Pathogens colonize their hosts by releasing molecules that can enter host cells. A biotrophic oomycete plant pathogen, Phytophthora sojae harbors a superfamily of effector genes whose protein products enter the cells of the host, soybean. Many of the effectors contain an RXLR-dEER motif in their N-terminus. More than 400 members belonging to this family have been previously identified using a Hidden Markov Model. Amino acids flanking the RXLR motif have been utilized to identify effector proteins from the P. sojae secretome, despite the high level of sequence divergence among the members of this protein family. I present here machine learning methods to identify protein candidates that belong to a particular class, such as the effector superfamily. Converting the flanking amino acid sequences of RXLR motifs (or other candidate motifs) into numeric values that reflect their physical properties enabled the protein sequences to be analyzed through these methods. The methods evaluated include Support Vector Machines and a related spherical classification method that I have developed. I also approached the effector prediction problem by building functional linkage networks and have produced lists of predicted P. sojae effector proteins. I tested the best candidate through gene gun bombardment assays using the beta-glucuronidase reporter system, which revealed that there is a high likelihood that the candidate can enter the soybean cells. / Ph. D.

Recent large-scale genome-wide studies for many human disorders have identified associations with numerous genetic variants. The biological interpretation of these variants presents a major challenge. In particular, the identification of biological pathways underlying the association could provide crucial insights into the disease aetiologies. In this thesis, I used functional genomics approaches to increase our understanding of neuropsychiatric and neurodevelopmental disorders. Firstly, in an integrative analysis of autism spectrum disorder (ASD), I looked into the role of genes targeted by Fragile-X Mental Retardation Protein ("FMRP targets"). I found evidence that FMRP targets contribute to ASD via two distinct aetiologies: (1) ultra-rare and highly penetrant single disruptions of embryonically upregulated FMRP targets ("single-hit aetiology") or (2) the combination of multiple less penetrant disruptions of synaptic FMRP targets ("multiple-hit aetiology"). In particular, I developed a pathway-association test sensitive to multiple-hit aetiologies. Secondly, I carried out an integrative analysis of bipolar disorder, following up a previously identified association with long-term potentiation. The association was not consistent across independent SNP and CNV datasets. Thirdly, I addressed the difficulty in identifying functional relationships between genes by integrating different datasets into a gene functional-linkage network tuned to the nervous system ("NsNet"). NsNet identified functional links between the genes disrupted by de novo loss-of-function mutations in ASD and, separately, in schizophrenia probands more sensitively than a general functional-linkage network. Fourthly, I considered the challenge of interpreting the phenotypic impact of gene disruptions, focusing on the identification of haploinsufficient genes. I constructed a gene haploinsufficiency score based on genome-wide datasets. Compared to existing approaches, the new score performed better in identifying less-studied haploinsufficient genes. This work both extends the methodology to detect the contribution of genetic variation to neuropsychiatric disorders and also yields insights into the variant genes and the pathways that underlie them. Firstly, in an integrative analysis of autism spectrum disorder (ASD), I looked into the role of genes targeted by Fragile-X Mental Retardation Protein ("FMRP targets"). I found evidence that FMRP targets contribute to ASD via two distinct aetiologies: (1) ultra-rare and highly penetrant single disruptions of embryonically upregulated FMRP targets ("single-hit aetiology") or (2) the combination of multiple less penetrant disruptions of synaptic FMRP targets ("multiple-hit aetiology"). In particular, I developed a pathway-association test sensitive to multiple-hit aetiologies. Secondly, I carried out an integrative analysis of bipolar disorder, following up a previously identified association with long-term potentiation. The association was not consistent across independent SNP and CNV datasets. Thirdly, I addressed the difficulty in identifying functional relationships between genes by integrating different datasets into a gene functional-linkage network tuned to the nervous system ("NsNet"). NsNet identified functional links between the genes disrupted by de novo loss-of-function mutations in ASD and, separately, in schizophrenia probands more sensitively than a general functional-linkage network. Fourthly, I considered the challenge of interpreting the phenotypic impact of gene disruptions, focusing on the identification of haploinsufficient genes. I constructed a gene haploinsufficiency score based on genome-wide datasets. Compared to existing approaches, the new score performed better in identifying less-studied haploinsufficient genes. This work both extends the methodology to detect the contribution of genetic variation to neuropsychiatric disorders and also yields insights into the variant genes and the pathways that underlie them.