|
Academic/Biomedical Research
News & Jobs
|
|
|
|
|
|
|
|
|
|
|
|
|
Free Newsletters
Archive
My Subscriptions

News by Subject
News by Disease
News by Date
PLoS
Search News
Post Your News
JoVE

Job Seeker Login
Most Recent Jobs
Search Jobs
Post Resume
Career Fairs
Career Resources
For Employers

Regional News
US & Canada
Biotech Bay
Biotech Beach
Genetown
Pharm Country
BioCapital
BioMidwest
Bio NC
BioForest
Southern Pharm
BioCanada East
US Device
Europe
Asia


Company Profiles

Research Store

Research Events
Post an Event

Real Estate
Business Opportunities
|
|
|
|
|
PLoS By Category | Recent
PLoS Articles
|
|
Mathematics - Neurological Disorders - Pediatrics and Child Health - Public Health and Epidemiology
|
Quantifying and Modeling Birth Order Effects in Autism
Published:
Wednesday, October 19, 2011
Author:
Tychele Turner et al.
by Tychele Turner, Vasyl Pihur, Aravinda Chakravarti
Autism is a complex genetic disorder with multiple etiologies whose molecular genetic basis is not fully understood. Although a number of rare mutations and dosage abnormalities are specific to autism, these explain no more than 10% of all cases. The high heritability of autism and low recurrence risk suggests multifactorial inheritance from numerous loci but other factors also intervene to modulate risk. In this study, we examine the effect of birth rank on disease risk which is not expected for purely hereditary genetic models.
We analyzed the data from three publicly available autism family collections in the USA for potential birth order effects and studied the statistical properties of three tests to show that adequate power to detect these effects exist. We detect statistically significant, yet varying, patterns of birth order effects across these collections. In multiplex families, we identify V-shaped effects where middle births are at high risk; in simplex families, we demonstrate linear effects where risk increases with each additional birth. Moreover, the birth order effect is gender-dependent in the simplex collection. It is currently unknown whether these patterns arise from ascertainment biases or biological factors. Nevertheless, further investigation of parental age-dependent risks yields patterns similar to those observed and could potentially explain part of the increased risk. A search for genes considering these patterns is likely to increase statistical power and uncover novel molecular etiologies.
More...
|
|
|
 |
 |
|
|
|
|
|
|
|
|