Sarah E. Medland

LRRTM1 on chromosome 2p12 is a maternally suppressed gene that is associated paternally with handedness and schizophrenia

Left–right asymmetrical brain function underlies much of human cognition, behavior and emotion. Abnormalities of cerebral asymmetry are associated with schizophrenia and other neuropsychiatric disorders. The molecular, developmental and evolutionary origins of human brain asymmetry are unknown. We found significant association of a haplotype upstream of the gene LRRTM1 (Leucine-rich repeat transmembrane neuronal 1) with a quantitative measure of human handedness in a set of dyslexic siblings, when the haplotype was inherited paternally (P=0.00002). While we were unable to find this effect in an epidemiological set of twin-based sibships, we did find that the same haplotype is overtransmitted paternally to individuals with schizophrenia/schizoaffective disorder in a study of 1002 affected families (P=0.0014). We then found direct confirmatory evidence that LRRTM1 is an imprinted gene in humans that shows a variable pattern of maternal downregulation. We also showed that LRRTM1 is expressed during the development of specific forebrain structures, and thus could influence neuronal differentiation and connectivity. This is the first potential genetic influence on human handedness to be identified, and the first putative genetic effect on variability in human brain asymmetry. LRRTM1 is a candidate gene for involvement in several common neurodevelopmental disorders, and may have played a role in human cognitive and behavioral evolution.

Genome Partitioning of Genetic Variation for Height from 11,214 Sibling Pairs

Height has been used for more than a century as a model by which to understand quantitative genetic variation in humans. We report that the entire genome appears to contribute to its additive genetic variance. We used genotypes and phenotypes of 11,214 sibling pairs from three countries to partition additive genetic variance across the genome. Using genome scans to estimate the proportion of the genomes of each chromosome from siblings that were identical by descent, we estimated the heritability of height contributed by each of the 22 autosomes and the X chromosome. We show that additive genetic variance is spread across multiple chromosomes and that at least six chromosomes (i.e., 3, 4, 8, 15, 17, and 18) are responsible for the observed variation. Indeed, the data are not inconsistent with a uniform spread of trait loci throughout the genome. Our estimate of the variance explained by a chromosome is correlated with the number of times suggestive or significant linkage with height has been reported for that chromosome. Variance due to dominance was not significant but was difficult to assess because of the high sampling correlation between additive and dominance components. Results were consistent with the absence of any large between-chromosome epistatic effects. Notwithstanding the proposed architecture of complex traits that involves widespread gene-gene and gene-environment interactions, our results suggest that variation in height in humans can be explained by many loci distributed over all autosomes, with an additive mode of gene action.

High loading of polygenic risk for ADHD in children with comorbid aggression

Objective Although attention deficit hyperactivity disorder (ADHD) is highly heritable, genome-wide association studies (GWAS) have not yet identified any common genetic variants that contribute to risk. There is evidence that aggression or conduct disorder in children with ADHD indexes higher genetic loading and clinical severity. The authors examine whether common genetic variants considered en masse as polygenic scores for ADHD are especially enriched in children with comorbid conduct disorder. Method Polygenic scores derived from an ADHD GWAS meta-analysis were calculated in an independent ADHD sample (452 case subjects, 5,081 comparison subjects). Multivariate logistic regression analyses were employed to compare polygenic scores in the ADHD and comparison groups and test for higher scores in ADHD case subjects with comorbid conduct disorder relative to comparison subjects and relative to those without comorbid conduct disorder. Association with symptom scores was tested using linear regression. Results Polygenic risk for ADHD, derived from the meta-analysis, was higher in the independent ADHD group than in the comparison group. Polygenic score was significantly higher in ADHD case subjects with conduct disorder relative to ADHD case subjects without conduct disorder. ADHD polygenic score showed significant association with comorbid conduct disorder symptoms. This relationship was explained by the aggression items. Conclusions Common genetic variation is relevant to ADHD, especially in individuals with comorbid aggression. The findings suggest that the previously published ADHD GWAS meta-analysis contains weak but true associations with common variants, support for which falls below genome-wide significance levels. The findings also highlight the fact that aggression in ADHD indexes genetic as well as clinical severity.

Replication of the association of common rs9939609 variant of FTO with increased BMI in an Australian adult twin population but no evidence for gene by environment (G × E) interaction

Objective:To further investigate a common variant (rs9939609) in the fat mass- and obesity-associated gene (FTO), which recent genome-wide association studies have shown to be associated with body mass index (BMI) and obesity.Design:We examined the effect of this FTO variant on BMI in 3353 Australian adult male and female twins.Results:The minor A allele of rs9939609 was associated with an increased BMI (P=0.0007). Each additional copy of the A allele was associated with a mean BMI increase of ∼1.04u2009kg/m2 (∼3.71u2009kg). Using variance components decomposition, we estimate that this single-nucleotide polymorphism accounts for ∼3% of the genetic variance in BMI in our sample (∼2% of the total variance). By comparing intrapair variances of monozygotic twins of different genotypes we were able to perform a direct test of gene by environment (G × E) interaction in both sexes and gene by parity (G × P) interaction in women, but no evidence was found for either.Conclusions:In addition to supporting earlier findings that the rs9939609 variant in the FTO gene is associated with an increased BMI, our results indicate that the associated genetic effect does not interact with environment or parity.

Mining the Human Phenome Using Allelic Scores That Index Biological Intermediates

It is common practice in genome-wide association studies (GWAS) to focus on the relationship between disease risk and genetic variants one marker at a time. When relevant genes are identified it is often possible to implicate biological intermediates and pathways likely to be involved in disease aetiology. However, single genetic variants typically explain small amounts of disease risk. Our idea is to construct allelic scores that explain greater proportions of the variance in biological intermediates, and subsequently use these scores to data mine GWAS. To investigate the approachs properties, we indexed three biological intermediates where the results of large GWAS meta-analyses were available: body mass index, C-reactive protein and low density lipoprotein levels. We generated allelic scores in the Avon Longitudinal Study of Parents and Children, and in publicly available data from the first Wellcome Trust Case Control Consortium. We compared the explanatory ability of allelic scores in terms of their capacity to proxy for the intermediate of interest, and the extent to which they associated with disease. We found that allelic scores derived from known variants and allelic scores derived from hundreds of thousands of genetic markers explained significant portions of the variance in biological intermediates of interest, and many of these scores showed expected correlations with disease. Genome-wide allelic scores however tended to lack specificity suggesting that they should be used with caution and perhaps only to proxy biological intermediates for which there are no known individual variants. Power calculations confirm the feasibility of extending our strategy to the analysis of tens of thousands of molecular phenotypes in large genome-wide meta-analyses. We conclude that our method represents a simple way in which potentially tens of thousands of molecular phenotypes could be screened for causal relationships with disease without having to expensively measure these variables in individual disease collections.

A Note on the Parameterization of Purcell’s G × E Model for Ordinal and Binary Data

Following the publication of Purcell’s approach to the modeling of gene by environment interaction in 2002, the interest in Gxa0×xa0E modeling in twin and family data increased dramatically. The analytic techniques described by Purcell were designed for use with continuous data. Here we explore the re-parameterization of these models for use with ordinal and binary outcome data. Analysis of binary and ordinal data within the context of a liability threshold model traditionally requires constraining the total variance to unity to ensure identification. Here, we demonstrate an alternative approach for use with ordinal data, in which the values of the first two thresholds are fixed, thus allowing the total variance to change as function of the moderator. We also demonstrate that when using binary data, constraining the total variance to unity for a given value of the moderator is sufficient to ensure identification. Simulation results indicate that analyses of ordinal and binary data can recover both the raw and standardized patterns of results. However, the scale of the results is dependent on the specification of (threshold or variance) constraints rather than the underlying distribution of liability. Example Mx scripts are provided.

Relative Finger Lengths, Sex Differences, and Psychological Traits

Various finger length and personality and ability measures were obtained for a sample of Australian adolescent twins (306 boys and 397 girls). A new measure of relative finger length (the length of a given finger relative to the sum of all four fingers) was investigated, and shown to be superior to the traditional 2D:4D for discriminating between the sexes. It also had the advantage of permitting a more analytic approach: for example, the 2nd finger-length contributed much more than the 4th finger length to the sex difference in 2D:4D, and a smooth gradient of sex differences across the hand was evident. Sex differences on right hands were greater than those for left hands. Within-sex correlations were obtained between the various finger-length measures and a personality and an ability scale that showed relatively large sex differences (Eysenck’s Psychoticism scale and the spatial subscale from Jackson’s Multidimensional Aptitude Battery). The correlations were low, but on the whole consistent with the between-sex differences for the girls. For the boys, this was so for Psychoticism, but spatial ability was, if anything, correlated in the opposite direction.

A Bivariate Twin Study of Regional Brain Volumes and Verbal and Nonverbal Intellectual Skills During Childhood and Adolescence

Twin studies indicate that both intelligence and brain structure are moderately to highly heritable. Recent bivariate studies of adult twins also suggest that intelligence and brain morphometry are influenced by shared genetic factors. The current study examines shared genetic and environmental factors between brain morphometry and intelligence in a sample of children and adolescents (twins, twin siblings, and singletons; nxa0=xa0649, ages 4–19). To extend previous studies, brain morphometric data were parsed into subregions (lobar gray/white matter volumes, caudate nucleus, lateral ventricles) and intelligence into verbal and nonverbal skills (Wechsler Vocabulary and Block Design subtests). Phenotypic relationships between brain volumes and intelligence were small. Verbal skills shared unique environmental effects with gray matter volumes while nonverbal skills shared genetic effects with both global and regional gray and white matter. These results suggest that distinct mechanisms contribute to the small phenotypic relationships between brain volumes and verbal versus nonverbal intelligence.

Flexible Mx specification of various extended twin kinship designs

The extended twin kinship design allows the simultaneous testing of additive and nonadditive genetic, shared and individual-specific environmental factors, as well as sex differences in the expression of genes and environment in the presence of assortative mating and combined genetic and cultural transmission (Eaves et al., 1999). It also handles the contribution of these sources of variance to the (co)variation of multiple phenotypes. Keller et al. (2008) extended this comprehensive model for family resemblance to allow or a flexible specification of assortment and vertical transmission. As such, it provides a general framework which can easily be reduced to fit subsets of data such as twin-parent data, children-of-twins data, etc. A flexible Mx specification of this model that allows handling of these various designs is presented in detail and applied to data from the Virginia 30,000. Data on height, body mass index, smoking status, church attendance, and political affiliation were obtained from twins and their families. Results indicate that biases in the estimation of variance components depend both on the types of relative available for analysis, and on the underlying genetic and environmental architecture of the phenotype of interest.

Gradient Boosting as a SNP Filter: an Evaluation Using Simulated and Hair Morphology Data

Typically, genome-wide association studies consist of regressing the phenotype on each SNP separately using an additive genetic model. Although statistical models for recessive, dominant, SNP-SNP, or SNP-environment interactions exist, the testing burden makes an evaluation of all possible effects impractical for genome-wide data. We advocate a two-step approach where the first step consists of a filter that is sensitive to different types of SNP main and interactions effects. The aim is to substantially reduce the number of SNPs such that more specific modeling becomes feasible in a second step. We provide an evaluation of a statistical learning method called “gradient boosting machine” (GBM) that can be used as a filter. GBM does not require an a priori specification of a genetic model, and permits inclusion of large numbers of covariates. GBM can therefore be used to explore multiple GxE interactions, which would not be feasible within the parametric framework used in GWAS. We show in a simulation that GBM performs well even under conditions favorable to the standard additive regression model commonly used in GWAS, and is sensitive to the detection of interaction effects even if one of the interacting variables has a zero main effect. The latter would not be detected in GWAS. Our evaluation is accompanied by an analysis of empirical data concerning hair morphology. We estimate the phenotypic variance explained by increasing numbers of highest ranked SNPs, and show that it is sufficient to select 10K-20K SNPs in the first step of a two-step approach.