Analysis of Twin Data Using SAS

SUMMARY Twin studies are essential for assessing disease inheritance. Data generated from twin studies are traditionally analyzed using specialized computational programs. For many researchers, especially those who are new to twin studies, understanding and using those specialized computational programs can be a daunting task. Given that SAS (Statistical Analysis Software) is the most popular software for statistical analysis, we suggest that the use of SAS procedures for twin data may be a helpful alternative and demonstrate that we can obtain similar results from SAS to those produced by specialized computational programs. This numerical validation is practically useful, because a natural concern with general statistical software is whether it can deal with data that are generated from special study designs such as twin studies and if it can test a particular hypothesis. We concluded through our extensive simulation that SAS procedures can be used easily as a very convenient alternative to specialized programs for twin data analysis.

[1]  R. Elston,et al.  The estimation of genetic variance from twin data , 1970, Behavior genetics.

[2]  L. Ment,et al.  Genetic Susceptibility to Retinopathy of Prematurity , 2006, Pediatrics.

[3]  N. Martin,et al.  A twin-pronged attack on complex traits , 1997, Nature Genetics.

[4]  L. Kovacs,et al.  Dysplasia: A review , 2007, Pediatric pulmonology.

[5]  Nicholas G Martin,et al.  The Genetics of Coronary Heart Disease: The Contribution of Twin Studies , 2003, Twin Research.

[6]  L. Ment,et al.  Familial and Genetic Susceptibility to Major Neonatal Morbidities in Preterm Twins , 2006, Pediatrics.

[7]  J K Hewitt,et al.  Fitting genetic models with LISREL: Hypothesis testing , 1989, Behavior genetics.

[8]  Emilio Ferrer,et al.  Modeling Latent Growth Curves With Incomplete Data Using Different Types of Structural Equation Modeling and Multilevel Software , 2004 .

[9]  Leena Peltonen,et al.  GenomEUtwin: A Strategy to Identify Genetic Influences on Health and Disease , 2003, Twin Research.

[10]  J. Mcardle.  Latent curve analyses of longitudinal twin data using a mixed-effects biometric approach. , 2006, Twin research and human genetics : the official journal of the International Society for Twin Studies.

[11]  A Skrondal,et al.  Biometrical Modeling of Twin and Family Data Using Standard Mixed Model Software , 2008, Biometrics.

[12]  D. Allison,et al.  Habitual physical activity in children: the role of genes and the environment. , 2005, The American journal of clinical nutrition.

[13]  N. Martin,et al.  Genetic and environmental influences on migraine: a twin study across six countries. , 2003, Twin research : the official journal of the International Society for Twin Studies.

[14]  D. Falconer,et al.  Introduction to Quantitative Genetics. , 1962 .

[15]  C. Prescott.  Using the Mplus Computer Program to Estimate Models for Continuous and Categorical Data from Twins , 2004, Behavior genetics.

[16]  O. Kempthorne,et al.  The interpretation of twin data. , 1961, American journal of human genetics.

[17]  Michael C. Neale,et al.  Methodology for Genetic Studies of Twins and Families , 1992 .

[18]  J C Christian,et al.  TWINAN90: a FORTRAN program for conducting ANOVA-based and likelihood-based analyses of twin data. , 1992, Computer methods and programs in biomedicine.

[19]  D. Gianola,et al.  Sire evaluation for ordered categorical data with a threshold model , 1983, Génétique, sélection, évolution.

[20]  S. Heymsfield,et al.  Genetic, environmental, and phenotypic links between body mass index and blood pressure among women. , 1995, American journal of medical genetics.