Biomedical Data Science Seminar Series

The scientific needs and computational limitations of the Twentieth Century fashioned classical statistical methodology. Both the needs and limitations have changed in the Twenty-First, and so has the meth-odology. Large-scale prediction algorithms - neural nets, deep learning, boosting, support vector machines, random forests - have achieved star status in the popular press. They are recognizable as heirs to the regression tradition, but ones carried out at enormous scale and on titanic data sets. How do these algorithms compare with standard regression techniques such as Ordinary Least Squares or logistic regression? Several key discrepancies will be examined, centering on the differences between prediction and estimation or prediction and attribution (that is, significance testing). Most of the discussion is carried out through small numerical examples. The talk does not assume familiarity with prediction algorithms.

The coronavirus will likely kill thousands of Americans. But what if I told you about a serious threat to American national security. This emergency comes from climate change and air pollution.

To help address this threat, we have developed an artificial neural network model that uses on-the-ground air-monitoring data and satellite-based measurements to estimate daily pollution levels dividing the country into 1-square-kilometer zones across the continental U.S. We have paired this information with health data contained in Medicare claims records from the last 12 years, which includes 97% of the population ages 65 or older. We also developed statistical methods for causal inference and computational efficient algorithms for the analysis of over 550 million health records.

The result? This data science platform is telling us that federal limits on the nation’s most widespread air pollutants are not stringent enough. Our research shows that short- and long-term exposure to air pollution is killing thousands of senior citizens each year.

Our research shows the critical new role of data science in public health and the associated methodological challenges. For example, with enormous amounts of data, the threat of unmeasured confounding bias is amplified, and causality is even harder to assess with observational studies. We will discuss these and other challenges.

Press coverage links

NPR: http://www.npr.org/sections/health-shots/2017/06/28/534594373/u-s-air-pollution-still-kills-thousands-every-year-study-concludes

Los Angeles Times: http://www.latimes.com/science/sciencenow/la-sci-sn-air-pollution-death-20170628-story.html

New York Times: https://www.nytimes.com/2017/06/28/well/even-safe-pollution-levels-can-be-deadly.html?_r=0

Podcast: https://www.hsph.harvard.edu/news/multimedia-article/harvard-chan-this-week-in-health-archive/

CNN: https://www.cnn.com/2019/11/27/health/air-pollution-hospitalization-study/index.html

The Guardian: https://www.theguardian.com/environment/2019/nov/27/impact-of-air-pollution-on-health-may-be-far-worse-than-thought-study-suggests

Harvard Magazine: https://harvardmagazine.com/2020/03/right-now-air-pollution-systemic-effects

Over 100,000 variants have been implicated in human traits through genome-wide association studies (GWAS).  Virtually all reported GWAS identify associations by measuring the correlation between a single variant and a phenotype of interest. Recently, several studies reported that at many risk loci, there may exist multiple causal variants, a phenomenon referred to as allelic heterogeneity. Naturally, for a locus with multiple causal variants with small effect sizes, the standard association test is underpowered to detect the associations. Alternatively, an approach considering effects of multiple variants simultaneously may increase statistical power. Counterintuitively, most approaches that consider multiple variants in association studies find fewer associations than the single SNP association test.  This is due to the fact that most multiple variant methods assume a structure of allelic heterogeneity which is very different from what is observed in genetic studies.  In this work, we propose a new statistical method, Model-based Association test Reflecting causal Status (MARS), that tries to find an association between variants in risk loci and a phenotype, considering multiple variants at each locus. One of the main advantages of MARS is that it only requires the existing summary statistics to detect associated risk loci. Thus, MARS is applicable to any association study with summary statistics, even though individual level data is not available for the study. Utilizing extensive simulated data sets, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while robustly controls the type I error. Applied to data of 44 tissues provided by the Genotype- Tissue Expression (GTEx) consortium, we show that MARS identifies more eGenes compared to previous approaches in most of the tissues; e.g. MARS identified 16% more eGenes than the ones reported by the GTEx consortium. Moreover, applied to Northern Finland Birth Cohort (NFBC) data, we demonstrate that MARS effectively identifies association loci with improved power (56% of more loci found by MARS) in GWAS studies compared to the standard association test.