non-causal SNPs and a variety of LD structures on the behavior of these tests. Ultimately, we find that non-causal SNPs can significantly impact the power of all gene-based tests. On average, we find that the “noise” from 6–12 non-causal SNPs will cancel out the “signal” of one causal SNP across five popular gene-based tests. Furthermore, we find complex and differing behavior of the methods in the presence of LD within and between non-causal and causal SNPs. Ultimately, better approaches for a priori prioritization of potentially causal SNPs (e.g., predicting functionality of non-synonymous SNPs), application of these methods to sequenced or fully imputed datasets, and limited use of window-based methods for assigning inter-genic SNPs to genes will improve power. However, significant power loss from non-causal SNPs may remain unless alternative statistical approaches robust to the inclusion of non-causal SNPs are developed.

Inflated Type I Error Rates When Using Aggregation Methods to Analyze Rare Variants in the 1000 Genomes Project Exon Sequencing Data in Unrelated Individuals: Summary Results from Group 7 at Genetic Analysis Workshop 17

As part of Genetic Analysis Workshop 17 (GAW17), our group considered the application of novel and standard approaches to the analysis of genotype‐phenotype association in next‐generation sequencing data. Our group identified a major issue in the analysis of the GAW17 next‐generation sequencing data: type I error and false‐positive report probability rates higher than those expected based on empirical type I error levels (as high as 90%). Two main causes emerged: population stratification and long‐range correlation (gametic phase disequilibrium) between rare variants. Population stratification was expected because of the diverse sample. Correlation between rare variants was attributable to both random causes (e.g., nearly 10,000 of 25,000 markers were private variants, and the sample size was small [n = 697]) and nonrandom causes (more correlation was observed than was expected by random chance). Principal components analysis was used to control for population structure and helped to minimize type I errors, but this was at the expense of identifying fewer causal variants. A novel multiple regression approach showed promise to handle correlation between markers. Further work is needed, first, to identify best practices for the control of type I errors in the analysis of sequencing data and then to explore and compare the many promising new aggregating approaches for identifying markers associated with disease phenotypes. Genet. Epidemiol. 35:S56–S60, 2011.