title: Partitioning reference intervals by use of genetic information.
abstract: BACKGROUND
  Reference intervals that incorporate genetic information could reduce the misidentification of unusual test results caused by non-disease-associated genetic variation and increase the detection of results indicating underlying pathology. Subdividing reference groups by genetic effects, however, may lead to increased uncertainty around reference interval endpoints (because of the smaller subgroup sample sizes), thus offsetting any benefits.
  
  
  METHODS
  We evaluated CLSI guidelines to develop a method appropriate for partitioning reference intervals on the basis of genetic variants with dominant or recessive effects. This method uses information available before reference samples are recruited, thus allowing a preliminary decision regarding partitioning to be made before sampling. We used this method to evaluate the example of Gilbert syndrome.
  
  
  RESULTS
  The decision point for partitioning occurs when the percentage of total variance attributable to a dominant or recessive genetic polymorphism exceeds 4%. Similarly, partitioning decision curves are presented based on difference in means between 2 subgroups, sample SD, and subgroup or allele frequency. Laboratory-specific partitioned reference intervals for Gilbert syndrome appear to be statistically warranted for white and African-American populations, but not for Asian populations.
  
  
  CONCLUSIONS
  We present a simple method to evaluate whether partitioning based on dominant or recessive genetic effects is statistically justified. Important limitations remain that, in many situations, will preclude integration of genetic, laboratory, and clinical information. As society moves toward personalized medicine, additional research is needed on how to evaluate patient normality while accounting for additive genetic, multigenic, and other multifactorial effects.
authors.affiliation: Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT 84112, USA. brian.shirts@hsc.utah.edu
authors.forename: Brian H
authors.initials: BH
authors.lastname: Shirts
authors.forename: Andrew R
authors.initials: AR
authors.lastname: Wilson
authors.forename: Brian R
authors.initials: BR
authors.lastname: Jackson
mesh_terms: D001663:Bilirubin / Q000097:blood; D019411:Clinical Laboratory Techniques / Q000592:standards / Q000706:statistics & numerical data*; D016001:Confidence Intervals; D005260:Female; D005787:Gene Frequency; D005826:Genetics, Medical / Q000592:standards / Q000706:statistics & numerical data*; D005838:Genotype; D005878:Gilbert Disease / Q000097:blood / Q000235:genetics; D014453:Glucuronosyltransferase / Q000235:genetics; D017408:Guidelines as Topic; D006801:Humans; D008297:Male; D008954:Models, Biological; D015233:Models, Statistical; D012016:Reference Values; D018401:Sample Size; D000099148:UGT1A1 Enzyme
chemical_list: D001663:Bilirubin; D014453:Glucuronosyltransferase; D000099148:UGT1A1 Enzyme