Search results for "TWIN DATA"
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A Critical Review of Statistical Methods for Twin Studies Relating Exposure to Early Life Health Conditions
2021
International audience; When investigating disease etiology, twin data provide a unique opportunity to control for confounding and disentangling the role of the human genome and exposome. However, using appropriate statistical methods is fundamental for exploiting such potential. We aimed to critically review the statistical approaches used in twin studies relating exposure to early life health conditions. We searched PubMed, Scopus, Web of Science, and Embase (2011–2021). We identified 32 studies and nine classes of methods. Five were conditional approaches (within-pair analyses): additive-common-erratic (ACE) models (11 studies), generalized linear mixed models (GLMMs, five studies), gene…
DSM-IV Combined Type ADHD Shows Familial Association With Sibling Trait Scores
2008
Contains fulltext : 69060.pdf (Publisher’s version ) (Closed access) Attention deficit hyperactivity disorder (ADHD) is a discrete clinical syndrome characterized by the triad of inattention, hyperactivity, and impulsivity in the context of marked impairments. Molecular genetic studies have been successful in identifying genetic variants associated with ADHD, particularly with DSM-IV inattentive and combined subtypes. Quantitative trait locus (QTL) approaches to linkage and association mapping have yet to be widely used in ADHD research, although twin studies investigating individual differences suggest that genetic liability for ADHD is continuously distributed throughout the population, u…
The Return-to-Entrepreneurship Puzzle
2013
The returns to entrepreneurship are monetary and non-monetary. We offer new evidence on these returns using a large sample of genetically identical male twins. Our within-twin analysis suggests that OLS estimates are downwards, and traditional first-differenced panel data estimates upwards biased. We find no differences in the earnings of men with either low or high education. Our within-twin analysis of non-monetary returns shows that entrepreneurs with low education work longer hours and have greater responsibilities, but also face a reduced risk of divorce and less monotonous work tasks. The same does not apply to highly educated entrepreneurs.