Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

How Negative Sentiment towards Muslim Americans Predicts Support for Trump in the 2016 Presidential Election

Replication files for "How Negative Sentiment towards Muslim Americans Predicts Support for Trump in the 2016 Presidential Election

Replication Data for: Labels vs. Pictures: Treatment-Mode Effects in Experiments About Discrimination

This is replication data and code for "Labels vs. Pictures: Treatment-Mode Effects in Experiments About Discrimination" published in Political Analysis

Racially Polarized Voting

Whether voting is racially polarized has for the last generation been the linchpin question in vote dilution cases under the core, nationally applicable provision of the Voting Rights Act. The polarization test is supposed to be clear-cut (“manageable”), diagnostic of liability, and free of strong racial assumptions. Using evidence from a random sample of vote dilution cases, we argue that these objectives have not been realized in practice and, further, that they cannot be realized under current conditions. The roots of the problem are twofold: (1) the widely shared belief that polarization determinations should be grounded on votes cast in actual elections; and (2) normative disagreement, often covert, about the meaning of racial vote dilution. We argue that the principal normative theories of vote dilution have conflicting implications for the racial-polarization test. We also show that votes are related only contingently to the political preferences that the polarization inquiry is supposed to reveal and, further, that the estimation of candidates’ vote shares by racial group from ballots cast in actual elections depends on racialhomogeneity assumptions similar to those that the Supreme Court has disavowed. Our analysis casts serious doubt on the notion—promoted in dicta by the Supreme Court and supported by prominent commentators—that courts should establish bright-line vote-share cutoffs for “legally significant” racial polarization. The courts would do better to screen vote dilution claims using either evidence of preference polarization derived from surveys or nonpreference evidence of minority political incorporation

Using Machine Learning to Uncover Hidden Heterogeneities in Survey Data.

Survey responses in public health surveys are heterogeneous. The quality of a respondent's answers depends on many factors, including cognitive abilities, interview context, and whether the interview is in person or self-administered. A largely unexplored issue is how the language used for public health survey interviews is associated with the survey response. We introduce a machine learning approach, Fuzzy Forests, which we use for model selection. We use the 2013 California Health Interview Survey (CHIS) as our training sample and the 2014 CHIS as the test sample. We found that non-English language survey responses differ substantially from English responses in reported health outcomes. We also found heterogeneity among the Asian languages suggesting that caution should be used when interpreting results that compare across these languages. The 2013 Fuzzy Forests model also correctly predicted 86% of good health outcomes using 2014 data as the test set. We show that the Fuzzy Forests methodology is potentially useful for screening for and understanding other types of survey response heterogeneity. This is especially true in high-dimensional and complex surveys