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Open Access Publications from the University of California

Scholarly Works (6 results)

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UCLA Electronic Theses and Dissertations (2023)

Investigating the genetic causes of disease allows for better understanding of disease pathology, and can lead to improved prevention, detection, and treatment of these diseases. In this dissertation I focus on investigations into the genetic architecture of two rare diseases, congenital heart disease (CHD) and coccidioidomycosis (also known as Valley Fever). First, I investigate CHD genetics. One percent of babies are born with congenital heart birth defects, defined by a failure of normal heart development leading to significant physiological defects. Only 30% of cases have a known, single-gene, pathogenic mutation associated with the CHD phenotype. All phenotypes have genetic and environmental contributions to them though. Thus, for a large proportion of individuals, we do not know what the genetic contribution is to the birth defect. We used a trio-based cohort of individuals that contains probands with CHD that remain genetically undiagnosed. We asked whether common variants can have an effect on disease phenotype presence and severity. We use genome-wide association (GWAS) data on CHD endo-phenotypes from publicly available datasets to develop polygenic risk scores (PRS) correlating with disease risk and disease severity. We find that PRS estimated using a GWAS for heart valve problems and heart murmur explain 2.5% of the variance in case-control status of CHD (all SNVs p=7.90e-3, fetal cardiac SNVs p=8.00e-3), and 1.8% of the variance in severity of CHD (fetal cardiac SNVs p=6.20e-3, all SNVs p=0.015). These results show that common variants captured in CHD phenotype-matched GWAS have a modest, but significant contribution to phenotypic expression of CHD. As a second project, I investigate the genetics of susceptibility to severe manifestations of coccidioidomycosis. Currently, it remains unknown why some individuals get more severe Valley Fever infections than others. Coccidioidomycosis, or Valley Fever, is a caused by a fungal infection with Coccidiodes Immities or Posadasii, which are endemic to the Southwest United States and parts of Central and South America. While 60% of people who inhale the fungal spores are asymptomatic (and thus often are not even reported), 40% of people develop a pneumonia-like infection. Most people recover, but a subset of the population develops chronic pulmonary disease and an even smaller number of people, 1% of all infected people, develop disseminated infections which spread to other areas of the body and can lead to meningitis and death. Treatment involves prolonged antifungal use, which contains the infection but has numerous side effects after long-term use. So, it is imperative we develop better ways to detect patients at risk for severe disease and then treat those patients. We use a cohort of Valley Fever patients to look at the genetic causes of having more severe phenotypes. Our dataset is split into patients with uncomplicated Valley Fever (UVF), chronic pulmonary coccidioidomycosis (CPC), and disseminated coccidioidomycosis (DCM). For the purposes of this study we considered UVF patients to be controls, as these patients recover without prolonged antifungal treatment. There is no large cohort of patients who have established coccidioidomycosis infection but without any symptoms. We show that having African genetic similarity is associated with increased risk of severe disease. We also explain how African genetic similarity does not necessarily correlate with self-identified race and ethnicity (SIRE) or skin-color. We also identify identity-by-descent segments shared by individuals with severe disease that may contain risk variants for developing severe disease. In conclusion, I present here a dissertation focused on the genetics of two rare conditions: congenital heart disease and of susceptibility to severe coccidioidomycosis. Researching rare diseases is important because although they affect a smaller proportion of the population, they can have a severe effect on the lives of the individuals with these diseases. In addition, collectively all rare-disease patients around the world represent over 350 million people and represent a significant disease burden. Finally, understanding the genetics of rare disease can help us understand and interpret genetic variation contributing to rare diseases.

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UCLA Previously Published Works (2017)
Although genetic correlations between complex traits provide valuable insights into epidemiological and etiological studies, a precise quantification of which genomic regions disproportionately contribute to the genome-wide correlation is currently lacking. Here, we introduce ρ-HESS, a technique to quantify the correlation between pairs of traits due to genetic variation at a small region in the genome. Our approach requires GWAS summary data only and makes no distributional assumption on the causal variant effect sizes while accounting for linkage disequilibrium (LD) and overlapping GWAS samples. We analyzed large-scale GWAS summary data across 36 quantitative traits, and identified 25 genomic regions that contribute significantly to the genetic correlation among these traits. Notably, we find 6 genomic regions that contribute to the genetic correlation of 10 pairs of traits that show negligible genome-wide correlation, further showcasing the power of local genetic correlation analyses. Finally, we report the distribution of local genetic correlations across the genome for 55 pairs of traits that show putative causal relationships.
    Cover page: Local Genetic Correlation Gives Insights into the Shared Genetic Architecture of Complex Traits
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    UCLA Previously Published Works (2022)
    Congenital heart disease (CHD) is a rare structural defect that occurs in ∼1% of live births. Studies on CHD genetic architecture have identified pathogenic single-gene mutations in less than 30% of cases. Single-gene mutations often show incomplete penetrance and variable expressivity. Therefore, we hypothesize that genetic background may play a role in modulating disease expression. Polygenic risk scores (PRSs) aggregate effects of common genetic variants to investigate whether, cumulatively, these variants are associated with disease penetrance or severity. However, the major limitations in this field have been in generating sufficient sample sizes for these studies. Here we used CHD-phenotype matched genome-wide association study (GWAS) summary statistics from the UK Biobank (UKBB) as our base study and whole-genome sequencing data from the CHD cohort (n1 = 711 trios, n2 = 362 European trios) of the Gabriella Miller Kids First dataset as our target study to develop PRSs for CHD. PRSs estimated using a GWAS for heart valve problems and heart murmur explain 2.5% of the variance in case-control status of CHD (all SNVs, p = 7.90 × 10-3; fetal cardiac SNVs, p = 8.00 × 10-3) and 1.8% of the variance in severity of CHD (fetal cardiac SNVs, p = 6.20 × 10-3; all SNVs, p = 0.015). These results show that common variants captured in CHD phenotype-matched GWASs have a modest but significant contribution to phenotypic expression of CHD. Further exploration of the cumulative effect of common variants is necessary for understanding the complex genetic etiology of CHD and other rare diseases.
      Cover page: Polygenic risk scores of endo-phenotypes identify the effect of genetic background in congenital heart disease
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      UCLA Previously Published Works (2023)
      Arboleda-Tham Syndrome (ARTHS) is a rare genetic disorder caused by heterozygous, de novo mutations in Lysine(K) acetyltransferase 6A (KAT6A). ARTHS is clinically heterogeneous and characterized by several common features, including intellectual disability, developmental and speech delay, and hypotonia, and affects multiple organ systems. KAT6A is the enzymatic core of a histone-acetylation protein complex; however, the direct histone targets and gene regulatory effects remain unknown. In this study, we use ARTHS patient (n = 8) and control (n = 14) dermal fibroblasts and perform comprehensive profiling of the epigenome and transcriptome caused by KAT6A mutations. We identified differential chromatin accessibility within the promoter or gene body of 23% (14/60) of genes that were differentially expressed between ARTHS and controls. Within fibroblasts, we show a distinct set of genes from the posterior HOXC gene cluster (HOXC10, HOXC11, HOXC-AS3, HOXC-AS2, and HOTAIR) that are overexpressed in ARTHS and are transcription factors critical for early development body segment patterning. The genomic loci harboring HOXC genes are epigenetically regulated with increased chromatin accessibility, high levels of H3K23ac, and increased gene-body DNA methylation compared to controls, all of which are consistent with transcriptomic overexpression. Finally, we used unbiased proteomic mass spectrometry and identified two new histone post-translational modifications (PTMs) that are disrupted in ARTHS: H2A and H3K56 acetylation. Our multi-omics assays have identified novel histone and gene regulatory roles of KAT6A in a large group of ARTHS patients harboring diverse pathogenic mutations. This work provides insight into the role of KAT6A on the epigenomic regulation in somatic cell types.
        Cover page: KAT6A mutations in Arboleda-Tham syndrome drive epigenetic regulation of posterior HOXC cluster.
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        UCLA Previously Published Works (2018)
        Clinical case reports (CCRs) provide an important means of sharing clinical experiences about atypical disease phenotypes and new therapies. However, published case reports contain largely unstructured and heterogeneous clinical data, posing a challenge to mining relevant information. Current indexing approaches generally concern document-level features and have not been specifically designed for CCRs. To address this disparity, we developed a standardized metadata template and identified text corresponding to medical concepts within 3,100 curated CCRs spanning 15 disease groups and more than 750 reports of rare diseases. We also prepared a subset of metadata on reports on selected mitochondrial diseases and assigned ICD-10 diagnostic codes to each. The resulting resource, Metadata Acquired from Clinical Case Reports (MACCRs), contains text associated with high-level clinical concepts, including demographics, disease presentation, treatments, and outcomes for each report. Our template and MACCR set render CCRs more findable, accessible, interoperable, and reusable (FAIR) while serving as valuable resources for key user groups, including researchers, physician investigators, clinicians, data scientists, and those shaping government policies for clinical trials.
          • Article
          • Peer Reviewed
          LBL Publications (2018)
          Clinical case reports (CCRs) provide an important means of sharing clinical experiences about atypical disease phenotypes and new therapies. However, published case reports contain largely unstructured and heterogeneous clinical data, posing a challenge to mining relevant information. Current indexing approaches generally concern document-level features and have not been specifically designed for CCRs. To address this disparity, we developed a standardized metadata template and identified text corresponding to medical concepts within 3,100 curated CCRs spanning 15 disease groups and more than 750 reports of rare diseases. We also prepared a subset of metadata on reports on selected mitochondrial diseases and assigned ICD-10 diagnostic codes to each. The resulting resource, Metadata Acquired from Clinical Case Reports (MACCRs), contains text associated with high-level clinical concepts, including demographics, disease presentation, treatments, and outcomes for each report. Our template and MACCR set render CCRs more findable, accessible, interoperable, and reusable (FAIR) while serving as valuable resources for key user groups, including researchers, physician investigators, clinicians, data scientists, and those shaping government policies for clinical trials.
            Cover page: A reference set of curated biomedical data and metadata from clinical case reports.
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