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Open Access Publications from the University of California

Scholarly Works (8 results)

  • Thesis
  • Peer Reviewed
UCLA Electronic Theses and Dissertations (2012)

Much work has targeted the detection of disease genes through genetic mapping for metabolic diseases such as type 2 diabetes (T2D), cardiovascular diseases (CVD), and other diabetes-related traits such as body mass index (BMI) and hemoglobin (HbA1C) levels. However, the etiology of metabolic diseases remains partially understood hampering the development of more personalized diagnosis, treatment and prevention strategies.

This dissertation examines the association between genetic variants with risk of metabolic diseases and diabetes-related quantitative traits in both candidate gene and genome-wide scan settings. In particular, we assessed the association of genetic loci related to adiposity, inflammation, and lipid storage, with the risk of diabetes using a candidate gene approach. We also investigated biological pathways that may give rise to the development of vascular disease (T2D and/or CVD) and also further investigated genetic variants related to BMI and HbA1C levels using a genome-wide approach. Chapter 1 introduces general background on the evolution of genetic research in the arena of metabolic diseases. Chapter 2 investigates common variants in the genomic region of FABP4, CRP, TNF, IL6 and PPARG in relation to diabetes risk among postmenopausal women enrolled in the Women's Health Initiative Observational Study (WHI-OS). Chapter 3 examines whether common variants involved in vascular disease risk are clustered in multiple pathways among African and Hispanic American participants in the WHI SNP Health Association Resource (SHARe) cohort. Chapter 4 examines the association between genetic variants with BMI and HbA1C levels using a family-based genome-wide association approach among participants in the Framingham Heart Study (FHS).

Our main findings are: 1) Candidate gene-based studies indicate that variation exists across even the candidate gene regions. FABP4 genotypes were associated with reduced VCAM-1 levels, though none of the common genetic variants in the FABP4 gene examined were associated with risk of T2D. We also observed modest associations between TNF genetic variants and circulating concentrations of TNF-α-R2, although common variants of CRP, TNF, and IL6 genes were not associated with T2D risk. Using the example of the PPARG gene with T2D risk, however, we replicated the association between the PPARG Pro12Ala genetic variant with diabetes risk and found that haplotype-based analysis is more powerful than single-SNP analysis for identifying genetic variants. 2) Using a pathway-based analytical approach and genome-wide scan data collected among African and Hispanic American postmenopausal women, we observed that genetic variants associated with vascular disease appeared to cluster into several biological pathways including the glycerolipid metabolism and PPAR signaling pathways. 3) We found strong associations between SNPs near the LOC100507205 locus and BMI in the family-based Framingham Heart Study with three generations. We also replicated five well-validated genes that have been previously reported to be significantly associated with the BMI trait. These findings contribute to the growing body of literature in identifying genetic variants in the development of metabolic disease, further future work (e.g. in the area of structure and functional variants) are warranted to improve understanding of the genetic architecture for metabolic outcomes. Increasing integration of cutting edge genomic science into population-based epidemiologic investigation will accelerate and improve our understanding of the genetic susceptibility of complex diseases. The work described in this dissertation represents a tip of our effort toward the ultimate improvement of the diagnosis, treatment and prevention of metabolic diseases in human populations.

    Cover page: Assessing the genetic architecture of metabolic diseases using candidate gene and genome-wide approach
    • Article
    UCLA Previously Published Works (2014)
    Since most analysis software for genome-wide association studies (GWAS) currently exploit only unrelated individuals, there is a need for efficient applications that can handle general pedigree data or mixtures of both population and pedigree data. Even data sets thought to consist of only unrelated individuals may include cryptic relationships that can lead to false positives if not discovered and controlled for. In addition, family designs possess compelling advantages. They are better equipped to detect rare variants, control for population stratification, and facilitate the study of parent-of-origin effects. Pedigrees selected for extreme trait values often segregate a single gene with strong effect. Finally, many pedigrees are available as an important legacy from the era of linkage analysis. Unfortunately, pedigree likelihoods are notoriously hard to compute. In this paper we re-examine the computational bottlenecks and implement ultra-fast pedigree-based GWAS analysis. Kinship coefficients can either be based on explicitly provided pedigrees or automatically estimated from dense markers. Our strategy (a) works for random sample data, pedigree data, or a mix of both; (b) entails no loss of power; (c) allows for any number of covariate adjustments, including correction for population stratification; (d) allows for testing SNPs under additive, dominant, and recessive models; and (e) accommodates both univariate and multivariate quantitative traits. On a typical personal computer (6 CPU cores at 2.67 GHz), analyzing a univariate HDL (high-density lipoprotein) trait from the San Antonio Family Heart Study (935,392 SNPs on 1357 individuals in 124 pedigrees) takes less than 2 minutes and 1.5 GB of memory. Complete multivariate QTL analysis of the three time-points of the longitudinal HDL multivariate trait takes less than 5 minutes and 1.5 GB of memory.
      • Article
      • Peer Reviewed
      UCLA Previously Published Works (2017)
      Since most analysis software for genome-wide association studies (GWAS) currently exploit only unrelated individuals, there is a need for efficient applications that can handle general pedigree data or mixtures of both population and pedigree data. Even datasets thought to consist of only unrelated individuals may include cryptic relationships that can lead to false positives if not discovered and controlled for. In addition, family designs possess compelling advantages. They are better equipped to detect rare variants, control for population stratification, and facilitate the study of parent-of-origin effects. Pedigrees selected for extreme trait values often segregate a single gene with strong effect. Finally, many pedigrees are available as an important legacy from the era of linkage analysis. Unfortunately, pedigree likelihoods are notoriously hard to compute. In this paper, we reexamine the computational bottlenecks and implement ultra-fast pedigree-based GWAS analysis. Kinship coefficients can either be based on explicitly provided pedigrees or automatically estimated from dense markers. Our strategy (a) works for random sample data, pedigree data, or a mix of both; (b) entails no loss of power; (c) allows for any number of covariate adjustments, including correction for population stratification; (d) allows for testing SNPs under additive, dominant, and recessive models; and (e) accommodates both univariate and multivariate quantitative traits. On a typical personal computer (six CPU cores at 2.67 GHz), analyzing a univariate HDL (high-density lipoprotein) trait from the San Antonio Family Heart Study (935,392 SNPs on 1,388 individuals in 124 pedigrees) takes less than 2 min and 1.5 GB of memory. Complete multivariate QTL analysis of the three time-points of the longitudinal HDL multivariate trait takes less than 5 min and 1.5 GB of memory. The algorithm is implemented as the Ped-GWAS Analysis (Option 29) in the Mendel statistical genetics package, which is freely available for Macintosh, Linux, and Windows platforms from http://genetics.ucla.edu/software/mendel.
        Cover page: Fast Genome‐Wide QTL Association Mapping on Pedigree and Population Data
        • Article
        • Peer Reviewed
        UCLA Previously Published Works (2018)
        BACKGROUND: Sex hormones are implicated in the development of diabetes. However, whether genetic variations in sex hormone pathways (SHPs) contribute to the risk of type 2 diabetes mellitus (T2DM) remains to be determined. This study investigated associations between genetic variations in all candidate genes in SHPs and T2DM risk among a cohort of women participating in the Womens Health Initiative (WHI). METHODS: Single nucleotide polymorphisms (SNPs) located within 30 kb upstream and downstream of SHP genes were comprehensively examined in 8180 African American, 3498 Hispanic American, and 3147 European American women in the WHI. In addition, whether significant SNPs would be replicated in independent populations was examined. RESULTS: After adjusting for age, region, and ancestry estimates and correcting for multiple testing, seven SNPs were significantly associated with the risk of T2DM among Hispanic American women were identified in the progesterone receptor (PGR) gene, with rs948516 showing the greatest significance (odds ratio 0.67; 95% confidence interval 0.57-0.78; P = 8.8 × 10-7 ; false discovery rate, Q = 7.8 × 10-4 ). These findings were not replicated in other ethnic groups in the WHI or in sex-combined analyses in replication studies. CONCLUSION: Significant signals were identified implicating the PGR gene in T2DM development in Hispanic American women in the WHI, which are consistent with genome-wide association studies findings linking PGR to glucose homeostasis. Nevertheless, the PGR SNPs-T2DM association was not statistically significant in other ethnic populations. Further studies, especially sex-specific analyses, are needed to confirm the findings and clarify the role of SHPs in T2DM.
          Cover page: Genetic variants in sex hormone pathways and the risk of type 2 diabetes among African American, Hispanic American, and European American postmenopausal women in the US.
          • Article
          • Peer Reviewed
          UCLA Previously Published Works (2014)

          Background

          Although cardiovascular disease (CVD) and type 2 diabetes mellitus (T2D) share many common risk factors, potential molecular mechanisms that may also be shared for these 2 disorders remain unknown.

          Methods and results

          Using an integrative pathway and network analysis, we performed genome-wide association studies in 8155 blacks, 3494 Hispanic American, and 3697 Caucasian American women who participated in the national Women's Health Initiative single-nucleotide polymorphism (SNP) Health Association Resource and the Genomics and Randomized Trials Network. Eight top pathways and gene networks related to cardiomyopathy, calcium signaling, axon guidance, cell adhesion, and extracellular matrix seemed to be commonly shared between CVD and T2D across all 3 ethnic groups. We also identified ethnicity-specific pathways, such as cell cycle (specific for Hispanic American and Caucasian American) and tight junction (CVD and combined CVD and T2D in Hispanic American). In network analysis of gene-gene or protein-protein interactions, we identified key drivers that included COL1A1, COL3A1, and ELN in the shared pathways for both CVD and T2D. These key driver genes were cross-validated in multiple mouse models of diabetes mellitus and atherosclerosis.

          Conclusions

          Our integrative analysis of American women of 3 ethnicities identified multiple shared biological pathways and key regulatory genes for the development of CVD and T2D. These prospective findings also support the notion that ethnicity-specific susceptibility genes and process are involved in the pathogenesis of CVD and T2D.
            Cover page: Shared Molecular Pathways and Gene Networks for Cardiovascular Disease and Type 2 Diabetes Mellitus in Women Across Diverse Ethnicities
            • Article
            • Peer Reviewed
            UC San Diego Previously Published Works (2019)
            Polygenic risk scores (PRSs) are weighted sums of risk allele counts of single-nucleotide polymorphisms (SNPs) associated with a disease or trait. PRSs are typically constructed based on published results from Genome-Wide Association Studies (GWASs), and the majority of which has been performed in large populations of European ancestry (EA) individuals. Although many genotype-trait associations have generalized across populations, the optimal choice of SNPs and weights for PRSs may differ between populations due to different linkage disequilibrium (LD) and allele frequency patterns. We compare various approaches for PRS construction, using GWAS results from both large EA studies and a smaller study in Hispanics/Latinos: The Hispanic Community Health Study/Study of Latinos (HCHS/SOL, n=12,803 ). We consider multiple approaches for selecting SNPs and for computing SNP weights. We study the performance of the resulting PRSs in an independent study of Hispanics/Latinos from the Women's Health Initiative (WHI, n=3,582 ). We support our investigation with simulation studies of potential genetic architectures in a single locus. We observed that selecting variants based on EA GWASs generally performs well, except for blood pressure trait. However, the use of EA GWASs for weight estimation was suboptimal. Using non-EA GWAS results to estimate weights improved results.
              Cover page: Generalizing polygenic risk scores from Europeans to Hispanics/Latinos
              • Article
              • Peer Reviewed
              UC San Diego Previously Published Works (2022)
              Much remains unknown about the role of added sugar in relation to cardiovascular disease (CVD) and the relative contributions of sugar-sweetened beverages (SSB) or artificially sweetened beverages (ASB) to CVD risk. Among the 109,034 women who participated in Women's Health Initiative, we assessed average intakes of added sugar, SSB and ASB, and conducted Cox regression to estimate the hazard ratios (HRs) and their 95% confidence intervals for CVD risk. The consistency of findings was compared to a network meta-analysis of all available cohorts. During an average of 17.4 years of follow-up, 11,597 cases of total CVD (nonfatal myocardial infarction, coronary heart disease (CHD) death, stroke, coronary revascularization, and/or incident heart failure) were confirmed. Added sugar as % energy intake daily (%EAS) at ≥15.0% was positively associated with total CVD (HR = 1.08 [1.01, 1.15]) and CHD (HR = 1.20 [1.09, 1.32]). There was also a higher risk of total CVD associated with ≥1 serving of SSB intake per day (HR = 1.29 [1.17, 1.42]), CHD (1.35 [1.16, 1.57]), and total stroke (1.30 [1.10, 1.53]). Similarly, ASB intake was associated with an increased risk of CVD (1.14 [1.03, 1.26]) and stroke (1.24 [1.04, 1.48]). According to the network meta-analysis, there was a large amount of heterogeneity across studies, showing no consistent pattern implicating added sugar, ASB, or SSB in CVD outcomes. A diet containing %EAS ≥15.0% and consuming ≥1 serving of SSB or ASB may be associated with a higher CVD incidence. The relative contribution of added sugar, SSB, and ASB to CVD risk warrants further investigation.
                Cover page: Added Sugar, Sugar-Sweetened Beverages, and Artificially Sweetened Beverages and Risk of Cardiovascular Disease: Findings from the Women’s Health Initiative and a Network Meta-Analysis of Prospective Studies
                • Article
                • Peer Reviewed
                UCLA Previously Published Works (2017)
                Cardiovascular diseases (CVD) and type 2 diabetes (T2D) are closely interrelated complex diseases likely sharing overlapping pathogenesis driven by aberrant activities in gene networks. However, the molecular circuitries underlying the pathogenic commonalities remain poorly understood. We sought to identify the shared gene networks and their key intervening drivers for both CVD and T2D by conducting a comprehensive integrative analysis driven by five multi-ethnic genome-wide association studies (GWAS) for CVD and T2D, expression quantitative trait loci (eQTLs), ENCODE, and tissue-specific gene network models (both co-expression and graphical models) from CVD and T2D relevant tissues. We identified pathways regulating the metabolism of lipids, glucose, and branched-chain amino acids, along with those governing oxidation, extracellular matrix, immune response, and neuronal system as shared pathogenic processes for both diseases. Further, we uncovered 15 key drivers including HMGCR, CAV1, IGF1 and PCOLCE, whose network neighbors collectively account for approximately 35% of known GWAS hits for CVD and 22% for T2D. Finally, we cross-validated the regulatory role of the top key drivers using in vitro siRNA knockdown, in vivo gene knockout, and two Hybrid Mouse Diversity Panels each comprised of >100 strains. Findings from this in-depth assessment of genetic and functional data from multiple human cohorts provide strong support that common sets of tissue-specific molecular networks drive the pathogenesis of both CVD and T2D across ethnicities and help prioritize new therapeutic avenues for both CVD and T2D.
                  Cover page: Shared genetic regulatory networks for cardiovascular disease and type 2 diabetes in multiple populations of diverse ethnicities in the United States
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