TY - JOUR
T1 - Bias in estimates of quantitative-trait-locus effect in genome scans
T2 - Demonstration of the phenomenon and a method-of-moments procedure for reducing bias
AU - Allison, David B.
AU - Fernandez, Jose R.
AU - Heo, Moonseong
AU - Zhu, Shankuan
AU - Etzel, Carol
AU - Beasley, T. Mark
AU - Amos, Christopher I.
N1 - Funding Information:
This research was supported in part by a grant from the Pittsburgh Supercomputing Center and National Institutes of Health grants R01DK51716, P30DK26687, R01ES09912, and R01HG02275. We are grateful to Drs. Gary Gadbury, Daniel Rabinowitz, Daniel Heitjan, and Robert Elston for their helpful comments and Dr. Robert L. Hanson for supplying additional information on the example.
PY - 2002/3
Y1 - 2002/3
N2 - An attractive feature of variance-components methods (including the Haseman-Elston tests) for the detection of quantitative-trait loci (QTL) is that these methods provide estimates of the QTL effect. However, estimates that are obtained by commonly used methods can be biased for several reasons. Perhaps the largest source of bias is the selection process. Generally, QTL effects are reported only at locations where statistically significant results are obtained. This conditional reporting can lead to a marked upward bias. In this article, we demonstrate this bias and show that its magnitude can be large. We then present a simple method-of-moments (MOM)-based procedure to obtain more-accurate estimates, and we demonstrate its validity via Monte Carlo simulation. Finally, limitations of the MOM approach are noted, and we discuss some alternative procedures that may also reduce bias.
AB - An attractive feature of variance-components methods (including the Haseman-Elston tests) for the detection of quantitative-trait loci (QTL) is that these methods provide estimates of the QTL effect. However, estimates that are obtained by commonly used methods can be biased for several reasons. Perhaps the largest source of bias is the selection process. Generally, QTL effects are reported only at locations where statistically significant results are obtained. This conditional reporting can lead to a marked upward bias. In this article, we demonstrate this bias and show that its magnitude can be large. We then present a simple method-of-moments (MOM)-based procedure to obtain more-accurate estimates, and we demonstrate its validity via Monte Carlo simulation. Finally, limitations of the MOM approach are noted, and we discuss some alternative procedures that may also reduce bias.
UR - http://www.scopus.com/inward/record.url?scp=0036178196&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0036178196&partnerID=8YFLogxK
U2 - 10.1086/339273
DO - 10.1086/339273
M3 - Article
C2 - 11836648
AN - SCOPUS:0036178196
SN - 0002-9297
VL - 70
SP - 575
EP - 585
JO - American Journal of Human Genetics
JF - American Journal of Human Genetics
IS - 3
ER -