Quantification of expected bi-allelic genetic variants' rate
Advances in genomic technologies have led to the identification of many novel disease-gene associations, allowing medical diagnoses to be more precise and tailored to an individual. However, the high number of variants present in each individual represents a significant challenge for the implementation of genomic medicine. The goal of this project is to enable the identification of novel genes associated with recessive disorders.
Researchers have already developed tools enabling the prioritization of genes potentially associated with dominant disorders. For example, constraint scores identify highly conserved genes (few observed variants in the population). Variants in these genes are investigated as disease-causing assuming that two functional copies are essential to a specific biological process.
The identification of novel genes associated with recessive disorders is more difficult. Several research groups have developed bioinformatic approaches to quantify enrichment for bi-allelic variants (homozygote and compound heterozygote variants). However, they are cohort dependent and do not easily apply to different populations.
The students will be expected to process a large dataset of familial samples (2 or more first-degree relatives) using existing approaches to calculate the expected number of bi-allelic variants both genome-wide and at the gene-level.
This is an UNPAID research project.
Faculty Advisor
- Professor: Ali Gharavi
- Department/School: Medicine
- Location: CUIMC
- The mission of the Center for Precision Medicine and Genomics (CPMG) is to improve human health through high quality research, education and clinical care.
Project Timeline
- Earliest starting date: 3/1/2021
- End date:
- Number of hours per week of research expected during Spring 2021: ~10
- Number of hours per week of research expected during Summer 2021: ~35
Candidate requirements
- Skill sets: Fluent in at least one programing language (R, Python, Perl, Java), at least one course in statistics and knowledge in genetics
- Student eligibility:
freshman,sophomore,junior, senior, master’s - International students on F1 or J1 visa: eligible
- Academic Credit Possible: Yes