| 11. |
|
|
| 12. |
|
|
| 13. |
- Smoller, JW, et al.
(author)
-
Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis.
- 2013
-
In: Lancet. - 1474-547X. ; 381:9875, s. 1371-9
-
Journal article (peer-reviewed)abstract
- Findings from family and twin studies suggest that genetic contributions to psychiatric disorders do not in all cases map to present diagnostic categories. We aimed to identify specific variants underlying genetic effects shared between the five disorders in the Psychiatric Genomics Consortium: autism spectrum disorder, attention deficit-hyperactivity disorder, bipolar disorder, major depressive disorder, and schizophrenia.
|
|
| 14. |
|
|
| 15. |
|
|
| 16. |
|
|
| 17. |
- Lucas, Gavin, et al.
(author)
-
Hypothesis-Based Analysis of Gene-Gene Interactions and Risk of Myocardial Infarction
- 2012
-
In: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 7:8
-
Journal article (peer-reviewed)abstract
- The genetic loci that have been found by genome-wide association studies to modulate risk of coronary heart disease explain only a fraction of its total variance, and gene-gene interactions have been proposed as a potential source of the remaining heritability. Given the potentially large testing burden, we sought to enrich our search space with real interactions by analyzing variants that may be more likely to interact on the basis of two distinct hypotheses: a biological hypothesis, under which MI risk is modulated by interactions between variants that are known to be relevant for its risk factors; and a statistical hypothesis, under which interacting variants individually show weak marginal association with MI. In a discovery sample of 2,967 cases of early-onset myocardial infarction (MI) and 3,075 controls from the MIGen study, we performed pair-wise SNP interaction testing using a logistic regression framework. Despite having reasonable power to detect interaction effects of plausible magnitudes, we observed no statistically significant evidence of interaction under these hypotheses, and no clear consistency between the top results in our discovery sample and those in a large validation sample of 1,766 cases of coronary heart disease and 2,938 controls from the Wellcome Trust Case-Control Consortium. Our results do not support the existence of strong interaction effects as a common risk factor for MI. Within the scope of the hypotheses we have explored, this study places a modest upper limit on the magnitude that epistatic risk effects are likely to have at the population level (odds ratio for MI risk 1.3-2.0, depending on allele frequency and interaction model).
|
|
| 18. |
|
|
| 19. |
|
|
| 20. |
- Benjamin, DJ, et al.
(author)
-
The Promises and Pitfalls of Genoeconomics*
- 2012
-
In: Annual review of economics. - : Annual Reviews. - 1941-1383 .- 1941-1391. ; 4, s. 627-
-
Journal article (peer-reviewed)abstract
- This article reviews existing research at the intersection of genetics and economics, presents some new findings that illustrate the state of genoeconomics research, and surveys the prospects of this emerging field. Twin studies suggest that economic outcomes and preferences, once corrected for measurement error, appear to be about as heritable as many medical conditions and personality traits. Consistent with this pattern, we present new evidence on the heritability of permanent income and wealth. Turning to genetic association studies, we survey the main ways that the direct measurement of genetic variation across individuals is likely to contribute to economics, and we outline the challenges that have slowed progress in making these contributions. The most urgent problem facing researchers in this field is that most existing efforts to find associations between genetic variation and economic behavior are based on samples that are too small to ensure adequate statistical power. This has led to many false positives in the literature. We suggest a number of possible strategies to improve and remedy this problem: (a) pooling data sets, (b) using statistical techniques that exploit the greater information content of many genes jointly, and (c) focusing on economically relevant traits that are most proximate to known biological mechanisms.
|
|
