Empower an End-to-end Scalable and Interpretable Data Science Ecosystem using Statistics, AI and Domain Science

摘要

The data science ecosystem encompasses data fairness, statistical, ML and AI methods and tools, interpretable data analysis and results, and trustworthy decision-making. Rapid advancements in AI have revolutionized data utilization and enabled machines to learn from data more effectively. Statistics, as the science of learning from data while accounting for uncertainty, plays a pivotal role in addressing complex real-world problems and facilitating trustworthy decision-making. In this talk, I will discuss the challenges and opportunities involved in building an end-to-end scalable and interpretable data science ecosystem using the analysis of whole genome sequencing studies and biobanks that integrates statistics, ML/AI, and genomic and health science as an example. Biobanks collect whole genome data, electronic health records and epidemiological data. I will illustrate key points using the analysis of multi-ancestry whole genome sequencing studies and biobanks by discussing a few scalable and interpretable statistical and ML/AI methods, tools and data science resources. Specifically, first, data fairness and diversity is a critical pillar of a trustworthy data science ecosystem. About 85+% of genome wide association study samples in the last 15 years are European, resulting in disparity in genetic research. I will discuss the community effort on improving diversity in genetic studies in the last 10 years. I will present trans-ancestry polygenic risk scores (PRS) using millions of common genetic variants across the genome by leveraging large GWAS sample sizes of European and smaller sample sizes of under-represented populations for predicting disease risk using transfer learning and genetic association summary statistics. The performance of deep learning methods for PRS will also be discussed. Second, scalability in cloud platforms is critical for large scale affordable analysis for multi-ancestry biobanks and whole genome studies. I will discuss improving scalability in cloud-computing using interpretable sparsity via FastSparseGRM. To build an interpretable and powerful end-to-end ecosystem of rare variant analysis of large scale whole genome sequencing studies and biobanks, I will first introduce FAVOR, a multi-faceted variant functional annotation database and portal of all possible 9 billions of variants across the whole genome. I will discuss FAVOR-GPT, a LLM interface of the FAVOR functional annotation database to improve user experience for navigating FAVOR and performing variant functional annotation query and variant functional summary statistics calculations. I will also discuss FAVORannotator which can be used to functionally annotate any whole genome sequencing studies. I will also discuss STAAR and STAAR and STAARpipeline, the WGS rare variant analysis pipeline that boosts the power of WGS rare variant association analysis by dynamically incorporating multi-faceted variant functional annotations. Extension of incorporating single-cell data in WGS analysis will also be discussed. I will also discuss ensemble methods that improve the power of rare variant association tests. Cloud-deployment of these resources and tools in several ecosystems will be presented, such as RAP for the UK biobank, AnVIL for the NHGRI Genome Sequencing Program and All of Us, and BioData Catalyst for the NHLBI Trans-omics Precision Medine Program (TOPMed). This talk aims to ignite proactive and thought-provoking discussions, foster collaboration, and cultivate open-minded approaches to advance scientific discovery.