+publisher:"Penn State University" +contributor:("Marylyn Deriggi Ritchie, Dissertation Advisor/Co-Advisor")

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Penn State University

1. Li, Ruowang. USING LARGE-SCALE GENOMICS DATA TO UNDERSTAND THE GENETIC BASIS OF COMPLEX TRAITS.

Degree: 2016, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/13423rvl5032

► With the arrival of big data in genetics in the past decade, the field has experienced drastic changes. One game-changing breakthrough in genetics was the… (more)

▼ With the arrival of big data in genetics in the past decade, the field has experienced drastic changes. One game-changing breakthrough in genetics was the invention of genotyping and sequencing technology that allows researchers to examining single nucleotide polymorphisms (SNPs) across the entire genome. The other major breakthrough was the identification of haplotypes of common alleles in major human populations, which permitted the design of genotyping assays that effectively cover entire human genomes at a resolution appropriate for genetic mapping. Together, these technology breakthroughs have permitted researchers to carry out Genome Wide Association Studies (GWAS) on a wide range of traits including, for example, height and disease status. With GWAS, causal SNPs have been identified for some Mendelian traits, but for more complex genetic traits, the genetic heritability explained by the associated SNPs are low. In addition, high-throughput technologies to generate other types of -omics data such as gene expression, DNA methylation, and protein levels data have also emerged recently. How to best utilize the SNP data and other multi-omics data to understand genetic traits is one of the most important questions in the field today. With the increasing prevalence of multi-omics data, new types of analysis schemes and tools are needed to handle the additional complexity of the data. In particular, two areas of method development are in great need. First, statistical methods employed by GWAS do not consider the potential interacting relationships among genetic loci. Thus, methods that can explore the joint effect between multiple genetic loci or genetic factors could unveil new associations. Second, different types of –omics data may give distinctive representations of the overall biological system. By combining multi-omics data, we could potentially aggregate non-overlapping information from each individual data types. Thus, the focus of this dissertation is on developing and improving computational methods that can jointly model multiple types of genomics data. First, an evaluation of an existing method, grammatical evolution neural network, was conducted to identify the optimal algorithm settings for the detection of genetic associations. It was found that under certain algorithm settings, the neural networks have been restricted to one-layer simple network. Using a parameter sweep approach, the analysis identified optimal settings that allow for building more flexible network structures. Then, the algorithm was applied to integrate multi-omics data to model drug-induced cytotoxicity for a number of cancer drugs. By combining different types of –omics data including SNPs, gene expression and methylation levels, we were able to model a higher portion of the observed variability than any individual data type alone. However, one drawback of the existing neural network approach is the limited interpretability. To this end, a new algorithm based on Bayesian Networks was created. One novelty of the approach is the… Advisors/Committee Members: Advisor%22%29&pagesize-30">Marylyn Deriggi Ritchie, Dissertation Advisor/Co-Advisor, Marylyn Deriggi Ritchie, Committee Chair/Co-Chair, Ross Cameron Hardison, Committee Member, Shaun A Mahony, Committee Member, Le Bao, Outside Member.

Subjects/Keywords: Bioinformatics; Genomics; Statistics; Data Integration; Bayesian Network; Epistasis

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APA (6^th Edition):

Li, R. (2016). USING LARGE-SCALE GENOMICS DATA TO UNDERSTAND THE GENETIC BASIS OF COMPLEX TRAITS. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/13423rvl5032

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Li, Ruowang. “USING LARGE-SCALE GENOMICS DATA TO UNDERSTAND THE GENETIC BASIS OF COMPLEX TRAITS.” 2016. Thesis, Penn State University. Accessed April 16, 2021. https://submit-etda.libraries.psu.edu/catalog/13423rvl5032.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Li, Ruowang. “USING LARGE-SCALE GENOMICS DATA TO UNDERSTAND THE GENETIC BASIS OF COMPLEX TRAITS.” 2016. Web. 16 Apr 2021.

Vancouver:

Li R. USING LARGE-SCALE GENOMICS DATA TO UNDERSTAND THE GENETIC BASIS OF COMPLEX TRAITS. [Internet] [Thesis]. Penn State University; 2016. [cited 2021 Apr 16]. Available from: https://submit-etda.libraries.psu.edu/catalog/13423rvl5032.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Li R. USING LARGE-SCALE GENOMICS DATA TO UNDERSTAND THE GENETIC BASIS OF COMPLEX TRAITS. [Thesis]. Penn State University; 2016. Available from: https://submit-etda.libraries.psu.edu/catalog/13423rvl5032

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

2. Basile, Anna Okula. CONTINUING TO SEARCH FOR THE MISSING HERITABILITY USING BIOLOGICALLY-INSPIRED AND DATA-DRIVEN APPROACHES.

Degree: 2018, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/14970azo121

► Genome-wide association studies (GWAS) have been a commonly utilized technique in complex disease research for the identification of loci associated with common, polygenic traits. These… (more)

▼ Genome-wide association studies (GWAS) have been a commonly utilized technique in complex disease research for the identification of loci associated with common, polygenic traits. These studies have been influential in identifying hundreds of reproducible variants in many traits. However, for most complex traits, GWAS have only explained a portion of the genetic contribution, thus revealing the problem of "missing heritability", or unexplained genetic variance. The genetic underpinnings of these traits are likely influenced by multiple components including structural variants, rare sequence variants, epistatic interactions, gene-environment interactions, epigenetics, and even our phenotypic definition. A deeper understanding of the interplay between the genetic and phenotypic complexities of complex traits is needed to fully elucidate their intricate etiologies and ultimately progress toward more precise clinical care. The dissertation herein aims to continue the search for the missing heritability by concentrating on two components that were not addressed in common variant GWAS, and that have been implicated in elucidating additional trait variability and disease risk. The first is examining the role of rare sequence variants. To date, rare susceptibility loci have been inculpated in numerous multifactorial conditions providing compelling evidence that rare variants are involved in the genetic etiology of complex traits. However, due to low allelic frequencies, rare variant analyses are challenging and often suffer from a loss of statistical power. Further, few methods provide a comprehensive platform for robust rare variant analysis. Aims described in this dissertation address this challenge by evaluating select statistical tests appropriate for rare variants and integrating components of the promising tests to create a comprehensive rare variant analysis method for DNA sequence data. The second factor undertaken in this work is accurate phenotype characterization which is often muddled by the presence of phenotype or trait heterogeneity introduced by treating a multifactorial disease as a single phenotype. Heterogeneity can result in substantially decreased ability to detect a true association between a disease and a locus. This confounding factor is confronted with the evaluation and application of unsupervised machine learning approaches to rich phenotypic data extracted from electronic health records (EHRs) for the creation of homogeneous patient subsets with more consistent underlying factors contributing to disease. The methods employed in these aims have the potential to uncover new relationships between rare variants and complex traits, identify phenotypic patterns in clinical EHR-derived data, unveil important biological complexities, and ultimately assess individual disease susceptibility. Advisors/Committee Members: Advisor%22%29&pagesize-30">Marylyn Deriggi Ritchie, Dissertation Advisor/Co-Advisor, Marylyn Deriggi Ritchie, Committee Chair/Co-Chair, Santhosh Girirajan, Committee Member, Ross Cameron Hardison, Committee Member, George H Perry, Outside Member.

Subjects/Keywords: human genetics; complex disease; machine learning; informatics; pattern recognition; rare variants; missing heritability

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Basile, A. O. (2018). CONTINUING TO SEARCH FOR THE MISSING HERITABILITY USING BIOLOGICALLY-INSPIRED AND DATA-DRIVEN APPROACHES. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/14970azo121

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Basile, Anna Okula. “CONTINUING TO SEARCH FOR THE MISSING HERITABILITY USING BIOLOGICALLY-INSPIRED AND DATA-DRIVEN APPROACHES.” 2018. Thesis, Penn State University. Accessed April 16, 2021. https://submit-etda.libraries.psu.edu/catalog/14970azo121.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Basile, Anna Okula. “CONTINUING TO SEARCH FOR THE MISSING HERITABILITY USING BIOLOGICALLY-INSPIRED AND DATA-DRIVEN APPROACHES.” 2018. Web. 16 Apr 2021.

Vancouver:

Basile AO. CONTINUING TO SEARCH FOR THE MISSING HERITABILITY USING BIOLOGICALLY-INSPIRED AND DATA-DRIVEN APPROACHES. [Internet] [Thesis]. Penn State University; 2018. [cited 2021 Apr 16]. Available from: https://submit-etda.libraries.psu.edu/catalog/14970azo121.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Basile AO. CONTINUING TO SEARCH FOR THE MISSING HERITABILITY USING BIOLOGICALLY-INSPIRED AND DATA-DRIVEN APPROACHES. [Thesis]. Penn State University; 2018. Available from: https://submit-etda.libraries.psu.edu/catalog/14970azo121

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

3. Verma, Shefali Setia. INVESTIGATING COMPUTATIONAL METHODS TO MODEL THE GENOTYPIC AND PHENOTYPIC COMPLEXITY OF ADVERSE HEALTH OUTCOMES: UNDERSTANDING UNDERCOVER HERITABILITY.

Degree: 2018, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/15006szs14

► Genome-wide association studies (GWAS) are the most popular and widely conducted experiments to understand the genetic architecture of common diseases. Though GWAS have been successful… (more)

▼ Genome-wide association studies (GWAS) are the most popular and widely conducted experiments to understand the genetic architecture of common diseases. Though GWAS have been successful in identifying many common genetic variants associated with common complex diseases, these studies have some shortcomings in explaining genetic heritability (specifically the broad-sense heritability). Thus, the genetic architecture of complex diseases can be further understood by exploring dominance and interacting variance components along with additive effects. Testing of common variants to understand genetic etiology of common diseases is commonly referred to as Common Disease Common Variants (CDCV) hypothesis where it is believed that many high frequency, or common genetic variants could have large effects on common disease risk. Due to the low cost of sequencing and advancement in technology, a plethora of sequencing data has also been generated which helps in identifying the low frequency or rare genetic variants. Another alternative hypothesis, the Common Disease Rare Variant (CDRV) hypothesis, suggests that low frequency (or rare) variants with high penetrance could largely affect the susceptibility to common genetic diseases. Many pieces of evidence for CDCV and CDRV hypotheses exist in the literature. Thus, both common and rare variant association studies (GWAS and Rare Variant Association Studies or RVAS) have shown importance in understanding missing heritability. To no surprise, it is likely that missing genotypic variance components can be explained by studying common and rare variants together and by integrating both additive and non-additive effects. The aims described in this thesis, address some of the challenges associated with additive and non-additive effect detection in common and rare genetic variants to apply them to dissect the heritability for a disease trait –steps towards uncovering the mystery of heritability. Advisors/Committee Members: Advisor%22%29&pagesize-30">Marylyn Deriggi Ritchie, Dissertation Advisor/Co-Advisor, Shaun A Mahony, Committee Chair/Co-Chair, Stephen Wade Schaeffer, Committee Member, Cooduvalli S Shashikant, Committee Member, William S. Bush, Outside Member.

Subjects/Keywords: Association Studies; GWAS; Rare Variants; Common Variants; Epistasis; Genetic Etiology; Complex Traits; Heritability

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Verma, S. S. (2018). INVESTIGATING COMPUTATIONAL METHODS TO MODEL THE GENOTYPIC AND PHENOTYPIC COMPLEXITY OF ADVERSE HEALTH OUTCOMES: UNDERSTANDING UNDERCOVER HERITABILITY. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/15006szs14

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Verma, Shefali Setia. “INVESTIGATING COMPUTATIONAL METHODS TO MODEL THE GENOTYPIC AND PHENOTYPIC COMPLEXITY OF ADVERSE HEALTH OUTCOMES: UNDERSTANDING UNDERCOVER HERITABILITY.” 2018. Thesis, Penn State University. Accessed April 16, 2021. https://submit-etda.libraries.psu.edu/catalog/15006szs14.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Vancouver:

Verma SS. INVESTIGATING COMPUTATIONAL METHODS TO MODEL THE GENOTYPIC AND PHENOTYPIC COMPLEXITY OF ADVERSE HEALTH OUTCOMES: UNDERSTANDING UNDERCOVER HERITABILITY. [Internet] [Thesis]. Penn State University; 2018. [cited 2021 Apr 16]. Available from: https://submit-etda.libraries.psu.edu/catalog/15006szs14.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Verma SS. INVESTIGATING COMPUTATIONAL METHODS TO MODEL THE GENOTYPIC AND PHENOTYPIC COMPLEXITY OF ADVERSE HEALTH OUTCOMES: UNDERSTANDING UNDERCOVER HERITABILITY. [Thesis]. Penn State University; 2018. Available from: https://submit-etda.libraries.psu.edu/catalog/15006szs14

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

4. Hall, Molly Ann. Beyond genome-wide association studies (GWAS): Emerging methods for investigating complex associations for common traits.

Degree: 2015, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/26751

► Genome-wide association studies (GWAS) have identified numerous loci associated with human phenotypes. This approach, however, does not consider the richly diverse and complex environment with… (more)

▼ Genome-wide association studies (GWAS) have identified numerous loci associated with human phenotypes. This approach, however, does not consider the richly diverse and complex environment with which humans interact throughout the life course, nor does it allow for interrelationships among genetic loci and across traits. Methods that embrace pleiotropy (the effect of one locus on more than one trait), gene-environment (GxE) and gene-gene (GxG) interactions will further unveil the impact of alterations in biological pathways and identify genes that are only involved with disease in the context of the environment. This valuable information can be used to assess personal risk and choose the most appropriate medical interventions based on an individual’s genotype and environment. Additionally, a richer picture of the genetic and environmental aspects that impact complex disease will inform environmental regulations to protect vulnerable populations. Three key limitations of GWAS lead to an inability to robustly model trait prediction in a manner that reflects biological complexity: 1) GWAS explore traits in isolation, one phenotype at a time, preventing investigators from uncovering relationships that exist among multiple traits; 2) GWAS do not account for the exposome; rather, they simply explore the effect of genetic loci on an outcome; and 3) GWAS do not allow for interactions between genetic loci, despite the complexity that exists in biology. The aims described in this dissertation address these limitations. Methods employed in each aim have the potential to: uncover genetic interactions, unveil complex biology behind phenotype networks, inform public policy decisions concerning environmental exposures, and ultimately assess individual disease-risk. Advisors/Committee Members: Advisor%22%29&pagesize-30">Marylyn Deriggi Ritchie, Dissertation Advisor/Co-Advisor, Marylyn Deriggi Ritchie, Committee Chair/Co-Chair, Santhosh Girirajan, Committee Chair/Co-Chair, Scott Brian Selleck, Committee Member, Ross Cameron Hardison, Committee Member, George H Perry, Committee Member, Catherine Mc Carty, Special Member.

Subjects/Keywords: gene-gene interactions; epistasis; PheWAS; phenome; EWAS; exposome; gene-environment interactions; complex traits

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Hall, M. A. (2015). Beyond genome-wide association studies (GWAS): Emerging methods for investigating complex associations for common traits. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/26751

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Hall, Molly Ann. “Beyond genome-wide association studies (GWAS): Emerging methods for investigating complex associations for common traits.” 2015. Thesis, Penn State University. Accessed April 16, 2021. https://submit-etda.libraries.psu.edu/catalog/26751.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Hall, Molly Ann. “Beyond genome-wide association studies (GWAS): Emerging methods for investigating complex associations for common traits.” 2015. Web. 16 Apr 2021.

Vancouver:

Hall MA. Beyond genome-wide association studies (GWAS): Emerging methods for investigating complex associations for common traits. [Internet] [Thesis]. Penn State University; 2015. [cited 2021 Apr 16]. Available from: https://submit-etda.libraries.psu.edu/catalog/26751.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hall MA. Beyond genome-wide association studies (GWAS): Emerging methods for investigating complex associations for common traits. [Thesis]. Penn State University; 2015. Available from: https://submit-etda.libraries.psu.edu/catalog/26751

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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