New Advancements of Scalable Statistical Methods for Learning Latent Structures in Big Data

Constant technology advances have caused data explosion in recent years. Accord- ingly modern statistical and machine learning methods must be adapted to deal with complex and heterogeneous data types. This phenomenon is particularly true for an- alyzing biological data. For example DNA sequence data can be viewed as categorical variables with each nucleotide taking four different categories. The gene expression data, depending on the quantitative technology, could be continuous numbers or counts. With the advancement of high-throughput technology, the abundance of such data becomes unprecedentedly rich. Therefore efficient statistical approaches are crucial in this big data era.

Previous statistical methods for big data often aim to find low dimensional struc- tures in the observed data. For example in a factor analysis model a latent Gaussian distributed multivariate vector is assumed. With this assumption a factor model produces a low rank estimation of the covariance of the observed variables. Another example is the latent Dirichlet allocation model for documents. The mixture pro- portions of topics, represented by a Dirichlet distributed variable, is assumed. This dissertation proposes several novel extensions to the previous statistical methods that are developed to address challenges in big data. Those novel methods are applied in multiple real world applications including construction of condition specific gene co-expression networks, estimating shared topics among newsgroups, analysis of pro- moter sequences, analysis of political-economics risk data and estimating population structure from genotype data.

The Genetics of Success: How Single-Nucleotide Polymorphisms Associated With Educational Attainment Relate to Life-Course Development.

A previous genome-wide association study (GWAS) of more than 100,000 individuals identified molecular-genetic predictors of educational attainment. We undertook in-depth life-course investigation of the polygenic score derived from this GWAS using the four-decade Dunedin Study (N = 918). There were five main findings. First, polygenic scores predicted adult economic outcomes even after accounting for educational attainments. Second, genes and environments were correlated: Children with higher polygenic scores were born into better-off homes. Third, children's polygenic scores predicted their adult outcomes even when analyses accounted for their social-class origins; social-mobility analysis showed that children with higher polygenic scores were more upwardly mobile than children with lower scores. Fourth, polygenic scores predicted behavior across the life course, from early acquisition of speech and reading skills through geographic mobility and mate choice and on to financial planning for retirement. Fifth, polygenic-score associations were mediated by psychological characteristics, including intelligence, self-control, and interpersonal skill. Effect sizes were small. Factors connecting DNA sequence with life outcomes may provide targets for interventions to promote population-wide positive development.

2000 NHGRI e-mail request for HGP sequencing center information, and corresponding aggregate data from NHGRI, compiled by Kris Wetterstrand

Email exchange in 2013 between Kathryn Maxson (Duke) and Kris Wetterstrand (NHGRI), regarding country funding and other data for the HGP sequencing centers. Also includes the email request for such information, from NHGRI to the centers, in 2000, and the aggregate data collected.

2012 09 February Jean Weissenbach interview

Jean Weissenbach, telephone interview by Kathryn Maxson and Robert Cook-Deegan, conducted from Durham, NC 09 February 2012. Jean Weissenbach, a leader in French genetic mapping, directed the French national sequencing center, Généthon, during the HGP and was instrumental in helping to build agreement to the Bermuda Principles in France.

2011 18 August Mark Guyer-Jane Peterson interview

Mark Guyer and Jane Peterson, in-person interview with Kathryn Maxson and Robert Cook-Deegan, conducted in Rockville, MD (NIH campus), 18 August 2011. Mark Guyer and Jane Peterson were grants program officers at the NIH during the HGP, and were some of the longest-standing employees in the HGP administrative structure. Both witnessed the transformation of the Office of Genome Research into the National Center for Human Genome Research and, finally, the National Human Genome Research Institute. They were close participants in the history of the Bermuda Principles within the NIH.

1997 Delegates to the Second International Strategy Meeting on Human Genome Sequencing in Bermuda from Gert-Jan van Ommen

Photographs from the February 1997 Bermuda meeting. Courtesy of Gert-Jan van Ommen.