Stacey J. Winham

Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm

Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

SNP interaction detection with Random Forests in high-dimensional genetic data

BackgroundIdentifying variants associated with complex human traits in high-dimensional data is a central goal of genome-wide association studies. However, complicated etiologies such as gene-gene interactions are ignored by the univariate analysis usually applied in these studies. Random Forests (RF) are a popular data-mining technique that can accommodate a large number of predictor variables and allow for complex models with interactions. RF analysis produces measures of variable importance that can be used to rank the predictor variables. Thus, single nucleotide polymorphism (SNP) analysis using RFs is gaining popularity as a potential filter approach that considers interactions in high-dimensional data. However, the impact of data dimensionality on the power of RF to identify interactions has not been thoroughly explored. We investigate the ability of rankings from variable importance measures to detect gene-gene interaction effects and their potential effectiveness as filters compared to p-values from univariate logistic regression, particularly as the data becomes increasingly high-dimensional.ResultsRF effectively identifies interactions in low dimensional data. As the total number of predictor variables increases, probability of detection declines more rapidly for interacting SNPs than for non-interacting SNPs, indicating that in high-dimensional data the RF variable importance measures are capturing marginal effects rather than capturing the effects of interactions.ConclusionsWhile RF remains a promising data-mining technique that extends univariate methods to condition on multiple variables simultaneously, RF variable importance measures fail to detect interaction effects in high-dimensional data in the absence of a strong marginal component, and therefore may not be useful as a filter technique that allows for interaction effects in genome-wide data.

Clinical phenotype of bipolar disorder with comorbid binge eating disorder

BACKGROUND To explore the relationship between binge eating disorder (BED) and obesity in patients with bipolar disorder (BP). METHODS 717 patients participating in the Mayo Clinic Bipolar Biobank completed structured diagnostic interviews and questionnaires for demographic and illness-related variables. They also had weight and height measured to determine body mass index (BMI). The effects of BED and obesity (BMI≥30 kg/m(2)), as well as their interaction, were assessed on one measure of general medical burden and six proxies of psychiatric illness burden. RESULTS 9.5% of patients received a clinical diagnosis of BED and 42.8% were obese. BED was associated with a significantly elevated BMI. Both BED and obesity were associated with greater psychiatric and general illness burden, but illness burden profiles differed. After controlling for obesity, BED was associated with suicidality, psychosis, mood instability, anxiety disorder comorbidity, and substance abuse comorbidity. After controlling for BED status, obesity was associated with greater general medical comorbidity, but lower substance abuse comorbidity. There were no significant interaction effects between obesity and BED, or BMI and BED, on any illness burden outcome. LIMITATIONS There may have been insufficient power to detect interactions between BED and obesity. CONCLUSIONS Among patients with BP, BED and obesity are highly prevalent and correlated, but associated with different profiles of enhanced illness burden. As the association of BED with greater psychiatric illness burden remained significant even after accounting for the effect of obesity, BP with BED may represent a clinically important sub-phenotype.

Genetics of cardiovascular disease: Importance of sex and ethnicity

Sex differences in incidence and prevalence of and morbidity and mortality from cardiovascular disease are well documented. However, many studies examining the genetic basis for cardiovascular disease fail to consider sex as a variable in the study design, in part, because there is an inherent difficulty in studying the contribution of the sex chromosomes in women due to X chromosome inactivation. This paper will provide general background on the X and Y chromosomes (including gene content, the pseudoautosomal regions, and X chromosome inactivation), discuss how sex chromosomes have been ignored in Genome-wide Association Studies (GWAS) of cardiovascular diseases, and discuss genetics influencing development of cardiovascular risk factors and atherosclerosis with particular attention to carotid intima-medial thickness, and coronary arterial calcification based on sex-specific studies. In addition, a brief discussion of how ethnicity and hormonal status act as confounding variables in sex-based analysis will be considered along with methods for statistical analysis to account for sex in cardiovascular disease.

Epigenome-wide ovarian cancer analysis identifies a methylation profile differentiating clear-cell histology with epigenetic silencing of the HERG K+ channel

Ovarian cancer remains the leading cause of death in women with gynecologic malignancies, despite surgical advances and the development of more effective chemotherapeutics. As increasing evidence indicates that clear-cell ovarian cancer may have unique pathogenesis, further understanding of molecular features may enable us to begin to understand the underlying biology and histology-specific information for improved outcomes. To study epigenetics in clear-cell ovarian cancer, fresh frozen tumor DNA (n = 485) was assayed on Illumina Infinium HumanMethylation450 BeadChips. We identified a clear-cell ovarian cancer tumor methylation profile (n = 163) which we validated in two independent replication sets (set 1, n = 163; set 2, n = 159), highlighting 22 CpG loci associated with nine genes (VWA1, FOXP1, FGFRL1, LINC00340, KCNH2, ANK1, ATXN2, NDRG21 and SLC16A11). Nearly all of the differentially methylated CpGs showed a propensity toward hypermethylation among clear-cell cases. Several loci methylation inversely correlated with tumor gene expression, most notably KCNH2 (HERG, a potassium channel) (P = 9.5 × 10(-7)), indicating epigenetic silencing. In addition, a predicted methylation class mainly represented by the clear-cell cases (20 clear cell out of 23 cases) had improved survival time. Although these analyses included only 30 clear-cell carcinomas, results suggest that loss of expression of KCNH2 (HERG) by methylation could be a good prognostic marker, given that overexpression of the potassium (K(+)) channel Eag family members promotes increased proliferation and results in poor prognosis. Validation in a bigger cohort of clear-cell tumors of the ovary is warranted.

Gene-environment interactions in genome-wide association studies: current approaches and new directions.

BACKGROUND Complex psychiatric traits have long been thought to be the result of a combination of genetic and environmental factors, and gene-environment interactions are thought to play a crucial role in behavioral phenotypes and the susceptibility and progression of psychiatric disorders. Candidate gene studies to investigate hypothesized gene-environment interactions are now fairly common in human genetic research, and with the shift toward genome-wide association studies, genome-wide gene-environment interaction studies are beginning to emerge. METHODS We summarize the basic ideas behind gene-environment interaction, and provide an overview of possible study designs and traditional analysis methods in the context of genome-wide analysis. We then discuss novel approaches beyond the traditional strategy of analyzing the interaction between the environmental factor and each polymorphism individually. RESULTS Two-step filtering approaches that reduce the number of polymorphisms tested for interactions can substantially increase the power of genome-wide gene-environment studies. New analytical methods including data-mining approaches, and gene-level and pathway-level analyses, also have the capacity to improve our understanding of how complex genetic and environmental factors interact to influence psychologic and psychiatric traits. Such methods, however, have not yet been utilized much in behavioral and mental health research. CONCLUSIONS Although methods to investigate gene-environment interactions are available, there is a need for further development and extension of these methods to identify gene-environment interactions in the context of genome-wide association studies. These novel approaches need to be applied in studies of psychology and psychiatry.

Five endometrial cancer risk loci identified through genome-wide association analysis

We conducted a meta-analysis of three endometrial cancer genome-wide association studies (GWAS) and two follow-up phases totaling 7,737 endometrial cancer cases and 37,144 controls of European ancestry. Genome-wide imputation and meta-analysis identified five new risk loci of genome-wide significance at likely regulatory regions on chromosomes 13q22.1 (rs11841589, near KLF5), 6q22.31 (rs13328298, in LOC643623 and near HEY2 and NCOA7), 8q24.21 (rs4733613, telomeric to MYC), 15q15.1 (rs937213, in EIF2AK4, near BMF) and 14q32.33 (rs2498796, in AKT1, near SIVA1). We also found a second independent 8q24.21 signal (rs17232730). Functional studies of the 13q22.1 locus showed that rs9600103 (pairwise r2 = 0.98 with rs11841589) is located in a region of active chromatin that interacts with the KLF5 promoter region. The rs9600103[T] allele that is protective in endometrial cancer suppressed gene expression in vitro, suggesting that regulation of the expression of KLF5, a gene linked to uterine development, is implicated in tumorigenesis. These findings provide enhanced insight into the genetic and biological basis of endometrial cancer.

Prevalence and correlates of DSM-5 eating disorders in patients with bipolar disorder

OBJECTIVE To determine prevalence rates and clinical correlates of current DSM-5 eating disorders in patients with bipolar disorder (BP). METHODS Prevalence rates of current DSM-5- and DSM-IV-defined binge eating disorder (BED), bulimia nervosa (BN), and anorexia nervosa (AN) were assessed with the Eating Disorder Diagnostic Scale (EDDS) in 1092 patients with BP. Psychiatric illness burden was evaluated with five proxy measures of BP illness severity. Medical illness burden was evaluated with the Cumulative Index Rating Scale (CIRS). RESULTS Twenty-seven percent of patients had a current DSM-5 eating disorder: 12% had BED, 15% had BN, and 0.2% had AN. Rates of DSM-5-defined BED and BN were higher than clinical diagnosis rates and rates of DSM-IV-defined BED and BN. Compared with BP patients without an eating disorder, BP patients with a DSM-5 eating disorder were younger and more likely to be women; had an earlier age of onset of BP; had higher EDDS composite scores and higher degrees of suicidality, mood instability, and anxiety disorder comorbidity; and had a higher mean BMI, higher rate of obesity, and higher CIRS total scores. In a logistic regression model controlling for previously identified correlates of an eating disorder, younger age, female gender, and higher BMI remained significantly associated with an eating disorder. LIMITATIONS The EDDS has not been validated in BP patients. CONCLUSION DSM-5-defined BED and BN are common in BP patients, possibly more common than DSM-IV-defined BED and BN, and associated with greater psychiatric and general medical illness burden. Further studies assessing DSM-5 eating disorders in people with BP are greatly needed.

Extent of atypical hyperplasia stratifies breast cancer risk in 2 independent cohorts of women

Women with atypical hyperplasia (AH) on breast biopsy have a substantially increased risk of breast cancer (BC). Here the BC risk for the extent and subtype of AH is reported for 2 separate cohorts.

CYP19A1 fine-mapping and Mendelian randomization: estradiol is causal for endometrial cancer

Candidate gene studies have reported CYP19A1 variants to be associated with endometrial cancer and with estradiol (E2) concentrations. We analyzed 2937 single nucleotide polymorphisms (SNPs) in 6608 endometrial cancer cases and 37 925 controls and report the first genome wide-significant association between endometrial cancer and a CYP19A1 SNP (rs727479 in intron 2, P=4.8×10−11). SNP rs727479 was also among those most strongly associated with circulating E2 concentrations in 2767 post-menopausal controls (P=7.4×10−8). The observed endometrial cancer odds ratio per rs727479 A-allele (1.15, CI=1.11–1.21) is compatible with that predicted by the observed effect on E2 concentrations (1.09, CI=1.03–1.21), consistent with the hypothesis that endometrial cancer risk is driven by E2. From 28 candidate-causal SNPs, 12 co-located with three putative gene-regulatory elements and their risk alleles associated with higher CYP19A1 expression in bioinformatical analyses. For both phenotypes, the associations with rs727479 were stronger among women with a higher BMI (Pinteraction=0.034 and 0.066 respectively), suggesting a biologically plausible gene-environment interaction.