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.
Data presentation for scientific publications in small sample size studies has not changed substantially in decades. It relies on static figures and tables that may not provide sufficient information ...for critical evaluation, particularly of the results from small sample size studies. Interactive graphics have the potential to transform scientific publications from static reports of experiments into interactive datasets. We designed an interactive line graph that demonstrates how dynamic alternatives to static graphics for small sample size studies allow for additional exploration of empirical datasets. This simple, free, web-based tool (http://statistika.mfub.bg.ac.rs/interactive-graph/) demonstrates the overall concept and may promote widespread use of interactive graphics.
Abstract 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.
Sex hormones play an important role in establishing sex‐distinctive brain structural and functional variations that could contribute to the sex differences in alcohol consumption behavior. Here, we ...systematically reviewed articles that studied sex hormone impacts on alcohol consumption and alcohol use disorder (AUD). An extensive literature search conducted in MEDLINE, PubMed, Scopus and CINAHL databases identified 776 articles, which were then evaluated for pre‐specified criteria for relevance and quality assurance. A total of 50 articles, including 19 human studies and 31 animal studies, were selected for this review. Existing evidence supports the association of increased testosterone level and increased risk for alcohol use and AUD in males but results are inconclusive in females. In contrast, the evidence supports the association of increased estrogen level and increased alcohol use in females, with mixed findings reported in males. Much less is known about the impact of progestins on alcohol use and misuse in human subjects. Future observational and experimental studies conducted in both sexes with a comprehensive hormone panel are needed to elucidate the impact of the interplay between various sex hormone levels during various developmental stages on alcohol use‐related phenotypes and AUD.
Sex hormones play an important role in establishing the sex differences in alcohol consumption behavior. Here, we systematically reviewed articles that studied sex hormone impacts on alcohol consumption and alcohol use disorder. Among the 50 articles that fulfilled selection requirements, 19 were human studies and 31 were animal studies. Existing evidence supports associations between sex hormones and alcohol use, but such associations were different between sexes and among developmental stages. Future studies with improved methodologies may elucidate these associations.
Statistically significant findings are more likely to be published than non-significant or null findings, leaving scientists and healthcare personnel to make decisions based on distorted scientific ...evidence. Continuously expanding ´file drawers' of unpublished data from well-designed experiments waste resources creates problems for researchers, the scientific community and the public. There is limited awareness of the negative impact that publication bias and selective reporting have on the scientific literature. Alternative publication formats have recently been introduced that make it easier to publish research that is difficult to publish in traditional peer reviewed journals. These include micropublications, data repositories, data journals, preprints, publishing platforms, and journals focusing on null or neutral results. While these alternative formats have the potential to reduce publication bias, many scientists are unaware that these formats exist and don't know how to use them. Our open source file drawer data liberation effort (fiddle) tool (RRID:SCR_017327 available at: http://s-quest.bihealth.org/fiddle/) is a match-making Shiny app designed to help biomedical researchers to identify the most appropriate publication format for their data. Users can search for a publication format that meets their needs, compare and contrast different publication formats, and find links to publishing platforms. This tool will assist scientists in getting otherwise inaccessible, hidden data out of the file drawer into the scientific community and literature. We briefly highlight essential details that should be included to ensure reporting quality, which will allow others to use and benefit from research published in these new formats.
Although bar graphs are designed for categorical data, they are routinely used to present continuous data in studies that have small sample sizes. This presentation is problematic, as many data ...distributions can lead to the same bar graph, and the actual data may suggest different conclusions from the summary statistics. To address this problem, many journals have implemented new policies that require authors to show the data distribution. This paper introduces a free, web-based tool for creating an interactive alternative to the bar graph (http://statistika.mfub.bg.ac.rs/interactive-dotplot/). This tool allows authors with no programming expertise to create customized interactive graphics, including univariate scatterplots, box plots, and violin plots, for comparing values of a continuous variable across different study groups. Individual data points may be overlaid on the graphs. Additional features facilitate visualization of subgroups or clusters of non-independent data. A second tool enables authors to create interactive graphics from data obtained with repeated independent experiments (http://statistika.mfub.bg.ac.rs/interactive-repeated-experiments-dotplot/). These tools are designed to encourage exploration and critical evaluation of the data behind the summary statistics and may be valuable for promoting transparency, reproducibility, and open science in basic biomedical research.
Numerous studies demonstrating that statistical errors are common in basic science publications have led to calls to improve statistical training for basic scientists. In this article, we sought to ...evaluate statistical requirements for PhD training and to identify opportunities for improving biostatistics education in the basic sciences. We provide recommendations for improving statistics training for basic biomedical scientists, including: 1. Encouraging departments to require statistics training, 2. Tailoring coursework to the students' fields of research, and 3. Developing tools and strategies to promote education and dissemination of statistical knowledge. We also provide a list of statistical considerations that should be addressed in statistics education for basic scientists.
Identifying 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.
RF 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.
While 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.
Leveraging GWAS: Path to Prevention? Winham, Stacey J; Sherman, Mark E
Cancer prevention research (Philadelphia, Pa.),
01/2024, Letnik:
17, Številka:
1
Journal Article
Recenzirano
Developing novel cancer prevention medication strategies is important for reducing mortality. Identification of common genetic variants associated with cancer risk suggests the potential to leverage ...these discoveries to define causal targets for cancer interception. Although each risk variant confers small increases in risk, researchers propose that blocking those that produce causal carcinogenic effects might have large impacts on cancer prevention. While a promising concept, we describe potential hurdles that may need to be scaled to reach this goal, including: (i) understanding the complexity of risk; (ii) achieving statistical power in studies with binary outcomes (cancer development: yes or no); (iii) characterization of cancer precursors; (iv) heterogeneity of cancer subtypes and the populations in which these diseases occur; (v) impact of static genetic markers across complex events of the life course; (vi) defining gene-gene and gene-environment interactions and (vii) demonstrating functional effects of markers in human populations. We assess short-term prospects for this research against the backdrop of these challenges and the potential to prevent cancer through other means. See related commentary by Peters and Tomlinson, p. 7.
Breast parenchymal texture features, including grayscale variation (V), capture the patterns of texture variation on a mammogram and are associated with breast cancer risk, independent of ...mammographic density (MD). However, our knowledge on the genetic basis of these texture features is limited.
We conducted a genome-wide association study of V in 7040 European-ancestry women. V assessments were generated from digitized film mammograms. We used linear regression to test the single-nucleotide polymorphism (SNP)-phenotype associations adjusting for age, body mass index (BMI), MD phenotypes, and the top four genetic principal components. We further calculated genetic correlations and performed SNP-set tests of V with MD, breast cancer risk, and other breast cancer risk factors.
We identified three genome-wide significant loci associated with V: rs138141444 (6q24.1) in ECT2L, rs79670367 (8q24.22) in LINC01591, and rs113174754 (12q22) near PGAM1P5. 6q24.1 and 8q24.22 have not previously been associated with MD phenotypes or breast cancer risk, while 12q22 is a known locus for both MD and breast cancer risk. Among known MD and breast cancer risk SNPs, we identified four variants that were associated with V at the Bonferroni-corrected thresholds accounting for the number of SNPs tested: rs335189 (5q23.2) in PRDM6, rs13256025 (8p21.2) in EBF2, rs11836164 (12p12.1) near SSPN, and rs17817449 (16q12.2) in FTO. We observed significant genetic correlations between V and mammographic dense area (r
= 0.79, P = 5.91 × 10
), percent density (r
= 0.73, P = 1.00 × 10
), and adult BMI (r
= - 0.36, P = 3.88 × 10
). Additional significant relationships were observed for non-dense area (z = - 4.14, P = 3.42 × 10
), estrogen receptor-positive breast cancer (z = 3.41, P = 6.41 × 10
), and childhood body fatness (z = - 4.91, P = 9.05 × 10
) from the SNP-set tests.
These findings provide new insights into the genetic basis of mammographic texture variation and their associations with MD, breast cancer risk, and other breast cancer risk factors.
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