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Quantifying and Modeling Birth Order Effects in Autism

Overview of attention for article published in PLOS ONE, October 2011
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Title
Quantifying and Modeling Birth Order Effects in Autism
Published in
PLOS ONE, October 2011
DOI 10.1371/journal.pone.0026418
Pubmed ID
Authors

Tychele Turner, Vasyl Pihur, Aravinda Chakravarti

Abstract

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.

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Geographical breakdown

Country Count As %
India 1 2%
United States 1 2%
France 1 2%
Unknown 52 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 15%
Researcher 6 11%
Student > Bachelor 5 9%
Other 5 9%
Student > Master 4 7%
Other 10 18%
Unknown 17 31%
Readers by discipline Count As %
Medicine and Dentistry 10 18%
Psychology 8 15%
Agricultural and Biological Sciences 5 9%
Biochemistry, Genetics and Molecular Biology 3 5%
Social Sciences 3 5%
Other 7 13%
Unknown 19 35%