Characterizing genetic variation in humans is an important task in statistical genetics, enabling disease-gene mapping in genome-wide association studies (GWAS) and informing studies of human evolutionary history. A common approach to quantifying genetic variation...

Community detection (CD) algorithms are applied to Hi-C data to discover new communities of loci in the 3D conformation of human and mouse DNA. We find that CD has some distinct advantages over pre-existing methods: (1) it is capable of finding a variable number of communities, (2) it can detect communities of DNA loci either adjacent or distant in the 1D sequence, and (3) it allows us to obtain a principled value of , the number of communities present. Forcing = 2, our method recovers earlier findings of Lieberman-Aiden, et al., but letting be a parameter, our method obtains as optimal value * = 6, discovering new candidate communities. In addition to discovering large communities that partition entire chromosomes, we also show that CD can detect small-scale topologically associating domains (TADs) such as those found in Dixon, et al. (2012). CD thus provides a natural and flexible statistical framework for understanding the folding structure of DNA at multiple scales in Hi-C data.

Abstract Objectives To determine the public health surveillance severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing volume needed, both for acute infection and seroprevalence. Methods Required testing volumes were developed using standard statistical methods based on test analytical performance, disease prevalence, desired precision, and population size. Results Widespread testing for individual health management cannot address surveillance needs. The number of people who must be sampled for public health surveillance and decision making, although not trivial, is potentially in the thousands for any given population or subpopulation, not millions. Conclusions While the contributions of diagnostic testing for SARS-CoV-2 have received considerable attention, concerns abound regarding the availability of sufficient testing capacity to meet demand. Different testing goals require different numbers of tests and different testing strategies; testing strategies for national or local disease surveillance, including monitoring of prevalence, receive less attention. Our clinical laboratory and diagnostic infrastructure are capable of incorporating required volumes for many local, regional, and national public health surveillance studies into their current and projected testing capacity. However, testing for surveillance requires careful design and randomization to provide meaningful insights.

No abstract

No abstract