Genetic and social influences on starting to smoke: a study of Dutch adolescent twins and their parents

In a study of 1600 Dutch adolescent twin pairs we found that 59% of the inter‐individual variation in smoking behaviour could be attributed to shared environmental influences and 31% to genetic factors. The magnitude of the genetic and environmental effects did not differ between boys and girls. However, environmental effects shared by male twins and environmental effects shared by female twins were imperfectly correlated in twins from opposite‐sex pairs, indicating that different environmental factors influence smoking in adolescent boys and girls. In the parents of these twins, the correlation between husband and wife for‘currently smoking’(r = 0.43) was larger than for‘ever smoked’(r = 0.18). There was no evidence that smoking of parents (at present or in the past) encouraged smoking in their offspring. Resemblance between parents and offspring was significant but rather low and could be accounted for completely by their genetic relatedness. Moreover, the association between‘currently smoking’in the parents and smoking behaviour in their children was not larger than the association between‘ever smoking’in parents and smoking in their children. Copyright © 1994, Wiley Blackwell. All rights reserve

Targeted Next-Generation Sequencing Analysis of 1,000 Individuals with Intellectual Disability.

To identify genetic causes of intellectual disability (ID), we screened a cohort of 986 individuals with moderate to severe ID for variants in 565 known or candidate ID-associated genes using targeted next-generation sequencing. Likely pathogenic rare variants were found in ∼11% of the cases (113 variants in 107/986 individuals: ∼8% of the individuals had a likely pathogenic loss-of-function [LoF] variant, whereas ∼3% had a known pathogenic missense variant). Variants in SETD5, ATRX, CUL4B, MECP2, and ARID1B were the most common causes of ID. This study assessed the value of sequencing a cohort of probands to provide a molecular diagnosis of ID, without the availability of DNA from both parents for de novo sequence analysis. This modeling is clinically relevant as 28% of all UK families with dependent children are single parent households. In conclusion, to diagnose patients with ID in the absence of parental DNA, we recommend investigation of all LoF variants in known genes that cause ID and assessment of a limited list of proven pathogenic missense variants in these genes. This will provide 11% additional diagnostic yield beyond the 10%-15% yield from array CGH alone.Action Medical Research (SP4640); the Birth Defect Foundation (RG45448); the Cambridge National Institute for Health Research Biomedical Research Centre (RG64219); the NIHR Rare Diseases BioResource (RBAG163); Wellcome Trust award WT091310; The Cell lines and DNA bank of Rett Syndrome, X-linked mental retardation and other genetic diseases (member of the Telethon Network of Genetic Biobanks (project no. GTB12001); the Genetic Origins of Congenital Heart Disease Study (GO-CHD)- funded by British Heart Foundation (BHF)This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1002/humu.2290

Linking Protective GAB2 Variants, Increased Cortical GAB2 Expression and Decreased Alzheimer's Disease Pathology

GRB-associated binding protein 2 (GAB2) represents a compelling genome-wide association signal for late-onset Alzheimer's disease (LOAD) with reported odds ratios (ORs) ranging from 0.75-0.85. We tested eight GAB2 variants in four North American Caucasian case-control series (2,316 LOAD, 2,538 controls) for association with LOAD. Meta-analyses revealed ORs ranging from (0.61-1.20) with no significant association (all p>0.32). Four variants were hetergeneous across the populations (all p<0.02) due to a potentially inflated effect size (OR = 0.61-0.66) only observed in the smallest series (702 LOAD, 209 controls). Despite the lack of association in our series, the previously reported protective association for GAB2 remained after meta-analyses of our data with all available previously published series (11,952-22,253 samples; OR = 0.82-0.88; all p<0.04). Using a freely available database of lymphoblastoid cell lines we found that protective GAB2 variants were associated with increased GAB2 expression (p = 9.5×10-9.3×10). We next measured GAB2 mRNA levels in 249 brains and found that decreased neurofibrillary tangle (r = -0.34, p = 0.0006) and senile plaque counts (r = -0.32, p = 0.001) were both good predictors of increased GAB2 mRNA levels albeit that sex (r = -0.28, p = 0.005) may have been a contributing factor. In summary, we hypothesise that GAB2 variants that are protective against LOAD in some populations may act functionally to increase GAB2 mRNA levels (in lymphoblastoid cells) and that increased GAB2 mRNA levels are associated with significantly decreased LOAD pathology. These findings support the hypothesis that Gab2 may protect neurons against LOAD but due to significant population heterogeneity, it is still unclear whether this protection is detectable at the genetic level. © 2013 Zou et al

Applying polygenic risk scoring for psychiatric disorders to a large family with bipolar disorder and major depressive disorder

Psychiatric disorders are thought to have a complex genetic pathology consisting of interplay of common and rare variation. Traditionally, pedigrees are used to shed light on the latter only, while here we discuss the application of polygenic risk scores to also highlight patterns of common genetic risk. We analyze polygenic risk scores for psychiatric disorders in a large pedigree (n similar to 260) in which 30% of family members suffer from major depressive disorder or bipolar disorder. Studying patterns of assortative mating and anticipation, it appears increased polygenic risk is contributed by affected individuals who married into the family, resulting in an increasing genetic risk over generations. This may explain the observation of anticipation in mood disorders, whereby onset is earlier and the severity increases over the generations of a family. Joint analyses of rare and common variation may be a powerful way to understand the familial genetics of psychiatric disorders

Using brain cell-type-specific protein interactomes to interpret neurodevelopmental genetic signals in schizophrenia

Genetics have nominated many schizophrenia risk genes and identified convergent signals between schizophrenia and neurodevelopmental disorders. However, functional interpretation of the nominated genes in the relevant brain cell types is often lacking. We executed interaction proteomics for six schizophrenia risk genes that have also been implicated in neurodevelopment in human induced cortical neurons. The resulting protein network is enriched for common variant risk of schizophrenia in Europeans and East Asians, is down-regulated in layer 5/6 cortical neurons of individuals affected by schizophrenia, and can complement fine-mapping and eQTL data to prioritize additional genes in GWAS loci. A sub-network centered on HCN1 is enriched for common variant risk and contains proteins (HCN4 and AKAP11) enriched for rare protein-truncating mutations in individuals with schizophrenia and bipolar disorder. Our findings showcase brain cell-type-specific interactomes as an organizing framework to facilitate interpretation of genetic and transcriptomic data in schizophrenia and its related disorders

Comparative Linkage Meta-Analysis Reveals Regionally-Distinct, Disparate Genetic Architectures: Application to Bipolar Disorder and Schizophrenia

New high-throughput, population-based methods and next-generation sequencing capabilities hold great promise in the quest for common and rare variant discovery and in the search for ”missing heritability.” However, the optimal analytic strategies for approaching such data are still actively debated, representing the latest rate-limiting step in genetic progress. Since it is likely a majority of common variants of modest effect have been identified through the application of tagSNP-based microarray platforms (i.e., GWAS), alternative approaches robust to detection of low-frequency (1–5% MAF) and rare (<1%) variants are of great importance. Of direct relevance, we have available an accumulated wealth of linkage data collected through traditional genetic methods over several decades, the full value of which has not been exhausted. To that end, we compare results from two different linkage meta-analysis methods—GSMA and MSP—applied to the same set of 13 bipolar disorder and 16 schizophrenia GWLS datasets. Interestingly, we find that the two methods implicate distinct, largely non-overlapping, genomic regions. Furthermore, based on the statistical methods themselves and our contextualization of these results within the larger genetic literatures, our findings suggest, for each disorder, distinct genetic architectures may reside within disparate genomic regions. Thus, comparative linkage meta-analysis (CLMA) may be used to optimize low-frequency and rare variant discovery in the modern genomic era