Literature DB >> 27557667

Estimation of the risk of a qualitative phenotype: dependence on population risk.

Naoyuki Kamatani1, Shigeo Kamitsuji1, Yasuaki Akazawa2, Takashi Kido3, Masanori Akita4.   

Abstract

Individual disease risk estimated based on the data from single or multiple genetic loci is generally calculated using the genotypes of a subject, frequencies of alleles of interest, odds ratios and the average population risk. However, it is often difficult to estimate accurately the average population risk, and therefore it is often expressed as an interval. To better estimate the risk of a subject with given genotypes, we built R scripts using the R environment and constructed graphs to examine the change in the estimated risk as well as the relative risk according to the change of the average population risk. In most cases, the graph of the relative risk did not cross the line of y=1, thereby indicating that the order of the relative risk for given genotypes and the population average risk does not change when the average risk increases or decreases. In rare cases, however, the graph of the relative risk crossed the line of y=1, thereby indicating that the order of the relative risk for given genotypes and the population average risk does change owing to the change in the population risk. We propose that the relative risk should be estimated for not only the point average population risk but also for an interval of the average population risk. Moreover, when the graph crosses the line of y=1 within the interval, this information should be reported to the consumer.

Mesh:

Year:  2016        PMID: 27557667     DOI: 10.1038/jhg.2016.106

Source DB:  PubMed          Journal:  J Hum Genet        ISSN: 1434-5161            Impact factor:   3.172


  8 in total

1.  Concordance study of 3 direct-to-consumer genetic-testing services.

Authors:  Kenta Imai; Larry J Kricka; Paolo Fortina
Journal:  Clin Chem       Date:  2010-12-15       Impact factor: 8.327

2.  Systematic evaluation of personal genome services for Japanese individuals.

Authors:  Takashi Kido; Minae Kawashima; Seiji Nishino; Melanie Swan; Naoyuki Kamatani; Atul J Butte
Journal:  J Hum Genet       Date:  2013-09-26       Impact factor: 3.172

3.  An agenda for personalized medicine.

Authors:  Pauline C Ng; Sarah S Murray; Samuel Levy; J Craig Venter
Journal:  Nature       Date:  2009-10-08       Impact factor: 49.962

4.  Evaluation of the discriminative accuracy of genomic profiling in the prediction of common complex diseases.

Authors:  Ramal Moonesinghe; Tiebin Liu; Muin J Khoury
Journal:  Eur J Hum Genet       Date:  2009-11-25       Impact factor: 4.246

5.  The genetic interpretation of area under the ROC curve in genomic profiling.

Authors:  Naomi R Wray; Jian Yang; Michael E Goddard; Peter M Visscher
Journal:  PLoS Genet       Date:  2010-02-26       Impact factor: 5.917

6.  Construction of a prediction model for type 2 diabetes mellitus in the Japanese population based on 11 genes with strong evidence of the association.

Authors:  Kazuaki Miyake; Woosung Yang; Kazuo Hara; Kazuki Yasuda; Yukio Horikawa; Haruhiko Osawa; Hiroto Furuta; Maggie C Y Ng; Yushi Hirota; Hiroyuki Mori; Keisuke Ido; Kazuya Yamagata; Yoshinori Hinokio; Yoshitomo Oka; Naoko Iwasaki; Yasuhiko Iwamoto; Yuichiro Yamada; Yutaka Seino; Hiroshi Maegawa; Atsunori Kashiwagi; He-Yao Wang; Toshihito Tanahashi; Naoto Nakamura; Jun Takeda; Eiichi Maeda; Ken Yamamoto; Katsushi Tokunaga; Ronald C W Ma; Wing-Yee So; Juliana C N Chan; Naoyuki Kamatani; Hideichi Makino; Kishio Nanjo; Takashi Kadowaki; Masato Kasuga
Journal:  J Hum Genet       Date:  2009-02-27       Impact factor: 3.172

Review 7.  The past, present, and future of direct-to-consumer genetic tests.

Authors:  Agnar Helgason; Kári Stefánsson
Journal:  Dialogues Clin Neurosci       Date:  2010       Impact factor: 5.986

8.  Variations in predicted risks in personal genome testing for common complex diseases.

Authors:  Rachel R J Kalf; Raluca Mihaescu; Suman Kundu; Peter de Knijff; Robert C Green; A Cecile J W Janssens
Journal:  Genet Med       Date:  2013-06-27       Impact factor: 8.822

  8 in total

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