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Quantifying and modeling birth order effects in autism.

Turner T, Pihur V, Chakravarti A - PLoS ONE (2011)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.

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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Related in: MedlinePlus

Logistic models for disease risk as a function of parental age; β is the rate of increase of logistic risk and a is the age in years at which risk is 50%.
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pone-0026418-g001: Logistic models for disease risk as a function of parental age; β is the rate of increase of logistic risk and a is the age in years at which risk is 50%.

Mentions: We considered four sets of parameters to model three classes of risk, namely, where disease risk was (a) independent of age, (b) linearly increasing with age (we tested only an increasing risk since the decreasing model is statistically equivalent), (c) logistic increase in risk with lower and higher rates of increase at later ages. These risk profiles are shown in Figure 1 with b assumed to be 0.1 for all cases. Empirically observed risk rates for Down Syndrome that shows a maternal-age effect are also plotted in open circles providing a reasonable benchmark for the four models. To generate family structures, we again used a Poisson distribution for sibship sizes with mean λ = 2.39. Parameter selection for the four models was motivated by presenting reasonable yet somewhat different risk patterns. In the no-birth effect model both β and a are set to 0; for the linear model, β = 0.05 and a = 30, and for the two logistic curves, a = 45 is selected as the age at which the risk reaches half of its maximum b at two different rates (β = 0.2 and β = 0.5). The model with β = 0.5 and a = 45 is clearly the one closest to the observed risk pattern in Down Syndrome and is of primary interest as a potential ‘model’ for the autism age-dependent risk profile.


Quantifying and modeling birth order effects in autism.

Turner T, Pihur V, Chakravarti A - PLoS ONE (2011)

Logistic models for disease risk as a function of parental age; β is the rate of increase of logistic risk and a is the age in years at which risk is 50%.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3198479&req=5

pone-0026418-g001: Logistic models for disease risk as a function of parental age; β is the rate of increase of logistic risk and a is the age in years at which risk is 50%.
Mentions: We considered four sets of parameters to model three classes of risk, namely, where disease risk was (a) independent of age, (b) linearly increasing with age (we tested only an increasing risk since the decreasing model is statistically equivalent), (c) logistic increase in risk with lower and higher rates of increase at later ages. These risk profiles are shown in Figure 1 with b assumed to be 0.1 for all cases. Empirically observed risk rates for Down Syndrome that shows a maternal-age effect are also plotted in open circles providing a reasonable benchmark for the four models. To generate family structures, we again used a Poisson distribution for sibship sizes with mean λ = 2.39. Parameter selection for the four models was motivated by presenting reasonable yet somewhat different risk patterns. In the no-birth effect model both β and a are set to 0; for the linear model, β = 0.05 and a = 30, and for the two logistic curves, a = 45 is selected as the age at which the risk reaches half of its maximum b at two different rates (β = 0.2 and β = 0.5). The model with β = 0.5 and a = 45 is clearly the one closest to the observed risk pattern in Down Syndrome and is of primary interest as a potential ‘model’ for the autism age-dependent risk profile.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.

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.

Show MeSH
Related in: MedlinePlus