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Using multiple measures for quantitative trait association analyses: application to estimated glomerular filtration rate.

TitleUsing multiple measures for quantitative trait association analyses: application to estimated glomerular filtration rate.
Publication TypeJournal Article
Year of Publication2013
AuthorsTin A, Colantuoni E, Boerwinkle E, Köttgen A, Franceschini N, Astor BC, Coresh JJ
Secondary AuthorsKao W H L
JournalJ Hum Genet
Volume58
Issue7
Pagination461-6
Date Published2013 Jul
ISSN1435-232X
KeywordsBiomarkers, European Continental Ancestry Group, Genetic Association Studies, Glomerular Filtration Rate, Humans, Kidney, Middle Aged, Phenotype, Polymorphism, Single Nucleotide, Prospective Studies, Quantitative Trait Loci
Abstract

Studies of multiple measures of a quantitative trait can have greater precision and thus statistical power compared with single-measure studies, but this has rarely been studied in the relation to quantitative trait measurement error models in genetic association studies. Using estimated glomerular filtration rate (eGFR), a quantitative measure of kidney function, as an example we constructed measurement error models of a quantitative trait with systematic and random error components. We then examined the effects on precision of the parameter estimate between genetic loci and eGFR, resulting from varying the correlation and contribution of the error components. We also compared the empirical results from three genome-wide association studies (GWAS) of kidney function in 9049 European Americans: a single measure model, a three-measure model of the same biomarker of kidney function and a six-measure model of different biomarkers of kidney function. Simulations showed that given the same amount of overall errors, inclusion of measures with less correlated systematic errors led to greater gain in precision. The empirical GWAS results confirmed that both the three- and six-measure models detected more eGFR-associated genomic loci with stronger statistical association than the single-measure model despite some heterogeneity among the measures. Multiple measures of a quantitative trait can increase the statistical power of a study without additional participant recruitment. However, careful attention must be paid to the correlation of systematic errors and inconsistent associations when different biomarkers or methods are used to measure the quantitative trait.

DOI10.1038/jhg.2013.23
Alternate JournalJ Hum Genet
PubMed ID23535967
PubMed Central IDPMC3711970
Grant ListHHSN268201100009I / HL / NHLBI NIH HHS / United States
R01 HL059367 / HL / NHLBI NIH HHS / United States
HHSN268201100007C / HL / NHLBI NIH HHS / United States
HHSN268201100011I / HL / NHLBI NIH HHS / United States
T32 HL007024 / HL / NHLBI NIH HHS / United States
U01 HG004402 / HG / NHGRI NIH HHS / United States
U01HG004402 / HG / NHGRI NIH HHS / United States
HHSN268201100006C / HL / NHLBI NIH HHS / United States
R01HL087641 / HL / NHLBI NIH HHS / United States
HHSN268201100005I / HL / NHLBI NIH HHS / United States
HHSN268201100007I / HL / NHLBI NIH HHS / United States
R01 HL087641 / HL / NHLBI NIH HHS / United States
R01HL086694 / HL / NHLBI NIH HHS / United States
HHSN268201100012C / HL / NHLBI NIH HHS / United States
UL1RR025005 / RR / NCRR NIH HHS / United States
R01HL59367 / HL / NHLBI NIH HHS / United States
HHSN268201100010C / HL / NHLBI NIH HHS / United States
UL1 RR025005 / RR / NCRR NIH HHS / United States
HHSN268201100008C / HL / NHLBI NIH HHS / United States
HHSN268201100005G / HL / NHLBI NIH HHS / United States
HHSN268201100008I / HL / NHLBI NIH HHS / United States
HHSN268201100011C / HL / NHLBI NIH HHS / United States
R01 HL086694 / HL / NHLBI NIH HHS / United States
HHSN268201100009C / HL / NHLBI NIH HHS / United States
HHSN268201100005C / HL / NHLBI NIH HHS / United States