Cystic Fibrosis (CF) exhibits morbidity in several organs, including progressive lung disease in all patients and intestinal obstruction at birth (meconium ileus) in ~15%. Individuals with the same causal CFTR mutations show variable disease presentation which is partly attributed to modifier genes. With >6,500 participants from the International CF Gene Modifier Consortium, genome-wide association investigation identified a new modifier locus for meconium ileus encompassing ATP12A on chromosome 13 (min p = 3.83x10 -10 ); replicated loci encompassing SLC6A14 on chromosome X and SLC26A9 on chromosome 1, (min p<2.2x10 -16 , 2.81x10 −11 , respectively); and replicated a suggestive locus on chromosome 7 near PRSS1 (min p = 2.55x10 -7 ). PRSS1 is exclusively expressed in the exocrine pancreas and was previously associated with non-CF pancreatitis with functional characterization demonstrating impact on PRSS1 gene expression. We thus asked whether the other meconium ileus modifier loci impact gene expression and in which organ. We developed and applied a colocalization framework called the Simple Sum (SS) that integrates regulatory and genetic association information, and also contrasts colocalization evidence across tissues or genes. The associated modifier loci colocalized with expression quantitative trait loci (eQTLs) for ATP12A (p = 3.35x10 -8 ), SLC6A14 (p = 1.12x10 -10 ) and SLC26A9 (p = 4.48x10 -5 ) in the pancreas, even though meconium ileus manifests in the intestine. The meconium ileus susceptibility locus on chromosome X appeared shifted in location from a previously identified locus for CF lung disease severity. Using the SS we integrated the lung disease association locus with eQTLs from nasal epithelia of 63 CF participants and demonstrated evidence of colocalization with airway-specific regulation of SLC6A14 (p = 2.3x10 -4 ). Cystic Fibrosis is realizing the promise of personalized medicine, and identification of the contributing organ and understanding of tissue specificity for a gene modifier is essential for the next phase of personalizing therapeutic strategies.

Cystic fibrosis is realizing the promise of personalized medicine. Recent advances in drug development that target the causal CFTR directly result in lung function improvement, but variability in response is demanding better prediction of outcomes to improve management decisions. The genetic modifier SLC26A9 contributes to disease severity in the CF pancreas and intestine at birth and here we assess its relationship with disease severity and therapeutic response in the airways. SLC26A9 association with lung disease was assessed in individuals from the Canadian and French CF Gene Modifier consortia with CFTR-gating mutations and in those homozygous for the common Phe508del mutation. Variability in response to a CFTR-directed therapy attributed to SLC26A9 genotype was assessed in Canadian patients with gating mutations. A primary airway model system determined if SLC26A9 shows modification of Phe508del CFTR function upon treatment with a CFTR corrector.In those with gating mutations that retain cell surface-localized CFTR we show that SLC26A9 modifies lung function while this is not the case in individuals homozygous for Phe508del where cell surface expression is lacking. Treatment response to ivacaftor, which aims to improve CFTR-channel opening probability in patients with gating mutations, shows substantial variability in response, 28% of which can be explained by rs7512462 in SLC26A9 (P = 0.0006). When homozygous Phe508del primary bronchial cells are treated to restore surface CFTR, SLC26A9 likewise modifies treatment response (P = 0.02). Our findings indicate that SLC26A9 airway modification requires CFTR at the cell surface, and that a common variant in SLC26A9 may predict response to CFTR-directed therapeutics.

Background Population‐based samples with valid, quantitative and genetically informative trait measures of psychopathology could be a powerful complement to case/control genetic designs. We report the convergent and predictive validity of the parent‐ and self‐report versions of the Strengths and Weaknesses of ADHD Symptoms and Normal Behavior Rating Scale (SWAN). We tested if SWAN scores were associated with ADHD diagnosis, ADHD polygenic risk, as well as traits and polygenic risk for disorders that co‐occur with ADHD: anxiety and obsessive‐compulsive disorder (OCD). Methods We collected parent‐ and self‐report SWAN scores in a sample of 15,560 children and adolescents (6–17 years) recruited at a science museum (Spit for Science sample). We established age and sex norms for the SWAN. Sensitivity‐specificity analyses determined SWAN cut‐points that discriminated those with and without a reported ADHD diagnosis. These cut‐points were validated in a clinic sample (266 ADHD cases; 36 controls). Convergent validity was established using the Conners’ parent‐ and self‐report scales. Using Spit for Science participants with genome‐wide data (n = 5,154), we tested if low, medium and high SWAN scores were associated with polygenic risk for ADHD, OCD and anxiety disorders. Results Parent‐ and self‐report SWAN scores showed high convergent validity with Conners’ scales and distinguished ADHD participants with high sensitivity and specificity in the Spit for Science sample. In a clinic sample, the Spit for Science cut‐points discriminated ADHD cases from controls with a sensitivity of 84% and specificity of 92%. High SWAN scores and scores above the Spit for Science cut‐points were significantly associated with polygenic risk for ADHD. SWAN scores were not associated with polygenic risk for OCD or anxiety disorders. Conclusions Our study supports the validity of the parent‐ and self‐report SWAN scales and their potential in ADHD population‐based genetic research.

Using a novel trait-based measure, we examined genetic variants associated with obsessive-compulsive (OC) traits and tested whether OC traits and obsessive-compulsive disorder (OCD) shared genetic risk. We conducted a genome-wide association analysis (GWAS) of OC traits using the Toronto Obsessive-Compulsive Scale (TOCS) in 5018 unrelated Caucasian children and adolescents from the community (Spit for Science sample). We tested the hypothesis that genetic variants associated with OC traits from the community would be associated with clinical OCD using a meta-analysis of all currently available OCD cases. Shared genetic risk was examined between OC traits and OCD in the respective samples using polygenic risk score and genetic correlation analyses. A locus tagged by rs7856850 in an intron of PTPRD (protein tyrosine phosphatase δ) was significantly associated with OC traits at the genome-wide significance level (p = 2.48 × 10−8). rs7856850 was also associated with OCD in a meta-analysis of OCD case/control genome-wide datasets (p = 0.0069). The direction of effect was the same as in the community sample. Polygenic risk scores from OC traits were significantly associated with OCD in case/control datasets and vice versa (p’s < 0.01). OC traits were highly, but not significantly, genetically correlated with OCD (rg = 0.71, p = 0.062). We report the first validated genome-wide significant variant for OC traits in PTPRD, downstream of the most significant locus in a previous OCD GWAS. OC traits measured in the community sample shared genetic risk with OCD case/control status. Our results demonstrate the feasibility and power of using trait-based approaches in community samples for genetic discovery.

Does genotype imputation with public reference panels identify variants contributing to disease? Genotype imputation using the 1000 Genomes Project (1KG; 2504 individuals) displayed poor coverage at the causal cystic fibrosis (CF) transmembrane conductance regulator (CFTR) locus for the International CF Gene Modifier Consortium. Imputation with the larger Haplotype Reference Consortium (HRC; 32,470 individuals) displayed improved coverage but low sensitivity of variants clinically relevant for CF. A hybrid reference that combined whole genome sequencing (WGS) from 101 CF individuals with the 1KG imputed a greater number of single-nucleotide variants (SNVs) that would be analyzed in a genetic association study (r2 ≥ 0.3 and MAF ≥ 0.5%) than imputation with the HRC, while the HRC excelled in the lower frequency spectrum. Using the 1KG or HRC as reference panels missed the most common CF-causing variants or displayed low imputation accuracy. Designs that incorporate population-specific WGS can improve imputation accuracy at disease-specific loci, while imputation using public data sets can omit disease-relevant genotypes.

This study examined the genetic correlates of obsessive-compulsive (OC) traits and their shared genetic risks with obsessive-compulsive disorder (OCD). We conducted genome-wide association analyses on OC traits in 5018 unrelated Caucasian children and adolescents. Overall OC traits and trait dimensions (e.g., cleaning/contamination) were measured with the Toronto Obsessive-Compulsive scale (TOCS). One locus tagged by rs7856850 in an intron of PTPRD (protein tyrosine phosphatase δ) was associated with OC traits at the genome-wide significance level (p=2.48x10 -8 ). A variant in GRID2 was significantly associated with only the symmetry/ordering dimension (p=3.2x10 -8 ). We tested the role of central nervous system (CNS) and glutamate gene-sets using hypothesis-driven methods. A stratified False Discovery Rate found OC traits were associated with SNPs in three CNS genes: NPAS2 (p=7.8x10 -7 ), GRID2 (p=1.6x10 -6 ) and SH3GL2 (p=1.9x10 -7 ). The combined effect of neither the CNS development nor the glutamate gene-set were associated with OC traits using the competitive gene-set test implemented with MAGMA. We replicated the SNP in PTPRD in a meta-analysis of three independent OCD case/control genome-wide datasets (p=0.0069, cases=3384, controls=8363). Polygenic risk from OC traits was significantly associated with OCD in a sample of childhood-onset OCD and vice versa (p's<0.01). OC traits were highly but not significantly correlated with OCD (rg=0.83, p=0.07). We report the first replicated genome-wide significant variant for OCD traits. Our results indicate that OC traits in the general population share genetic risk with OCD in independent samples. This study demonstrates the feasibility and power of using trait-based approaches in community samples in psychiatric genomics.

Medical research increasingly includes high‐dimensional regression modeling with a need for error‐in‐variables methods. The Convex Conditioned Lasso (CoCoLasso) utilizes a reformulated Lasso objective function and an error‐corrected cross‐validation to enable error‐in‐variables regression, but requires heavy computations. Here, we develop a Block coordinate Descent Convex Conditioned Lasso (BDCoCoLasso) algorithm for modeling high‐dimensional data that are only partially corrupted by measurement error. This algorithm separately optimizes the estimation of the uncorrupted and corrupted features in an iterative manner to reduce computational cost, with a specially calibrated formulation of cross‐validation error. Through simulations, we show that the BDCoCoLasso algorithm successfully copes with much larger feature sets than CoCoLasso, and as expected, outperforms the naïve Lasso with enhanced estimation accuracy and consistency, as the intensity and complexity of measurement errors increase. Also, a new smoothly clipped absolute deviation penalization option is added that may be appropriate for some data sets. We apply the BDCoCoLasso algorithm to data selected from the UK Biobank. We develop and showcase the utility of covariate‐adjusted genetic risk scores for body mass index, bone mineral density, and lifespan. We demonstrate that by leveraging more information than the naïve Lasso in partially corrupted data, the BDCoCoLasso may achieve higher prediction accuracy. These innovations, together with an R package, BDCoCoLasso, make error‐in‐variables adjustments more accessible for high‐dimensional data sets. We posit the BDCoCoLasso algorithm has the potential to be widely applied in various fields, including genomics‐facilitated personalized medicine research.

BackgroundValid and genetically-informative trait measures of psychopathology collected in the general population would provide a powerful complement to case/control genetic designs. We report the convergent, predictive and discriminant validity of the parent- and the self-report versions of the Strengths and Weaknesses of ADHD Symptoms and Normal Behavior Rating Scale (SWAN) for attention-deficit/hyperactivity disorder (ADHD) traits. We tested if SWAN ADHD scores were associated with ADHD diagnosis, ADHD polygenic risk, as well as with traits and polygenic risk for co-occurring disorders such as anxiety and obsessive-compulsive disorder (OCD).MethodsWe collected parent- and self-report SWAN scores in a community sample (n=15,560; 6-18 years of age) and created norms. Sensitivity-specificity analyses determined SWAN cut-points that discriminated those with a community ADHD diagnosis (n=972) from those without a community diagnosis. We validated cut-points from the community sample in a clinical sample (266 ADHD cases; 36 controls). We tested if SWAN scores were associated with anxiety and obsessive-compulsive (OC) traits and polygenic risk for ADHD, OCD and anxiety disorders.ResultsBoth the parent- and the self-report SWAN measures showed high convergent validity with established ADHD measures and distinguished ADHD participants with high sensitivity and specificity in the community sample. Cut-points established in the community sample discriminated ADHD clinic cases from controls with a sensitivity of 86% and specificity of 94%. High parent- and self-report SWAN scores and scores above the community-based cut-points were associated with polygenic risk for ADHD. High ADHD traits were associated with high anxiety traits, but not OC traits. SWAN scores were not associated with OCD or anxiety disorder polygenic risk.ConclusionThe parent- and self-report SWAN are potentially useful in genetic research because they predict ADHD diagnoses and are associated with ADHD polygenic risk.