Grant: Discovery Grants Program - Individual
Memorial University of Newfoundland
$18,000.00
Sampling Designs and Statistical Methods for the Analysis of Complex Life History and Genetic Data
FY 2024-25
Summary
The objective of this research program is to develop novel statistical modeling approaches and methods to identify the genetic basis of complex traits. A complex trait is one that is not straightforwardly determined by a single gene but is instead, determined by numerous genetic and environmental factors. It is difficult to isolate how any genetic factor affects the way a trait is expressed. This is because the effect of any factor may be very small, or some factors may be rare genetic variants that are not often observed in the population. Also, these factors may interact with each other and the environment. Development of informative statistical modeling approaches, appropriate and efficient sampling designs and methods of analysis is essential to detect and understand the role of such factors. My program of research, and the plan proposed for the next 5 years is mainly focused on development of novel informative statistical modeling approaches and efficient methods for the analysis of time-to-event complex traits and life history data and evaluation of sampling designs for life history analysis. Providing more informative and powerful analytical tools will enable researchers to better understand the underlying biological mechanisms of complex traits. In addition, this program will enable to train highly qualified personnel in Statistical Sciences.
Program
Discovery Grants Program - Individual
Recipient
Location
Newfoundland and Labrador, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
RGPIN-2020-05528
Name nom
Yilmaz, Yildiz
Department département
Mathematics and Statistics
Organization id
89
Institution établissement
Memorial University of Newfoundland
Province en
Newfoundland and Labrador
Province fr
Terre-Neuve-et-Labrador
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2024
Competition year année de concours
2020
Award amount
18000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1508
Committee name en
Mathematics and Statistics
Committee name fr
Mathématiques et statistique
Area of application code
1201
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Life sciences (including biotechnology)
Area of application fr
Sciences de la vie (y compris biotechnologie)
Research subject code
3005
Research subject group en
Statistics and probability
Research subject group fr
Statistique et probabilité
Research subject en
Biostatistics
Research subject fr
Biostatistiques
Field of research list names en
Not available
Field of research list names fr
Non disponible
Socioeconomic objective list names en
Not available
Socioeconomic objective list names fr
Non disponible
Application title
Sampling Designs and Statistical Methods for the Analysis of Complex Life History and Genetic Data
Keywords
biostatistics; survival analysis; multi-state modelling; sampling designs; response-selective sampling; statistical genetics; likelihood-based methods; estimating equations; multivariate analysis; causal inference
Application summary
The objective of this research program is to develop novel statistical modeling approaches and methods to identify the genetic basis of complex traits. A complex trait is one that is not straightforwardly determined by a single gene but is instead, determined by numerous genetic and environmental factors. It is difficult to isolate how any genetic factor affects the way a trait is expressed. This is because the effect of any factor may be very small, or some factors may be rare genetic variants that are not often observed in the population. Also, these factors may interact with each other and the environment. Development of informative statistical modeling approaches, appropriate and efficient sampling designs and methods of analysis is essential to detect and understand the role of such factors. My program of research, and the plan proposed for the next 5 years is mainly focused on development of novel informative statistical modeling approaches and efficient methods for the analysis of time-to-event complex traits and life history data and evaluation of sampling designs for life history analysis. Providing more informative and powerful analytical tools will enable researchers to better understand the underlying biological mechanisms of complex traits. In addition, this program will enable to train highly qualified personnel in Statistical Sciences.