@article {388, title = {Content, diagnostic, correlational, and genetic similarities between common measures of childhood aggressive behaviors and related psychiatric traits}, journal = {Journal of Child Psychology and Psychiatry}, volume = {61}, year = {2020}, pages = {1328-1338}, abstract = {

Background Given the role of childhood aggressive behavior (AGG) in everyday child development, precise and accurate measurement is critical in clinical practice and research. This study aims to quantify agreement among widely used measures of childhood AGG regarding item content, clinical concordance, correlation, and underlying genetic construct. Methods We analyzed data from 1254 Dutch twin pairs (age 8{\^a}\€\“10{\^A}\ years, 51.1\% boys) from a general population sample for whom both parents completed the A-TAC, CBCL, and SDQ at the same occasion. Results There was substantial variation in item content among AGG measures, ranging from .00 (i.e., mutually exclusive) to .50 (moderate agreement). Clinical concordance (i.e., do the same children score above a clinical threshold among AGG measures) was very weak to moderate with estimates ranging between .01 and .43 for mother-reports and between .12 and .42 for father-reports. Correlations among scales were weak to strong, ranging from .32 to .70 for mother-reports and from .32 to .64 for father-reports. We found weak to very strong genetic correlations among the measures, with estimates between .65 and .84 for mother-reports and between .30 and .87 for father-reports. Conclusions Our results demonstrated that degree of agreement between measures of AGG depends on the type (i.e., item content, clinical concordance, correlation, genetic correlation) of agreement considered. Because agreement was higher for correlations compared to clinical concordance (i.e., above or below a clinical cutoff), we propose the use of continuous scores to assess AGG, especially for combining data with different measures. Although item content can be different and agreement among observed measures may not be high, the genetic correlations indicate that the underlying genetic liability for childhood AGG is consistent across measures.

}, keywords = {Childhood aggressive behavior, clinical concordance, Genetic correlation, item overlap}, doi = {https://doi.org/10.1111/jcpp.13218}, url = {https://acamh.onlinelibrary.wiley.com/doi/abs/10.1111/jcpp.13218}, author = {Hendriks, Anne M. and Ip, Hill F. and Nivard, Michel G. and Finkenauer, Catrin and Van Beijsterveldt, Catharina E.M. and Bartels, Meike and Boomsma, Dorret I.} } @article {249, title = {Characterizing the Relation Between Expression QTLs and Complex Traits: Exploring the Role of Tissue Specificity}, journal = {Behavior Genetics}, volume = {48}, year = {2018}, month = {Sep}, pages = {374{\textendash}385}, abstract = {

Measurement of gene expression levels and detection of eQTLs (expression quantitative trait loci) are difficult in tissues with limited sample availability, such as the brain. However, eQTL overlap between tissues might be high, which would allow for inference of eQTL functioning in the brain via eQTLs detected in readily accessible tissues, e.g. whole blood. Applying Stratified Linkage Disequilibrium Score Regression (SLDSR), we quantified the enrichment in polygenic signal of blood and brain eQTLs in genome-wide association studies (GWAS) of 11 complex traits. We looked at eQTLs discovered in 44 tissues by the Genotype-Tissue Expression (GTEx) consortium and two other large representative studies, and found no tissue-specific eQTL effects. Next, we integrated the GTEx eQTLs with regions associated with tissue-specific histone modifiers, and interrogated their effect on rheumatoid arthritis and schizophrenia. We observed substantially enriched effects of eQTLs located inside regions bearing modification H3K4me1 on schizophrenia, but not rheumatoid arthritis, and not tissue-specific. Finally, we extracted eQTLs associated with tissue-specific differentially expressed genes and determined their effects on rheumatoid arthritis and schizophrenia, these analysis revealed limited enrichment of eQTLs associated with gene specifically expressed in specific tissues. Our results pointed to strong enrichment of eQTLs in their effect on complex traits, without evidence for tissue-specific effects. Lack of tissue-specificity can be either due to a lack of statistical power or due to the true absence of tissue-specific effects. We conclude that eQTLs are strongly enriched in GWAS signal and that the enrichment is not specific to the eQTL discovery tissue. Until sample sizes for eQTL discovery grow sufficiently large, working with relatively accessible tissues as proxy for eQTL discovery is sensible and restricting lookups for GWAS hits to a specific tissue for which limited samples are available might not be advisable.

}, issn = {1573-3297}, doi = {10.1007/s10519-018-9914-2}, url = {https://doi.org/10.1007/s10519-018-9914-2}, author = {Ip, Hill F. and Jansen, Rick and Abdellaoui, Abdel and Meike Bartels and UK Brain Expression Consortium and Ryten, Mina and Hardy, John and Weale, Michael E. and Ramasamy, Adaikalavan and Forabosco, Paola and Matarin, Mar and Vandrovcova, Jana and Botia, Juan A. and D{\textquoteright}Sa, Karishma and Guelfi, Sebastian and Smith, Colin and Walker, Robert and Reynolds, Regina H. and Zhang, David and Trabzuni, Daniah and Dorret I. Boomsma and Michel G. Nivard} } @article {269, title = {Genomic SEM Provides Insights into the Multivariate Genetic Architecture of Complex Traits}, journal = {bioRxiv}, year = {2018}, abstract = {

Methods for using GWAS to estimate genetic correlations between pairwise combinations of traits have produced \’atlases\’ of genetic architecture. Genetic atlases reveal pervasive pleiotropy, and genome-wide significant loci are often shared across different phenotypes. We introduce genomic structural equation modeling (Genomic SEM), a multivariate method for analyzing the joint genetic architectures of complex traits. Using formal methods for modeling covariance structure, Genomic SEM synthesizes genetic correlations and SNP-heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap. Genomic SEM can be used to identify variants with effects on general dimensions of cross-trait liability, boost power for discovery, and calculate more predictive polygenic scores. Finally, Genomic SEM can be used to identify loci that cause divergence between traits, aiding the search for what uniquely differentiates highly correlated phenotypes. We demonstrate several applications of Genomic SEM, including a joint analysis of GWAS summary statistics from five genetically correlated psychiatric traits. We identify 27 independent SNPs not previously identified in the univariate GWASs, 5 of which have been reported in other published GWASs of the included traits. Polygenic scores derived from Genomic SEM consistently outperform polygenic scores derived from GWASs of the individual traits. Genomic SEM is flexible, open ended, and allows for continuous innovations in how multivariate genetic architecture is modeled.

}, doi = {10.1101/305029}, url = {https://www.biorxiv.org/content/early/2018/04/21/305029}, author = {Grotzinger, Andrew D and Rhemtulla, Mijke and de Vlaming, Ronald and Ritchie, Stuart J. and Mallard, Travis T. and Hill, W. David and Ip, Hill F. and McIntosh, Andrew M. and Deary, Ian J. and Koellinger, Philipp D. and Harden, K. Paige and Michel G. Nivard and Tucker-Drob, Elliot M.} }