Bibliographic record
Abstract
This dissertation is the culmination of my graduate studies in the laboratory of Jake Lusis at UCLA. The research presented here utilizes systems genetics studies performed in mice to aid in discovery in human disease in three separate studies. A significant portion of disease-oriented research is performed in mice, but a major criticism from the medical community is that laboratory mice are generally inbred and thus have no genetic variation among individuals. Almost 15 years ago, the Lusis lab developed a novel genetic resource for association analysis in the mouse called the Hybrid Mouse Diversity Panel (HMDP). The HMDP is a panel of inbred mouse strains that was developed for performing association studies with adequate statistical power and resolution for mapping of complex traits. Mouse genome wide association studies (GWAS) studies are a powerful tool and can be performed relatively easily, but translating the data obtained from these studies to human disease is still in its infancy. My dissertation work reveals three different novel approaches to the utilization of data from GWAS studies performed on the HMDP for translation into human disease processes, namely cardiovascular disease. The first study utilizes novel genetic signatures in murine macrophages to predict disease incidence and survival in humans. The second study utilizes a traditional GWAS to candidate gene discovery to elucidate the mechanisms underlying cardiac remodeling in humans. Lastly, the third study utilizes mouse GWAS data for novel heart failure biomarker discovery in humans. As an introduction to this dissertation, Chapter 1 briefly summarizes the history of GWAS in mice using the HMDP and GWAS in humans. Chapter 2 is a completed and accepted first-author manuscript entitled “Natural diversity reveals macrophage activation spectra predictive of inflammation and cancer survival.” Chapter 3 explores the role of CD200, a candidate gene obtained from a large heart failure GWAS study in mice, and it’s receptor, CD200R1 in cardiac homeostasis and injury. Chapter 4 describes a novel approach to biomarker discovery for human heart failure using data from a large heart failure GWAS study. Chapter 5 is a departure from mouse systems genetics. In this chapter, I describe the strengths and pitfalls of exome sequencing. In addition, I describe two cases of rare cardiovascular disease in which exome sequencing is utilized to find causal variants of disease. Ultimately, I’d like to use what I’ve learned in my studies of mouse genetics and translate this to discovery in human disease. In conclusion, this dissertation work contributes significant findings to the expanding knowledge of utilizing mouse GWAS for discovery in human disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".