Literature DB >> 19223933

Predicting human height by Victorian and genomic methods.

Yurii S Aulchenko1, Maksim V Struchalin, Nadezhda M Belonogova, Tatiana I Axenovich, Michael N Weedon, Albert Hofman, Andre G Uitterlinden, Manfred Kayser, Ben A Oostra, Cornelia M van Duijn, A Cecile J W Janssens, Pavel M Borodin.   

Abstract

In the Victorian era, Sir Francis Galton showed that 'when dealing with the transmission of stature from parents to children, the average height of the two parents, ... is all we need care to know about them' (1886). One hundred and twenty-two years after Galton's work was published, 54 loci showing strong statistical evidence for association to human height were described, providing us with potential genomic means of human height prediction. In a population-based study of 5748 people, we find that a 54-loci genomic profile explained 4-6% of the sex- and age-adjusted height variance, and had limited ability to discriminate tall/short people, as characterized by the area under the receiver-operating characteristic curve (AUC). In a family-based study of 550 people, with both parents having height measurements, we find that the Galtonian mid-parental prediction method explained 40% of the sex- and age-adjusted height variance, and showed high discriminative accuracy. We have also explored how much variance a genomic profile should explain to reach certain AUC values. For highly heritable traits such as height, we conclude that in applications in which parental phenotypic information is available (eg, medicine), the Victorian Galton's method will long stay unsurpassed, in terms of both discriminative accuracy and costs. For less heritable traits, and in situations in which parental information is not available (eg, forensics), genomic methods may provide an alternative, given that the variants determining an essential proportion of the trait's variation can be identified.

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Year:  2009        PMID: 19223933      PMCID: PMC2986552          DOI: 10.1038/ejhg.2009.5

Source DB:  PubMed          Journal:  Eur J Hum Genet        ISSN: 1018-4813            Impact factor:   4.246


  15 in total

1.  Initial sequencing and analysis of the human genome.

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Journal:  Nature       Date:  2001-02-15       Impact factor: 49.962

2.  R2: a useful measure of model performance when predicting a dichotomous outcome.

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Authors:  Yurii S Aulchenko; Peter Heutink; Ian Mackay; Aida M Bertoli-Avella; Jan Pullen; Norbert Vaessen; Tessa A M Rademaker; Lodewijk A Sandkuijl; Lon Cardon; Ben Oostra; Cornelia M van Duijn
Journal:  Eur J Hum Genet       Date:  2004-07       Impact factor: 4.246

4.  The effect of genetic drift in a young genetically isolated population.

Authors:  L M Pardo; Ian MacKay; Ben Oostra; Cornelia M van Duijn; Yurii S Aulchenko
Journal:  Ann Hum Genet       Date:  2005-05       Impact factor: 1.670

5.  Prediction of coronary heart disease using risk factor categories.

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Journal:  Circulation       Date:  1998-05-12       Impact factor: 29.690

6.  The meaning and use of the area under a receiver operating characteristic (ROC) curve.

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Journal:  Radiology       Date:  1982-04       Impact factor: 11.105

7.  Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings.

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8.  The Rotterdam Study: objectives and design update.

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Journal:  Eur J Epidemiol       Date:  2007-10-23       Impact factor: 8.082

9.  Predictive testing for complex diseases using multiple genes: fact or fiction?

Authors:  A Cecile J W Janssens; Yurii S Aulchenko; Stefano Elefante; Gerard J J M Borsboom; Ewout W Steyerberg; Cornelia M van Duijn
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10.  Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts.

Authors:  Yurii S Aulchenko; Samuli Ripatti; Ida Lindqvist; Dorret Boomsma; Iris M Heid; Peter P Pramstaller; Brenda W J H Penninx; A Cecile J W Janssens; James F Wilson; Tim Spector; Nicholas G Martin; Nancy L Pedersen; Kirsten Ohm Kyvik; Jaakko Kaprio; Albert Hofman; Nelson B Freimer; Marjo-Riitta Jarvelin; Ulf Gyllensten; Harry Campbell; Igor Rudan; Asa Johansson; Fabio Marroni; Caroline Hayward; Veronique Vitart; Inger Jonasson; Cristian Pattaro; Alan Wright; Nick Hastie; Irene Pichler; Andrew A Hicks; Mario Falchi; Gonneke Willemsen; Jouke-Jan Hottenga; Eco J C de Geus; Grant W Montgomery; John Whitfield; Patrik Magnusson; Juha Saharinen; Markus Perola; Kaisa Silander; Aaron Isaacs; Eric J G Sijbrands; Andre G Uitterlinden; Jacqueline C M Witteman; Ben A Oostra; Paul Elliott; Aimo Ruokonen; Chiara Sabatti; Christian Gieger; Thomas Meitinger; Florian Kronenberg; Angela Döring; H-Erich Wichmann; Johannes H Smit; Mark I McCarthy; Cornelia M van Duijn; Leena Peltonen
Journal:  Nat Genet       Date:  2008-12-07       Impact factor: 38.330

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2.  Common DNA variants predict tall stature in Europeans.

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4.  Gene × environment interaction by a longitudinal epigenome-wide association study (LEWAS) overcomes limitations of genome-wide association study (GWAS).

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Journal:  Epigenomics       Date:  2012-12       Impact factor: 4.778

5.  The insulin-like growth factor 1 receptor (IGF1R) contributes to reduced size in dogs.

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6.  Family-based genetic risk prediction of multifactorial disease.

Authors:  Douglas M Ruderfer; Joshua Korn; Shaun M Purcell
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Review 7.  Leg length, body proportion, and health: a review with a note on beauty.

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Journal:  Int J Environ Res Public Health       Date:  2010-03-11       Impact factor: 3.390

8.  Variance heterogeneity analysis for detection of potentially interacting genetic loci: method and its limitations.

Authors:  Maksim V Struchalin; Abbas Dehghan; Jacqueline Cm Witteman; Cornelia van Duijn; Yurii S Aulchenko
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9.  From disease association to risk assessment: an optimistic view from genome-wide association studies on type 1 diabetes.

Authors:  Zhi Wei; Kai Wang; Hui-Qi Qu; Haitao Zhang; Jonathan Bradfield; Cecilia Kim; Edward Frackleton; Cuiping Hou; Joseph T Glessner; Rosetta Chiavacci; Charles Stanley; Dimitri Monos; Struan F A Grant; Constantin Polychronakos; Hakon Hakonarson
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10.  Genetic scoring analysis: a way forward in genome wide association studies?

Authors:  Najaf Amin; Cornelia M van Duijn; A Cecile J W Janssens
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