Simulation of Finnish population history, guided by empirical genetic data, to assess power of rare-variant tests in Finland.
Finnish samples have been extensively utilized in studying single-gene disorders, where the founder effect has clearly aided in discovery, and more recently in genome-wide association studies of complex traits, where the founder effect has had less obvious impacts. As the field starts to explore rare variants' contribution to polygenic traits, it is of great importance to characterize and confirm the Finnish founder effect in sequencing data and to assess its implications for rare-variant association studies. Here, we employ forward simulation, guided by empirical deep resequencing data, to model the genetic architecture of quantitative polygenic traits in both the general European and the Finnish populations simultaneously. We demonstrate that power of rare-variant association tests is higher in the Finnish population, especially when variants' phenotypic effects are tightly coupled with fitness effects and therefore reflect a greater contribution of rarer variants. SKAT-O, variable-threshold tests, and single-variant tests are more powerful than other rare-variant methods in the Finnish population across a range of genetic models. We also compare the relative power and efficiency of exome array genotyping to those of high-coverage exome sequencing. At a fixed cost, less expensive genotyping strategies have far greater power than sequencing; in a fixed number of samples, however, genotyping arrays miss a substantial portion of genetic signals detected in sequencing, even in the Finnish founder population. As genetic studies probe sequence variation at greater depth in more diverse populations, our simulation approach provides a framework for evaluating various study designs for gene discovery.
Division of Endocrinology, Boston Children's Hospital, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA.
Sophie R
SR
Wang
Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Program in Biophysics, Graduate School of Arts and Sciences, Harvard University, Cambridge, MA 02138, USA.
Vineeta
V
Agarwala
Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA.
Jason
J
Flannick
Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Charleston W K
CW
Chiang
Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Department of Molecular Biology, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
David
D
Altshuler
Division of Endocrinology, Boston Children's Hospital, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA. Electronic address: joelh@broadinstitute.org.
Joel N
JN
Hirschhorn
D003198:Computer Simulation; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics; D059472:Exome / Q000235:genetics; D005387:Finland; D018703:Founder Effect; D006801:Humans; D008957:Models, Genetic; D020412:Multifactorial Inheritance / Q000235:genetics; D011153:Population / Q000235:genetics*; D044465:White People / Q000235:genetics*