TY - JOUR
T1 - SimPEL
T2 - Simulation-based power estimation for sequencing studies of low-prevalence conditions
AU - Mak, Lauren
AU - Li, Minghao
AU - Cao, Chen
AU - Gordon, Paul
AU - Tarailo-Graovac, Maja
AU - Bousman, Chad
AU - Wang, Pei
AU - Long, Quan
N1 - Publisher Copyright:
© 2018 WILEY PERIODICALS, INC.
PY - 2018/7
Y1 - 2018/7
N2 - Power estimations are important for optimizing genotype-phenotype association study designs. However, existing frameworks are designed for common disorders, and thus ill-suited for the inherent challenges of studies for low-prevalence conditions such as rare diseases and infrequent adverse drug reactions. These challenges include small sample sizes and the need to leverage genetic annotation resources in association analyses for the purpose of ranking potential causal genes. We present SimPEL, a simulation-based program providing power estimations for the design of low-prevalence condition studies. SimPEL integrates the usage of gene annotation resources for association analyses. Customizable parameters, including the penetrance of the putative causal allele and the employed pathogenic scoring system, allow SimPEL to realistically model a large range of study designs. To demonstrate the effects of various parameters on power, we estimated the power of several simulated designs using SimPEL and captured power trends in agreement with observations from current literature on low-frequency condition studies. SimPEL, as a tool, provides researchers studying low-frequency conditions with an intuitive and highly flexible avenue for statistical power estimation. The platform-independent “batteries included” executable and default input files are available at https://github.com/precisionomics/SimPEL.
AB - Power estimations are important for optimizing genotype-phenotype association study designs. However, existing frameworks are designed for common disorders, and thus ill-suited for the inherent challenges of studies for low-prevalence conditions such as rare diseases and infrequent adverse drug reactions. These challenges include small sample sizes and the need to leverage genetic annotation resources in association analyses for the purpose of ranking potential causal genes. We present SimPEL, a simulation-based program providing power estimations for the design of low-prevalence condition studies. SimPEL integrates the usage of gene annotation resources for association analyses. Customizable parameters, including the penetrance of the putative causal allele and the employed pathogenic scoring system, allow SimPEL to realistically model a large range of study designs. To demonstrate the effects of various parameters on power, we estimated the power of several simulated designs using SimPEL and captured power trends in agreement with observations from current literature on low-frequency condition studies. SimPEL, as a tool, provides researchers studying low-frequency conditions with an intuitive and highly flexible avenue for statistical power estimation. The platform-independent “batteries included” executable and default input files are available at https://github.com/precisionomics/SimPEL.
KW - adverse drug reactions
KW - association analyses
KW - genetic variant annotation
KW - genome-wide sequencing
KW - power estimation
KW - rare disease
UR - http://www.scopus.com/inward/record.url?scp=85049050190&partnerID=8YFLogxK
U2 - 10.1002/gepi.22129
DO - 10.1002/gepi.22129
M3 - Article
C2 - 29790190
AN - SCOPUS:85049050190
SN - 0741-0395
VL - 42
SP - 480
EP - 487
JO - Genetic Epidemiology
JF - Genetic Epidemiology
IS - 5
ER -