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In mosquitoes, sex determination is initiated by a dominant male-determining factor located on the Y chromosome in Anopheles mosquitoes or in a small Y-like region called the M locus in Aedes mosquitoes. Before my research, not a single gene from the Anopheles Y or Aedes M locus had ever been discovered. During the course of my undergraduate research in the Tu lab, I developed the chromosome quotient (CQ) method which identifies Y chromosome/M locus sequences by comparing the ratio of alignments from separate pools of female and male Illumina sequencing data. The focus of my dissertation is using the CQ method to identify potential male-determining factors in Aedes and Anopheles mosquitoes. First, we identified a novel gene tightly-linked to the M locus in Aedes aegypti called myo-sex. Myo-sex encodes a myosin heavy chain protein that is highly expressed in the pupa and adult male. Myo-sex is generally only found in males, but can sporadically be found in females due to a rare recombination. The fact that myo-sex can be found in females combined with a lack of early-embryonic expression suggests that myo-sex is not the male-determining factor. Next, we identified a gene in Aedes aegypti, Nix, which appeared to be persistently linked to the M locus and was expressed in the early embryo. Nix shows distant similarity at the amino acid level to Transformer2, a gene involved in the sex determination pathway of Drosophila melanogaster. Nix knockout with CRISPR/Cas9 resulted in feminization of genetic males and the production of the female isoforms of doublesex and fruitless, two key regulators of downstream sexual differentiation. Ectopic expression of Nix resulted in masculinization of genetic females. Based on these results, we concluded that Nix is a male-determining factor in Aedes aegypti. We also characterized large portions of the Anopheles gambiae Y chromosome using PacBio sequencing and the CQ method. We discovered that 92.3 percent of predicted Y sequences fell into two classes, the zanzibar amplified region (ZAR) and the satellite amplified region (SAR). This analysis fills in a large piece of the Anopheles gambiae genome missing since 2002. / Ph. D.

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Identifying a group of individuals that share a common set of characteristics is a conceptually simple task, which is often difficult in practice. Such phenotyping problems emerge in various settings, including the analysis of clinical data. In this setting, phenotyping is often stymied by persistent data quality issues. These include a lack of reliable labels to indicate the presence of absence of characteristics of interest, and significant missingness in observed variables. This dissertation introduces methods for learning phenotypes when the data contain missing values (partially observed) and labels are scarce (partially labeled). Aim 1 utilizes an unsupervised probabilistic graphical model to learn phenotypes from partially observed data. Aim 2 introduces a related semi-supervised probabilistic graphical model for learning phenotypes from partially labeled clinical data. Finally, Aim 3 describes a method for training deep generative models when the training data contain missing values. The algorithm is then applied in a semi-supervised setting where it accounts for partially labeled data as well.