SHORT REPORT Consanguinity in India and Its Association With Autism Spectrum Disorder Madhu P. Mamidala, Mahesh K. Kalikiri, Praveen Kumar P. T. V., N. Rajesh, OmSai R. Vallamkonda, and Vidya Rajesh Autism Spectrum Disorder (ASD) has both genetic and environmental factors in its etiology. The risk for many disorders is increased by consanguinity, but it is not known whether it increases the risk for ASD. Our study from large population in India concludes that consanguinity increases the risk for ASD with an odds ratio of 3.22. Autism Res 2014, ••: ••–••. © 2014 International Society for Autism Research, Wiley Periodicals, Inc. Keywords: Autism Spectrum Disorder; consanguinity; risk factors; genetic association; India
Introduction Consanguineous marriages, as defined by Morgan [1870] (marriage between individuals with a common ancestor), is still common in India, as it is in some parts of Africa, the Middle East, and Asia [Bittles, Mason, Greene, & Rao, 1991]. The global prevalence of consanguinity is 10.4% [Bittles & Black, 2010]. It is also reported that globally at least 20% of the human population lives in communities with a preference for consanguineous marriage, and at least 8.5% of children have consanguineous parents [Modell & Darr, 2002]. Due to changing mindsets, such marriages are stigmatized but are still practiced to foster stronger family connections and integrity of property [Rao, Asha, Sambamurthy, & Rao, 2009]. In India, consanguinity is more commonly practiced by Arab Muslims and Dravidian Hindus [Rao et al., 2009] (Fig. 1). Consanguinity is often associated with low socioeconomic status, illiteracy, and rural residence [Bittles et al., 1991]. Medical complications are well documented in consanguineous marriages; these include both rare recessive genetic disorders [Bittles, 2008] and malformations [Jaber et al., 2005], as well as disorders of complex inheritance, such as psychiatric disorders [Mansour et al., 2010; Musante & Ropers, 2014; Sharkia, Azem, &
Kaiyal, 2010]. However, the relationship of consanguinity to autism spectrum disorder (ASD) risk has not been explored. We undertook this study to test the hypothesis that consanguinity increases the risk of ASD. Consanguinity is most commonly associated with rare recessive conditions, and some of the ASD genes are likely to be of this type. However, disorders of more complex inheritance could be more common in the offspring of consanguineous marriages. Thus, it is important to study the association of consanguinity with ASD. To evaluate our hypothesis, a questionnaire-based psychometric analysis was planned. This report from a country like India where inbreeding is practiced even today would provide us a greater insight into the risk associations of ASD.
Methods Sampling Frame Both cases and controls were recruited between September 2010 and December 2012. A simple random sampling procedure was followed for data recruitment. Centers or schools were selected based on their size and the probability of finding children from various socioeconomic backgrounds.
From the Department of Biological Sciences, Birla Institute of Technology and Science, Hyderabad, India (M.P.M., M.K.K., V.R.); Department of Mathematics, Birla Institute of Technology and Science, Hyderabad, India (P.K.P.T.V); Department of Chemistry, Birla Institute of Technology and Science, Hyderabad, India (N.R.); Department of Medical Sciences, National Institute for the Mentally Handicapped (NIMH), Secunderabad, India (O.R.V.) Received October 17, 2013; accepted for publication September 29, 2014 Address for correspondence and reprints: Vidya Rajesh, Department of Biological Sciences, Birla Institute of Technology and Science, Pilani— Hyderabad Campus, Jawaharnagar, Shamirpet (M), Hyderabad 500078, Andhra Pradesh, India. E-mail: [email protected]; [email protected] Conflict of interest declaration: Madhu P. Mamidala: has no conflict of interest relevant to this article to disclose; Mahesh K. Kalikiri: has no conflict of interest relevant to this article to disclose; Praveen Kumar P.T.V.: has no conflict of interest relevant to this article to disclose; N. Rajesh: has no conflict of interest relevant to this article to disclose; OmSai R. Vallamkonda: has no conflict of interest relevant to this article to disclose; Vidya Rajesh: conflict of interest; research funded by UGC—F.37–118/2009(SR). Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/aur.1431 © 2014 International Society for Autism Research, Wiley Periodicals, Inc.
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Autism Research ••: ••–••, 2014
1
Figure 1. Types of consanguinity in Indian Population. (A,B) parallel first cousins; (C) cross first cousins; (D) uncle-niece; (E) double first cousins; (F) second cousins. Data Sources and Collection Our study, approved by the Institutional Human Ethics Committee, included children between 2 and 10 years of age across India. For recruitment of cases, collaborations were established with 70 centers covering nine cities. The presence of ASD was the sole criteria for the recruitment of cases. Children under a suspicion for ASD but without a formal diagnosis and children with cerebral palsy and Down syndrome were excluded from the study. Case ascertainment was done using confirmed report for diagnosis of ASD available with parent and the center. The diagnosis of ASD was done in a standard way based on internationally accepted standards, like the Diagnostic and Statistical Manual of Mental Disorders IV or the International Classification of Diseases 10 or the Indian Scale for Assessment of ASD, by a trained physician at the child psychiatry department of hospitals. Additionally, the Childhood Autism Rating Scale was used commonly to classify case status. For the control population (age- and gender-matched), collaborations were established with schools (regular, government-run, and private), and data was collected by visiting the houses randomly across the cities, and this ensured good representation of all sections of children belonging to diverse socioeconomic background. The detailed methodology for data collection is available in our earlier paper [Mamidala et al., 2013] and the same procedure was followed. A questionnaire was designed based on the probable risk factors of ASD from existing literature and those specific to Indian environment. Validity of the questionnaire was tested with a small convenient sample, followed by construct and reli-
2
ability testing. The data obtained concluded that the questionnaire was a good instrument for psychometric research and is communicated for publication elsewhere. An informed consent was taken from parents of the participating children (both cases and controls) prior to the study. Majority of the data was collected through direct interaction with the parents. In places where it was not feasible, teachers of those organizations were trained with respect to the content of the questionnaire, and they helped parents to complete the questionnaire by translating the content into their respective dialect, as well as clarifying any doubts. Analysis A total of 500 ASD children and an equal number of controls were enrolled in the study. The types of consanguinity were divided into four profiles, namely first cousins, second cousins, uncle-niece, and double first cousin [Bener, Hussain, & Teebi, 2007] (Fig. 1), and their inbreeding coefficient (F) was evaluated. Statistical analysis was done using SAS 9.1.3 (SAS Institute, Inc, Cary, NC, USA) version. We performed a univariable logistic regression analysis of all variables, and the data are available in our earlier paper [Mamidala et al., 2013]. In the present study, we evaluated the independent association of consanguinity by univariate analysis for the risk of ASD. Subsequently, a multivariable logistic regression model was performed to calculate odds ratio, 95% confidence intervals (CIs), and 5% α. The parental characteristics of consanguinity along with the reported prenatal, perinatal, and neonatal factors [Mamidala et al., 2013] were our explanatory variables, while ASD was our main
Mamidala et al./Consanguinity and autism spectrum disorder
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Table 1.
Analysis of Different Types of Consanguinity and Inbreeding Coefficients Number
Types of consanguinity Total First cousins Parallel cousins Cross cousins Uncle-niece Double first cousins Second cousins
Cases
%
Control
%
100 68 13 55 15 10 7
20 13.6 2.6 11 3 2 1.4
36 23 9 14 7 2 4
7.2 4.6 1.8 2.8 1.4 0.4 0.8
dependent variable. We also evaluated the risk contributing factors by conditioning consanguinity. We plotted the estimated log odds for each variable from the logistic regression model to probabilities. Reference standards considered for ordinal variables were 30 years and above for parental age [Grether, Anderson, Croen, Smith, & Windham, 2009], less than 37 weeks for abnormal gestational term, and less than 2500 g for low birth weight (LBW) category, respectively [Muthayya, 2009; WHO, 2004].
Inbreeding coefficient (F)
0.0625
0.125 0.125 0.0156
CI – 1.78, 5.44, P < 0.0001)], labor complications [odds ratio of 5.13 (95% CI – 2.98, 8.84, P < 0.0001)], and preterm birth [odds ratio of 1.83 (95% CI – 1.16, 2.88, P < 0.008)] remained significant in our study population. Also, LBW [odds ratio of 2.02 (95% CI – 1.39, 2.93, P < 0.01)], which was insignificant in our earlier study, became significant in this study. But when we conditioned consanguinity in multivariate analysis, all factors remained significant with decreased odds ratio (Table 2), and LBW again became insignificant (Table 2).
Discussion Results The recruitment was gender- and age-matched, and there were no significant differences in age or gender between the 500 cases and 500 controls. The male to female ratio in our population was calculated to be 4:1 (395 boys and 105 girls in cases, and 397 boys and 103 girls in controls). Characteristics of Consanguinity We evaluated the nature of consanguinity in our study population, the results of which are shown in Table 1 and Figure 1.The ASD cases had significant level of consanguinity when compared with controls, and statistical evaluation using univariate analysis substantiated that there is a risk factor of 3.22 (95% CI—2.07, 4.62, P < 0.0001) for ASD when parents are consanguineous. Risk Contribution of Consanguinity With Reported Risk Factors for ASD We also have analyzed the regression values of consanguinity along with reported prenatal, perinatal, and neonatal risk factors of ASD [Mamidala et al., 2013] using a multivariate approach. Among the factors analyzed (Table 2), advanced maternal age [odds ratio of 1.58 (95% CI – 1.15, 2.15, P – 0.003)], consanguinity [odds ratio of 3.20 (95% CI – 2.15, 4.82, P < 0.0001)], gestational respiratory tract infections [odds ratio of 4.77 (95% CI – 1.80, 12.65, P < 0.001)], fetal distress [odds ratio of 3.11 (95%
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This paper evaluates the effect of parental consanguinity on the risk for ASD using a case control design from an Indian population where consanguinity is relatively common. The risk of consanguinity for the ASD etiology by a factor of 3.2 found in our study population has several important implications. It raises the possibility of recessively inherited genetic risk factors for the etiology of ASD. Consanguinity is reported to have serious effects on fetal growth and development, and increases the risk of congenital malformations [Kulkarni & Kurian, 1990]. Moreover, it has been reported that children born to consanguineous parents had lower cognitive ability [Afzal, 1988] and social behavior [Afzal & Sinha, 1983], which are the major issues with ASD children. Hence, it is evident that consanguinity could contribute toward the risk for ASD. Consanguinity is also reported to increase the risk of adverse perinatal outcomes, including stillbirth [Hussain, Bittles, & Sullivan, 2001; Khoury & Massad, 2000; Stoltenberg, 1999], LBW [Benson, 2005; Hussain et al., 2001; Jordan, 2007; Khalid, Ghina, Fadi, & Fadi, 2006; Khoury & Massad, 2000; Mumtaz et al., 2007; Sezik, Ozkaya, Sezik, Yapar, & Kaya, 2006; Stoltenberg, 1999], preterm delivery [Al-Eissa & Ba’Aqeel, 1994; Mumtaz et al., 2010], apnea of prematurity [Tamim, Khogali, & Beydoun, 2003], infant and child mortality [Bittles & Black, 2010], and congenital birth defects and malformations [Jaber et al., 2005].
Mamidala et al./Consanguinity and autism spectrum disorder
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Table 2.
Logistic Regression of Risk Factors of Autism Spectrum Disorder Cases
Factors Advanced paternal agea Advanced maternal ageb Consanguinity Respiratory tract infections Fetal distress Labor complications Preterm birthc Low birth weightd
Controls
Multivariate analysis (conditioned consanguinity)
Multivariate analysis
Number
%
Number
%
Odds ratio
[95% confidence interval]
340 121 100 23 75 94 73 90
68 24.2 20 4.6 15 18.8 14.6 18
334 84 36 5 19 18 37 49
66.8 16.8 7.2 0.1 3.8 3.6 7.4 9.8
1.05 1.58 3.20 4.77 3.11 5.13 1.83 2.02
[0.81, 1.37] [1.15, 2.15] [2.15, 4.82] [1.80, 12.65] [1.78, 5.44] [2.98, 8.84] [1.16, 2.88] [1.39, 2.93]
P-value
Odds ratio
[95% confidence interval]
P-value
0.68 0.003
Introduction Consanguineous marriages, as defined by Morgan [1870] (marriage between individuals with a common ancestor), is still common in India, as it is in some parts of Africa, the Middle East, and Asia [Bittles, Mason, Greene, & Rao, 1991]. The global prevalence of consanguinity is 10.4% [Bittles & Black, 2010]. It is also reported that globally at least 20% of the human population lives in communities with a preference for consanguineous marriage, and at least 8.5% of children have consanguineous parents [Modell & Darr, 2002]. Due to changing mindsets, such marriages are stigmatized but are still practiced to foster stronger family connections and integrity of property [Rao, Asha, Sambamurthy, & Rao, 2009]. In India, consanguinity is more commonly practiced by Arab Muslims and Dravidian Hindus [Rao et al., 2009] (Fig. 1). Consanguinity is often associated with low socioeconomic status, illiteracy, and rural residence [Bittles et al., 1991]. Medical complications are well documented in consanguineous marriages; these include both rare recessive genetic disorders [Bittles, 2008] and malformations [Jaber et al., 2005], as well as disorders of complex inheritance, such as psychiatric disorders [Mansour et al., 2010; Musante & Ropers, 2014; Sharkia, Azem, &
Kaiyal, 2010]. However, the relationship of consanguinity to autism spectrum disorder (ASD) risk has not been explored. We undertook this study to test the hypothesis that consanguinity increases the risk of ASD. Consanguinity is most commonly associated with rare recessive conditions, and some of the ASD genes are likely to be of this type. However, disorders of more complex inheritance could be more common in the offspring of consanguineous marriages. Thus, it is important to study the association of consanguinity with ASD. To evaluate our hypothesis, a questionnaire-based psychometric analysis was planned. This report from a country like India where inbreeding is practiced even today would provide us a greater insight into the risk associations of ASD.
Methods Sampling Frame Both cases and controls were recruited between September 2010 and December 2012. A simple random sampling procedure was followed for data recruitment. Centers or schools were selected based on their size and the probability of finding children from various socioeconomic backgrounds.
From the Department of Biological Sciences, Birla Institute of Technology and Science, Hyderabad, India (M.P.M., M.K.K., V.R.); Department of Mathematics, Birla Institute of Technology and Science, Hyderabad, India (P.K.P.T.V); Department of Chemistry, Birla Institute of Technology and Science, Hyderabad, India (N.R.); Department of Medical Sciences, National Institute for the Mentally Handicapped (NIMH), Secunderabad, India (O.R.V.) Received October 17, 2013; accepted for publication September 29, 2014 Address for correspondence and reprints: Vidya Rajesh, Department of Biological Sciences, Birla Institute of Technology and Science, Pilani— Hyderabad Campus, Jawaharnagar, Shamirpet (M), Hyderabad 500078, Andhra Pradesh, India. E-mail: [email protected]; [email protected] Conflict of interest declaration: Madhu P. Mamidala: has no conflict of interest relevant to this article to disclose; Mahesh K. Kalikiri: has no conflict of interest relevant to this article to disclose; Praveen Kumar P.T.V.: has no conflict of interest relevant to this article to disclose; N. Rajesh: has no conflict of interest relevant to this article to disclose; OmSai R. Vallamkonda: has no conflict of interest relevant to this article to disclose; Vidya Rajesh: conflict of interest; research funded by UGC—F.37–118/2009(SR). Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/aur.1431 © 2014 International Society for Autism Research, Wiley Periodicals, Inc.
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Autism Research ••: ••–••, 2014
1
Figure 1. Types of consanguinity in Indian Population. (A,B) parallel first cousins; (C) cross first cousins; (D) uncle-niece; (E) double first cousins; (F) second cousins. Data Sources and Collection Our study, approved by the Institutional Human Ethics Committee, included children between 2 and 10 years of age across India. For recruitment of cases, collaborations were established with 70 centers covering nine cities. The presence of ASD was the sole criteria for the recruitment of cases. Children under a suspicion for ASD but without a formal diagnosis and children with cerebral palsy and Down syndrome were excluded from the study. Case ascertainment was done using confirmed report for diagnosis of ASD available with parent and the center. The diagnosis of ASD was done in a standard way based on internationally accepted standards, like the Diagnostic and Statistical Manual of Mental Disorders IV or the International Classification of Diseases 10 or the Indian Scale for Assessment of ASD, by a trained physician at the child psychiatry department of hospitals. Additionally, the Childhood Autism Rating Scale was used commonly to classify case status. For the control population (age- and gender-matched), collaborations were established with schools (regular, government-run, and private), and data was collected by visiting the houses randomly across the cities, and this ensured good representation of all sections of children belonging to diverse socioeconomic background. The detailed methodology for data collection is available in our earlier paper [Mamidala et al., 2013] and the same procedure was followed. A questionnaire was designed based on the probable risk factors of ASD from existing literature and those specific to Indian environment. Validity of the questionnaire was tested with a small convenient sample, followed by construct and reli-
2
ability testing. The data obtained concluded that the questionnaire was a good instrument for psychometric research and is communicated for publication elsewhere. An informed consent was taken from parents of the participating children (both cases and controls) prior to the study. Majority of the data was collected through direct interaction with the parents. In places where it was not feasible, teachers of those organizations were trained with respect to the content of the questionnaire, and they helped parents to complete the questionnaire by translating the content into their respective dialect, as well as clarifying any doubts. Analysis A total of 500 ASD children and an equal number of controls were enrolled in the study. The types of consanguinity were divided into four profiles, namely first cousins, second cousins, uncle-niece, and double first cousin [Bener, Hussain, & Teebi, 2007] (Fig. 1), and their inbreeding coefficient (F) was evaluated. Statistical analysis was done using SAS 9.1.3 (SAS Institute, Inc, Cary, NC, USA) version. We performed a univariable logistic regression analysis of all variables, and the data are available in our earlier paper [Mamidala et al., 2013]. In the present study, we evaluated the independent association of consanguinity by univariate analysis for the risk of ASD. Subsequently, a multivariable logistic regression model was performed to calculate odds ratio, 95% confidence intervals (CIs), and 5% α. The parental characteristics of consanguinity along with the reported prenatal, perinatal, and neonatal factors [Mamidala et al., 2013] were our explanatory variables, while ASD was our main
Mamidala et al./Consanguinity and autism spectrum disorder
INSAR
Table 1.
Analysis of Different Types of Consanguinity and Inbreeding Coefficients Number
Types of consanguinity Total First cousins Parallel cousins Cross cousins Uncle-niece Double first cousins Second cousins
Cases
%
Control
%
100 68 13 55 15 10 7
20 13.6 2.6 11 3 2 1.4
36 23 9 14 7 2 4
7.2 4.6 1.8 2.8 1.4 0.4 0.8
dependent variable. We also evaluated the risk contributing factors by conditioning consanguinity. We plotted the estimated log odds for each variable from the logistic regression model to probabilities. Reference standards considered for ordinal variables were 30 years and above for parental age [Grether, Anderson, Croen, Smith, & Windham, 2009], less than 37 weeks for abnormal gestational term, and less than 2500 g for low birth weight (LBW) category, respectively [Muthayya, 2009; WHO, 2004].
Inbreeding coefficient (F)
0.0625
0.125 0.125 0.0156
CI – 1.78, 5.44, P < 0.0001)], labor complications [odds ratio of 5.13 (95% CI – 2.98, 8.84, P < 0.0001)], and preterm birth [odds ratio of 1.83 (95% CI – 1.16, 2.88, P < 0.008)] remained significant in our study population. Also, LBW [odds ratio of 2.02 (95% CI – 1.39, 2.93, P < 0.01)], which was insignificant in our earlier study, became significant in this study. But when we conditioned consanguinity in multivariate analysis, all factors remained significant with decreased odds ratio (Table 2), and LBW again became insignificant (Table 2).
Discussion Results The recruitment was gender- and age-matched, and there were no significant differences in age or gender between the 500 cases and 500 controls. The male to female ratio in our population was calculated to be 4:1 (395 boys and 105 girls in cases, and 397 boys and 103 girls in controls). Characteristics of Consanguinity We evaluated the nature of consanguinity in our study population, the results of which are shown in Table 1 and Figure 1.The ASD cases had significant level of consanguinity when compared with controls, and statistical evaluation using univariate analysis substantiated that there is a risk factor of 3.22 (95% CI—2.07, 4.62, P < 0.0001) for ASD when parents are consanguineous. Risk Contribution of Consanguinity With Reported Risk Factors for ASD We also have analyzed the regression values of consanguinity along with reported prenatal, perinatal, and neonatal risk factors of ASD [Mamidala et al., 2013] using a multivariate approach. Among the factors analyzed (Table 2), advanced maternal age [odds ratio of 1.58 (95% CI – 1.15, 2.15, P – 0.003)], consanguinity [odds ratio of 3.20 (95% CI – 2.15, 4.82, P < 0.0001)], gestational respiratory tract infections [odds ratio of 4.77 (95% CI – 1.80, 12.65, P < 0.001)], fetal distress [odds ratio of 3.11 (95%
INSAR
This paper evaluates the effect of parental consanguinity on the risk for ASD using a case control design from an Indian population where consanguinity is relatively common. The risk of consanguinity for the ASD etiology by a factor of 3.2 found in our study population has several important implications. It raises the possibility of recessively inherited genetic risk factors for the etiology of ASD. Consanguinity is reported to have serious effects on fetal growth and development, and increases the risk of congenital malformations [Kulkarni & Kurian, 1990]. Moreover, it has been reported that children born to consanguineous parents had lower cognitive ability [Afzal, 1988] and social behavior [Afzal & Sinha, 1983], which are the major issues with ASD children. Hence, it is evident that consanguinity could contribute toward the risk for ASD. Consanguinity is also reported to increase the risk of adverse perinatal outcomes, including stillbirth [Hussain, Bittles, & Sullivan, 2001; Khoury & Massad, 2000; Stoltenberg, 1999], LBW [Benson, 2005; Hussain et al., 2001; Jordan, 2007; Khalid, Ghina, Fadi, & Fadi, 2006; Khoury & Massad, 2000; Mumtaz et al., 2007; Sezik, Ozkaya, Sezik, Yapar, & Kaya, 2006; Stoltenberg, 1999], preterm delivery [Al-Eissa & Ba’Aqeel, 1994; Mumtaz et al., 2010], apnea of prematurity [Tamim, Khogali, & Beydoun, 2003], infant and child mortality [Bittles & Black, 2010], and congenital birth defects and malformations [Jaber et al., 2005].
Mamidala et al./Consanguinity and autism spectrum disorder
3
Table 2.
Logistic Regression of Risk Factors of Autism Spectrum Disorder Cases
Factors Advanced paternal agea Advanced maternal ageb Consanguinity Respiratory tract infections Fetal distress Labor complications Preterm birthc Low birth weightd
Controls
Multivariate analysis (conditioned consanguinity)
Multivariate analysis
Number
%
Number
%
Odds ratio
[95% confidence interval]
340 121 100 23 75 94 73 90
68 24.2 20 4.6 15 18.8 14.6 18
334 84 36 5 19 18 37 49
66.8 16.8 7.2 0.1 3.8 3.6 7.4 9.8
1.05 1.58 3.20 4.77 3.11 5.13 1.83 2.02
[0.81, 1.37] [1.15, 2.15] [2.15, 4.82] [1.80, 12.65] [1.78, 5.44] [2.98, 8.84] [1.16, 2.88] [1.39, 2.93]
P-value
Odds ratio
[95% confidence interval]
P-value
0.68 0.003
