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The noncoding genome affects gene regulation and disease, yet we lack tools for rapid identification and manipulation of noncoding elements. We developed a CRISPR screen using ~18,000 single guide RNAs targeting >700 kilobases surrounding the genes NF1, NF2, and CUL3, which are involved in BRAF inhibitor resistance in melanoma. We find that noncoding locations that modulate drug resistance also harbor predictive hallmarks of noncoding function. With a subset of regions at the CUL3 locus, we demonstrate that engineered mutations alter transcription factor occupancy and long-range and local epigenetic environments, implicating these sites in gene regulation and chemotherapeutic resistance. Through our expansion of the potential of pooled CRISPR screens, We provide tools for genomic discovery and for elucidating biologically relevant mechanisms of gene regulation.

About 10% of people older than 70 years of age carry one or more mutations in their hematopoietic cells, and these persons have a higher relative risk of a hematologic cancer (by a factor of 11) and of death from cardiovascular disease (by a factor of 2.0 to 2.6). Cancer is thought to arise through the stepwise acquisition of genetic or epigenetic changes that transform a normal cell. 1 Hence, the existence of a premalignant state bearing only the initiating lesions may be detectable in some persons who have no other signs of disease. For example, multiple myeloma is frequently preceded by monoclonal gammopathy of unknown significance, 2 and chronic lymphocytic leukemia is commonly preceded by monoclonal B-cell lymphocytosis. 3 Several lines of evidence have suggested that clonal hematopoiesis resulting from an expansion of cells that harbor an initiating driver mutation might be an aspect of the aging hematopoietic system. Clonal hematopoiesis . . .

Polygenic risk prediction is a widely investigated topic because of its promising clinical applications. Genetic variants in functional regions of the genome are enriched for complex trait heritability. Here, we introduce a method for polygenic prediction, LDpred-funct, that leverages trait-specific functional priors to increase prediction accuracy. We fit priors using the recently developed baseline-LD model, including coding, conserved, regulatory, and LD-related annotations. We analytically estimate posterior mean causal effect sizes and then use cross-validation to regularize these estimates, improving prediction accuracy for sparse architectures. We applied LDpred-funct to predict 21 highly heritable traits in the UK Biobank (avg N = 373 K as training data). LDpred-funct attained a +4.6% relative improvement in average prediction accuracy (avg prediction R 2 = 0.144; highest R 2 = 0.413 for height) compared to SBayesR (the best method that does not incorporate functional information). For height, meta-analyzing training data from UK Biobank and 23andMe cohorts ( N = 1107 K) increased prediction R 2 to 0.431. Our results show that incorporating functional priors improves polygenic prediction accuracy, consistent with the functional architecture of complex traits. Incorporating functional information has shown promise for improving polygenic risk prediction of complex traits. Here, the authors describe polygenic prediction method LDpred-funct, and demonstrate its utility across 21 heritable traits in the UK Biobank.

We trained and validated risk prediction models for the three major types of skin cancer— basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma—on a cross-sectional and longitudinal dataset of 210,000 consented research participants who responded to an online survey covering personal and family history of skin cancer, skin susceptibility, and UV exposure. We developed a primary disease risk score (DRS) that combined all 32 identified genetic and non-genetic risk factors. Top percentile DRS was associated with an up to 13-fold increase (odds ratio per standard deviation increase >2.5) in the risk of developing skin cancer relative to the middle DRS percentile. To derive lifetime risk trajectories for the three skin cancers, we developed a second and age independent disease score, called DRSA. Using incident cases, we demonstrated that DRSA could be used in early detection programs for identifying high risk asymptotic individuals, and predicting when they are likely to develop skin cancer. High DRSA scores were not only associated with earlier disease diagnosis (by up to 14 years), but also with more severe and recurrent forms of skin cancer. Predicting who will develop skin cancer is difficult. Here, the authors from 23andMe developed a polygenic risk score for skin cancer based on a questionnaire and genetic data from more than 210,000 individuals and suggest that the score could be used in early screening programmes.

Major depressive disorder (MDD) and loneliness are phenotypically and genetically correlated with coronary artery disease (CAD), but whether these associations are explained by pleiotropic genetic variants or shared comorbidities is unclear. To tease apart these scenarios, we first assessed the medical morbidity pattern associated with genetic risk factors for MDD and loneliness by conducting a phenome-wide association study in 18,385 European-ancestry individuals in the Vanderbilt University Medical Center biobank, BioVU. Polygenic scores for MDD and loneliness were developed for each person using previously published meta-GWAS summary statistics, and were tested for association with 882 clinical diagnoses ascertained via billing codes in electronic health records. We discovered strong associations with heart disease diagnoses, and next embarked on targeted analyses of CAD in 3893 cases and 4197 controls. We found odds ratios of 1.11 (95% CI, 1.04–1.18; P 8.43 × 10−4) and 1.13 (95% CI, 1.07–1.20; P 4.51 × 10−6) per 1-SD increase in the polygenic scores for MDD and loneliness, respectively. Results were similar in patients without psychiatric symptoms, and the increased risk persisted in females even after adjusting for multiple conventional risk factors and a polygenic score for CAD. In a final sensitivity analysis, we statistically adjusted for the genetic correlation between MDD and loneliness and re-computed polygenic scores. The polygenic score unique to loneliness remained associated with CAD (OR 1.09, 95% CI 1.03–1.15; P 0.002), while the polygenic score unique to MDD did not (OR 1.00, 95% CI 0.95–1.06; P 0.97). Our replication sample was the Atherosclerosis Risk in Communities (ARIC) cohort of 7197 European-ancestry participants (1598 incident CAD cases). In ARIC, polygenic scores for MDD and loneliness were associated with hazard ratios of 1.07 (95% CI, 0.99–1.14; P = 0.07) and 1.07 (1.01–1.15; P = 0.03), respectively, and we replicated findings from the BioVU sensitivity analyses. We conclude that genetic risk factors for MDD and loneliness act pleiotropically to increase CAD risk in females.

Gene expression levels appear to be under pervasive stabilizing selection. Yet the genetic architecture underlying abundant gene expression diversity within and between populations remains elusive. Here, we investigated the role of dominance in the segregation of cis- and trans-regulation within and between populations. We used chromosome substitution lines of Drosophila melanogaster to show that (i) >70% of the genes that are differentially expressed between two homozygous lines are masked in the heterozygous, suggesting that one of the substituted chromosomes contains a recessive allele; (ii) such large masking is already obtained with heterozygous chromosomes originating from the same population, with the time of divergence between chromosomes in heterozygous lines making only a small but significant contribution to the masking of variation observed in homozygous lines; (iii) variation in gene expression due to trans-regulation is biased toward greater deviations from additivity because of recessive and dominant alleles, whereas variation due to cis-regulation shows higher additivity; and (iv) genetic divergence between second chromosomes is associated with increased cis-regulation, whereas the level of trans-regulation shows little increase over the time scale studied. Our results indicate that cis-acting alleles may be preferentially fixed by positive natural selection because of their higher additivity, and that the disruption of gene expression by recessive variation with pervasive trans-effects may be important for understanding gene expression variation within populations. We suggest that widespread regulatory effects of recessive low-frequency homozygous variation may provide a general mechanism mediating disease phenotypes and the genetic load of natural populations.

Arbuscular mycorrhizal fungi (AMF) are among the most abundant symbionts of plants, improving plant productivity and diversity. They are thought to mostly grow vegetatively, a trait assumed to limit adaptability. However, AMF can also harbor genetically different nuclei (nucleotypes). It has been shown that one AMF can produce genotypically novel offspring with proportions of different nucleotypes. We hypothesized that (1) AMF respond rapidly to a change of environment (plant host) through changes in the frequency of nucleotypes; (2) genotypically novel offspring exhibit different genetic responses to environmental change than the parent; and (3) genotypically novel offspring exhibit a wide range of phenotypic plasticity to a change of environment. We subjected AMF parents and offspring to a host shift. We observed rapid and large genotypic changes in all AMF lines that were not random. Genotypic and phenotypic responses were different among offspring and their parents. Even though growing vegetatively, AMF offspring display a broad range of genotypic and phenotypic changes in response to host shift. We conclude that AMF have the ability to rapidly produce variable progeny, increasing their probability to produce offspring with different fitness than their parents and, consequently, their potential adaptability to new environmental conditions. Such genotypic and phenotypic flexibility could be a fast alternative to sexual reproduction and is likely to be a key to the ecological success of AMF.

The “antidepressant efficacy” survey (AES) was deployed to > 50,000 23andMe, Inc. research participants to investigate the genetic basis of treatment-resistant depression (TRD) and non-treatment-resistant depression (NTRD). Genome-wide association studies (GWAS) were performed, including TRD vs. NTRD, selective serotonin reuptake inhibitor (SSRI) responders vs. non-responders, serotonin-norepinephrine reuptake inhibitor (SNRI) responders vs. non-responders, and norepinephrine-dopamine reuptake inhibitor responders vs. non-responders. Only the SSRI association reached the genome-wide significance threshold ( p  < 5 × 10 −8 ): one genomic region in RNF219-AS1 (SNP rs4884091, p  = 2.42 × 10 −8 , OR = 1.21); this association was also observed in the meta-analysis (13,130 responders vs. 6,610 non-responders) of AES and an earlier “antidepressant efficacy and side effects” survey (AESES) cohort. Meta-analysis for SNRI response phenotype derived from AES and AESES (4030 responders vs. 3049 non-responders) identified another genomic region (lead SNP rs4955665, p  = 1.62 × 10 −9 , OR = 1.25) in an intronic region of MECOM passing the genome-wide significance threshold. Meta-analysis for the TRD phenotype (31,068 NTRD vs 5,714 TRD) identified one additional genomic region (lead SNP rs150245813, p  = 8.07 × 10 −9 , OR = 0.80) in 10p11.1 passing the genome-wide significance threshold. A stronger association for rs150245813 was observed in current study ( p  = 7.35 × 10 −7 , OR = 0.79) than the previous study ( p  = 1.40 × 10 −3 , OR = 0.81), and for rs4955665, a stronger association in previous study ( p  = 1.21 × 10 −6 , OR = 1.27) than the current study ( p  = 2.64 × 10 −4 , OR = 1.21). In total, three novel loci associated with SSRI or SNRI (responders vs. non-responders), and NTRD vs TRD were identified; gene level association and gene set enrichment analyses implicate enrichment of genes involved in immune process.

Objective To develop a clinico‐genetic predictor of impulse control disorder (ICD) risk in Parkinson's disease (PD). Methods In 5770 individuals from three PD cohorts (the 23andMe, Inc.; the University of Pennsylvania [UPenn]; and the Parkinson's Progression Markers Initiative [PPMI]), we used a discovery‐replication strategy to develop a clinico‐genetic predictor for ICD risk. We first performed a Genomewide Association Study (GWAS) for ICDs anytime during PD in 5262 PD individuals from the 23andMe cohort. We then combined newly discovered ICD risk loci with 13 ICD risk loci previously reported in the literature to develop a model predicting ICD in a Training dataset (n = 339, from UPenn and PPMI cohorts). The model was tested in a non‐overlapping Test dataset (n = 169, from UPenn and PPMI cohorts) and used to derive a continuous measure, the ICD‐risk score (ICD‐RS), enriching for PD individuals with ICD (ICD+ PD). Results By GWAS, we discovered four new loci associated with ICD at p‐values of 4.9e‐07 to 1.3e‐06. Our best logistic regression model included seven clinical and two genetic variables, achieving an area under the receiver operating curve for ICD prediction of 0.75 in the Training and 0.72 in the Test dataset. The ICD‐RS separated groups of PD individuals with ICD prevalence of nearly 40% (highest risk quartile) versus 7% (lowest risk quartile). Interpretation In this multi‐cohort, international study, we developed an easily computed clinico‐genetic tool, the ICD‐RS, that substantially enriches for subgroups of PD at very high versus very low risk for ICD, enabling pharmacogenetic approaches to PD medication selection.

The author list was in the wrong order in the HTML version of the original article and in the HTML version of the original correction notice. This has been corrected to show the 23andMe Research Team as the fourth author and Abraham A. Palmer as the last author in both places.