Limits...
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

Statistical distribution of birth order effect of the data in Figure 2.Proportion of observed and expected numbers of affected offspring at each birth rank and the numbers of affected offspring for each sibship size and birth order is shown.
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pone-0026418-g003: Statistical distribution of birth order effect of the data in Figure 2.Proportion of observed and expected numbers of affected offspring at each birth rank and the numbers of affected offspring for each sibship size and birth order is shown.

Mentions: To examine the nature of birth rank effects recovered we simulated 10,000 families with logistic risks and the parameters β = 0.5 and a = 45. The proportion of affected offspring by birth rank and the distributions of parental age for both affected and unaffected offspring are shown in Figure 2. The shift to the right in the two age distributions is very apparent and is also reflected in the birth ranks, especially in the deficiency of the first-born affected children. Figure 3 demonstrates the exponential increase in risk for all sibship sizes in birth rank, measured as proportion of affected at each rank, which directly corresponds to the underlying increase in risk by parental age. The expected and observed proportions of affected offsprings by birth rank differ substantially (top left) and the χ2-type test is highly significant (p<0.001). In Figure 4, we examine a linear risk model as a function of parent's age that translates approximately into a linear risk in birth ranks. This model corresponds to β = 0.05 and a = 30 and is also plotted in red in Figure 1. The χ2-type test for the birth order effect in these 10,000 families is also highly significant (p<0.001) although the departure of observed to expected proportions is not as substantial as in the previous logistic example. Nevertheless, we still observe an increase in risk at each sibship size and the overall test confirms this effect. To estimate the statistical power for these logistic models we again resort to Monte-Carlo simulations. The estimated power for sample sizes 150, 500 and 1,000 families based on 100 simulations is shown in Table 3. Clearly, the rank-sum test has the largest power in all scenarios, followed by the χ2-type test. As expected, the inverse rank-sum test has the least power for monotonic patterns of risk increase and should not be used in such cases.


Quantifying and modeling birth order effects in autism.

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

Statistical distribution of birth order effect of the data in Figure 2.Proportion of observed and expected numbers of affected offspring at each birth rank and the numbers of affected offspring for each sibship size and birth order is shown.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0026418-g003: Statistical distribution of birth order effect of the data in Figure 2.Proportion of observed and expected numbers of affected offspring at each birth rank and the numbers of affected offspring for each sibship size and birth order is shown.
Mentions: To examine the nature of birth rank effects recovered we simulated 10,000 families with logistic risks and the parameters β = 0.5 and a = 45. The proportion of affected offspring by birth rank and the distributions of parental age for both affected and unaffected offspring are shown in Figure 2. The shift to the right in the two age distributions is very apparent and is also reflected in the birth ranks, especially in the deficiency of the first-born affected children. Figure 3 demonstrates the exponential increase in risk for all sibship sizes in birth rank, measured as proportion of affected at each rank, which directly corresponds to the underlying increase in risk by parental age. The expected and observed proportions of affected offsprings by birth rank differ substantially (top left) and the χ2-type test is highly significant (p<0.001). In Figure 4, we examine a linear risk model as a function of parent's age that translates approximately into a linear risk in birth ranks. This model corresponds to β = 0.05 and a = 30 and is also plotted in red in Figure 1. The χ2-type test for the birth order effect in these 10,000 families is also highly significant (p<0.001) although the departure of observed to expected proportions is not as substantial as in the previous logistic example. Nevertheless, we still observe an increase in risk at each sibship size and the overall test confirms this effect. To estimate the statistical power for these logistic models we again resort to Monte-Carlo simulations. The estimated power for sample sizes 150, 500 and 1,000 families based on 100 simulations is shown in Table 3. Clearly, the rank-sum test has the largest power in all scenarios, followed by the χ2-type test. As expected, the inverse rank-sum test has the least power for monotonic patterns of risk increase and should not be used in such cases.

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