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You searched for +publisher:"Penn State University" +contributor:("Marylyn Deriggi Ritchie, Dissertation Advisor/Co-Advisor"). Showing records 1 – 4 of 4 total matches.

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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

 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)

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

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

APA (6th 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 (16th 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 (7th 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

 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)

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 (6th 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 (16th 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 (7th 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

 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)

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 (6th 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 (16th 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 (7th Edition):

Verma, Shefali Setia. “INVESTIGATING COMPUTATIONAL METHODS TO MODEL THE GENOTYPIC AND PHENOTYPIC COMPLEXITY OF ADVERSE HEALTH OUTCOMES: UNDERSTANDING UNDERCOVER HERITABILITY.” 2018. Web. 16 Apr 2021.

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

 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)

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 (6th 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 (16th 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 (7th 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

.