Abstract: In the postgenomic era, it remains a challenging task to understand the cellular functions of genes and how the dysfunction of a gene relates to a disease. Since genes work cooperatively for particular cellular tasks, a functional linkage network (FLN) can be used for function-related studies. In this network, the nodes represent genes and the weighted edges represent the degree of their functional association. Here I explore the FLN construction, FLN-based gene-function prediction, and FLN-based new-disease-gene prediction. In the first part of the dissertation, aiming to provide precise functional annotation for as many genes as possible, I explore and propose a two-step framework: (i) construction of a high-coverage and reliable FLN via data integration, and (ii) development of a reliable decision rule for functional annotation. This framework is tested in yeast and E. coli . In step one, I demonstrate that commonly used machine learning methods such as Linear SVM and Naïve Bayes all combine heterogeneous data to produce reliable and high-coverage FLNs. In step two, empirical tuning of an adjustable decision rule on the FLN reveals that basing annotation on maximum edge weight results in the most precise annotation at high coverages. In the second part of the dissertation, I build and validate a human genome-scale FLN by data integration using a Naïve Bayes classifier. This FLN is then used to predict new candidate disease genes associated with 110 diseases. In particular I hypothesize that the neighborhood of known disease genes tends to be enriched in genes that are also associated with the same disease. This is based on the observation that disease genes underlying common diseases tend to occur in distinct functional modules. The network thus enables one to identify previously unimplicated genes, and to rank them by the likelihood of their involvement. I show that this FLN is able to predict new disease genes for diverse diseases and outperforms networks based solely on protein-protein physical interactions. Additionally, based on the observation that disease genes underlying similar or related diseases tend to be functionally related, I illustrate that the FLN can also help to assess disease-disease associations.

Abstract: Heterozygous mutations in the cytotoxic T lymphocyte antigen-4 (CTLA-4) are associated with lymphadenopathy, autoimmunity, immune dysregulation, and hypogammaglobulinemia in about 70% of the carriers. So far, the incomplete penetrance of CTLA-4 haploinsufficiency has been attributed to unknown genetic modifiers, epigenetic changes, or environmental effects. We sought to identify potential genetic modifiers in a family with differential clinical penetrance of CTLA-4 haploinsufficiency. Here, we report on a rare heterozygous gain-of-function mutation in Janus kinase-3 (JAK3) (p.R840C), which is associated with the clinical manifestation of CTLA-4 haploinsufficiency in a patient carrying a novel loss-of-function mutation in CTLA-4 (p.Y139C). While the asymptomatic parents carry either the CTLA-4 mutation or the JAK3 variant, their son has inherited both heterozygous mutations and suffers from hypogammaglobulinemia combined with autoimmunity and lymphoid hyperplasia. Although the patient's lymph node and spleen contained many hyperplastic germinal centers with follicular helper T (TFH) cells and immunoglobulin (Ig) G-positive B cells, plasma cell, and memory B cell development was impaired. CXCR5+PD-1+TIGIT+ TFH cells contributed to a large part of circulating T cells, but they produced only very low amounts of interleukin (IL)-4, IL-10, and IL-21 required for the development of memory B cells and plasma cells. We, therefore, suggest that the combination of the loss-of-function mutation in CTLA-4 with the gain-of-function mutation in JAK3 directs the differentiation of CD4 T cells into dysfunctional TFH cells supporting the development of lymphadenopathy, hypogammaglobulinemia, and immunodeficiency. Thus, the combination of rare genetic heterozygous variants that remain clinically unnoticed individually may lead to T cell hyperactivity, impaired memory B cell, and plasma cell development resulting finally in combined immunodeficiency