Longitudinal genetic analysis of problem behaviours in biologically related and unrelated adoptees

The genetic and environmental influences on problem behaviors at two assessment points, three years apart, and their stability were studied in a sample of international adoptees, initially aged 10 to 15 years. Parents of 111 pairs of adopted biological siblings, 221 pairs of adopted nonbiological siblings and 1484 adopted singletons completed the Child Behavior Checklist (75 pairs, 154 pairs and 1080 singletons respectively at second assessment). At first assessment, genetic factors accounted for more than 50% of the variance in the Externalizing, Aggressive Behavior, Attention Problems and Social Problems scales. Shared environmental influences explained 40% of the variance in the Total Problem scale and less for all other scales. Nonshared environmental influences were most important for the Internalizing scale and its subscales, and for the Thought Problems and Delinquent Behavior scales. At the second assessment, genetic factors explained most of the variance in the Total Problem, Externalizing and Aggressive Behavior scales, while nonshared environmental influences explained most of the variance in all other scales. Shared environmental influences explained 33% of the variance in the Internalizing scale and less for the other scales. The stability of the Externalizing scale over time was caused mostly by genetic factors, while nonshared environmental factors mostly caused the stability of the Internalizing scale

Genetic and environmental influences on Anxious/Depression during childhood: a study from the Netherlands Twin Register

For a large sample of twin pairs from the Netherlands Twins Register who were recruited at birth and followed through childhood, we obtained parental ratings of Anxious/Depression (A/D). Maternal ratings were obtained at ages 3 years (for 9025 twin pairs), 5 years (9222 pairs), 7 years (7331 pairs), 10 years (4430 pairs) and 12 years (2363 pairs). For 60-90% of the pairs, father ratings were also available. Multivariate genetic models were used to test for rater-independent and rater-specific assessments of A/D and to determine the genetic and environmental influences on individual differences in A/D at different ages. At all ages, monozygotic twins resembled each other more closely for A/D than dizygotic twins, implying genetic influences on variation in A/D. Opposite sex twin pairs resembled each other to same extent as same-sex dizygotic twins, suggesting that the same genes are expressed in boys and girls. Heritability estimates for rater-independent A/D were high in 3-year olds (76%) and decreased in size as children grew up [60% at age 5, 67% at age 7, 53% at age 10 (60% in boys) and 48% at age 12 years]. The decrease in genetic influences was accompanied by an increase in the influence of the shared family environment [absent at ages 3 and 7, 16% at age 5, 20% at age 10 (5% in boys) and 18% at age 12 years]. The agreement between parental A/D ratings was between 0.5 and 0.7, with somewhat higher correlations for the youngest group. Disagreement in ratings between the parents was not merely the result of unreliability or rater bias. Both the parents provided unique information from their own perspective on the behavior of their children. Significant influences of genetic and shared environmental factors were found for the unique parental views. At all ages, the contribution of shared environmental factors to variation in rater-specific views was higher for father ratings. Also, at all ages except age 12, the heritability estimates for the rater-specific phenotype were higher for mother ratings (59% at age 3 and decreasing to 27% at age 12 years) than for father ratings (between 14 and 29%). Differences between children, even as young as 3 years, in A/D are to a large extent due to genetic differences. As children grow up, the variation in A/D is due in equal parts to genetic and environmental influences. Anxious/Depression, unlike many other common childhood psychopathologies, is influenced by the shared family environment. These findings may provide support for why certain family therapeutic approaches are effective in the A/D spectrum of illnesses. Copyright © Blackwell Munksgaard 2005

Heritability of attention problems in children II: longitudinal results from a study of twins age 3 to 12.

this paper we present data of large samples of twin families, with an equal number of girls and boys. The well-known gender difference with boys displaying more OA and AP was observed at each age. Even at the age of 3, boys display more OA problems than girls. Clinical studies have indicated that severe problem behavior can be identified in very young children (see for review, Campbell, 1995; Keenan & Wakschlag, 2000; Shaw, Owens, Giovannelli, & Winslow, 2001) and that the onset of ADHD is during the pre-school period (Barkley, Fisher, Edelbrock, & Smallish, 1990; Table 6 Top part includes percentages of total variances (diagonal) and covariances (off-diagonal) explained by additive genetic, genetic dominance, and unique environmental components based on best fitting models. Percentages for boys and girls are reported below and above diagonal, respectively. Lower part includes correlations calculated for additive genetic, genetic dominance, and unique environmental sources of variance between different ages. Correlations for boys and girls are reported below and above diagonal, respectively Relative proportions of variance and covariance BoysnGirls A% D% E% OA 3 AP 7 AP 10 AP 12 OA 3 AP 7 AP 10 AP 12 OA 3 AP 7 AP 10 AP 12 OA 3 50n41 73 79 75 22n33 17 13 14 28n26 10 8 11 AP 7 59 33n57 50 53 31 39n16 31 28 10 28n27 19 19 AP 10 86 31 41n48 47 6 51 31n25 32 8 18 28n27 21 AP 12 71 24 31 40n54 16 55 45 30n18 13 21 24 30n28 Correlations between different ages BoysnGirls ADE OA 3 AP 7 AP 10 AP 12 OA 3 AP 7 AP 10 AP 12 OA 3 AP 7 AP 10 AP 12 OA 3 1.00 .60 .66 .57 1.00 .30 .16 .20 1.00 .15 .12 .14 AP 7 .57 1.00 .62 .57 .41 1.00 .99 1.00 .15 1.00 .46 .41 AP 10 .68 .56 1.00 .61 .08 .94 1.00 1.00 .11 .42 1.00 .50 AP 12 .49 .42 .53 1.00 .20 .98 .99 1.00 .14 .45 .58 1.00 ..

No Reliable Association between Runs of Homozygosity and Schizophrenia in a Well-Powered Replication Study

It is well known that inbreeding increases the risk of recessive monogenic diseases, but it is less certain whether it contributes to the etiology of complex diseases such as schizophrenia. One way to estimate the effects of inbreeding is to examine the association between disease diagnosis and genome-wide autozygosity estimated using runs of homozygosity (ROH) in genome-wide single nucleotide polymorphism arrays. Using data for schizophrenia from the Psychiatric Genomics Consortium (n = 21,868), Keller et al. (2012) estimated that the odds of developing schizophrenia increased by approximately 17% for every additional percent of the genome that is autozygous (β = 16.1, CI(β) = [6.93, 25.7], Z = 3.44, p = 0.0006). Here we describe replication results from 22 independent schizophrenia case-control datasets from the Psychiatric Genomics Consortium (n = 39,830). Using the same ROH calling thresholds and procedures as Keller et al. (2012), we were unable to replicate the significant association between ROH burden and schizophrenia in the independent PGC phase II data, although the effect was in the predicted direction, and the combined (original + replication) dataset yielded an attenuated but significant relationship between Froh and schizophrenia (β = 4.86,CI(β) = [0.90,8.83],Z = 2.40,p = 0.02). Since Keller et al. (2012), several studies reported inconsistent association of ROH burden with complex traits, particularly in case-control data. These conflicting results might suggest that the effects of autozygosity are confounded by various factors, such as socioeconomic status, education, urbanicity, and religiosity, which may be associated with both real inbreeding and the outcome measures of interest

Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood

J. Lönnqvist on työryhmän Psychiat Genomics Consortium jäsen.Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e., linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual sample and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on similar to 150,000 individuals give a higher accuracy than LDSC estimates based on similar to 400,000 individuals (from combinedmeta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-datasets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if sample sizes are lesser.Peer reviewe