Schizophrenia-associated somatic copy-number variants from 12,834 cases reveal recurrent NRXN1 and ABCB11 disruptions

While germline copy-number variants (CNVs) contribute to schizophrenia (SCZ) risk, the contribution of somatic CNVs (sCNVs)—present in some but not all cells—remains unknown. We identified sCNVs using blood-derived genotype arrays from 12,834 SCZ cases and 11,648 controls, filtering sCNVs at loci recurrently mutated in clonal blood disorders. Likely early-developmental sCNVs were more common in cases (0.91%) than controls (0.51%, p = 2.68e−4), with recurrent somatic deletions of exons 1–5 of the NRXN1 gene in five SCZ cases. Hi-C maps revealed ectopic, allele-specific loops forming between a potential cryptic promoter and non-coding cis-regulatory elements upon 5′ deletions in NRXN1. We also observed recurrent intragenic deletions of ABCB11, encoding a transporter implicated in anti-psychotic response, in five treatment-resistant SCZ cases and showed that ABCB11 is specifically enriched in neurons forming mesocortical and mesolimbic dopaminergic projections. Our results indicate potential roles of sCNVs in SCZ risk

Household environmental microbiota influences early-life eczema development

Exposure to a diverse microbial environment during pregnancy and early postnatal period is important in determining predisposition towards allergy. However, the effect of environmental microbiota exposure on allergy during preconception, pregnancy and postnatal life on development of allergy in the child has not been investigated so far. In the S-PRESTO (Singapore PREconception Study of long Term maternal and child Outcomes) cohort, we collected house dust during all three critical window periods and analysed microbial composition using 16S rRNA gene sequencing. At 6 and 18 months, the child was assessed for eczema by clinicians. In the eczema group, household environmental microbiota was characterized by presence of human-associated bacteria Actinomyces, Anaerococcus, Finegoldia, Micrococcus, Prevotella and Propionibacterium at all time points, suggesting their possible contributions to regulating host immunity and increasing the susceptibility to eczema. In the home environment of the control group, putative protective effect of an environmental microbe Planomicrobium (Planococcaceae family) was observed to be significantly higher than that in the eczema group. Network correlation analysis demonstrated inverse relationships between beneficial Planomicrobium and human associated bacteria (Actinomyces, Anaerococcus, Finegoldia, Micrococcus, Prevotella and Propionibacterium). Exposure to natural environmental microbiota may be beneficial to modulate shed human associated microbiota in an indoor environment

Household environmental microbiota influences early-life eczema development

Exposure to a diverse microbial environment during pregnancy and early postnatal period is important in determining predisposition towards allergy. However, the effect of environmental microbiota exposure during preconception, pregnancy and postnatal life on development of allergy in the child has not been investigated so far. In the S-PRESTO (Singapore PREconception Study of long Term maternal and child Outcomes) cohort, we collected house dust during all three critical window periods and analysed microbial composition using 16S rRNA gene sequencing. At 6 and 18 months, the child was assessed for eczema by clinicians. In the eczema group, household environmental microbiota was characterized by presence of human-associated bacteria Actinomyces, Anaerococcus, Finegoldia, Micrococcus, Prevotella and Propionibacterium at all time points, suggesting their possible contributions to regulating host immunity and increasing the susceptibility to eczema. In the home environment of the control group, putative protective effect of an environmental microbe Planomicrobium (Planococcaceae family) was observed to be significantly higher than that in the eczema group. Network correlation analysis demonstrated inverse relationships between beneficial Planomicrobium and human-associated bacteria (Actinomyces, Anaerococcus, Finegoldia, Micrococcus, Prevotella and Propionibacterium). Exposure to natural environmental microbiota may be beneficial to modulate shed human-associated microbiota in an indoor environment

Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R2 increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase

Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R2 increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase