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Using an ORF kinome screen in MCF-7 cells treated with the CDK4/6 inhibitor ribociclib plus fulvestrant, we identified FGFR1 as a mechanism of drug resistance. FGFR1-amplified/ER+ breast cancer cells and MCF-7 cells transduced with FGFR1 were resistant to fulvestrant ± ribociclib or palbociclib. This resistance was abrogated by treatment with the FGFR tyrosine kinase inhibitor (TKI) lucitanib. Addition of the FGFR TKI erdafitinib to palbociclib/fulvestrant induced complete responses of FGFR1-amplified/ER+ patient-derived-xenografts. Next generation sequencing of circulating tumor DNA (ctDNA) in 34 patients after progression on CDK4/6 inhibitors identified FGFR1/2 amplification or activating mutations in 14/34 (41%) post-progression specimens. Finally, ctDNA from patients enrolled in MONALEESA-2, the registration trial of ribociclib, showed that patients with FGFR1 amplification exhibited a shorter progression-free survival compared to patients with wild type FGFR1. Thus, we propose breast cancers with FGFR pathway alterations should be considered for trials using combinations of ER, CDK4/6 and FGFR antagonists. Era+ breast cancer patients often develop resistance to endocrine therapy. Here, the authors show that FGFR1 amplification is a resistance mechanism to CDK4/6 inhibitor and endocrine therapy and that combined treatment with FGFR, CDK4/6, and anti-estrogens is a potential therapeutic strategy in Era+ breast cancer tumors.

Like most complex phenotypes, exceptional longevity is thought to reflect a combined influence of environmental (e.g., lifestyle choices, where we live) and genetic factors. To explore the genetic contribution, we undertook a genome-wide association study of exceptional longevity in 801 centenarians (median age at death 104 years) and 914 genetically matched healthy controls. Using these data, we built a genetic model that includes 281 single nucleotide polymorphisms (SNPs) and discriminated between cases and controls of the discovery set with 89% sensitivity and specificity, and with 58% specificity and 60% sensitivity in an independent cohort of 341 controls and 253 genetically matched nonagenarians and centenarians (median age 100 years). Consistent with the hypothesis that the genetic contribution is largest with the oldest ages, the sensitivity of the model increased in the independent cohort with older and older ages (71% to classify subjects with an age at death>102 and 85% to classify subjects with an age at death>105). For further validation, we applied the model to an additional, unmatched 60 centenarians (median age 107 years) resulting in 78% sensitivity, and 2863 unmatched controls with 61% specificity. The 281 SNPs include the SNP rs2075650 in TOMM40/APOE that reached irrefutable genome wide significance (posterior probability of association = 1) and replicated in the independent cohort. Removal of this SNP from the model reduced the accuracy by only 1%. Further in-silico analysis suggests that 90% of centenarians can be grouped into clusters characterized by different \"genetic signatures\" of varying predictive values for exceptional longevity. The correlation between 3 signatures and 3 different life spans was replicated in the combined replication sets. The different signatures may help dissect this complex phenotype into sub-phenotypes of exceptional longevity.

The strong familiality of living to extreme ages suggests that human longevity is genetically regulated. The majority of genes found thus far to be associated with longevity primarily function in lipoprotein metabolism and insulin/IGF-1 signaling. There are likely many more genetic modifiers of human longevity that remain to be discovered. Here, we first show that 18 single nucleotide polymorphisms (SNPs) in the RNA editing genes ADARB1 and ADARB2 are associated with extreme old age in a U.S. based study of centenarians, the New England Centenarian Study. We describe replications of these findings in three independently conducted centenarian studies with different genetic backgrounds (Italian, Ashkenazi Jewish and Japanese) that collectively support an association of ADARB1 and ADARB2 with longevity. Some SNPs in ADARB2 replicate consistently in the four populations and suggest a strong effect that is independent of the different genetic backgrounds and environments. To evaluate the functional association of these genes with lifespan, we demonstrate that inactivation of their orthologues adr-1 and adr-2 in C. elegans reduces median survival by 50%. We further demonstrate that inactivation of the argonaute gene, rde-1, a critical regulator of RNA interference, completely restores lifespan to normal levels in the context of adr-1 and adr-2 loss of function. Our results suggest that RNA editors may be an important regulator of aging in humans and that, when evaluated in C. elegans, this pathway may interact with the RNA interference machinery to regulate lifespan.

Serum bilirubin levels have been associated with polymorphisms in the UGT1A1 promoter in normal populations and in patients with hemolytic anemias, including sickle cell anemia. When hemolysis occurs circulating heme increases, leading to elevated bilirubin levels and an increased incidence of cholelithiasis. We performed the first genome-wide association study (GWAS) of bilirubin levels and cholelithiasis risk in a discovery cohort of 1,117 sickle cell anemia patients. We found 15 single nucleotide polymorphisms (SNPs) associated with total bilirubin levels at the genome-wide significance level (p value <5 × 10(-8)). SNPs in UGT1A1, UGT1A3, UGT1A6, UGT1A8 and UGT1A10, different isoforms within the UGT1A locus, were identified (most significant rs887829, p = 9.08 × 10(-25)). All of these associations were validated in 4 independent sets of sickle cell anemia patients. We tested the association of the 15 SNPs with cholelithiasis in the discovery cohort and found a significant association (most significant p value 1.15 × 10(-4)). These results confirm that the UGT1A region is the major regulator of bilirubin metabolism in African Americans with sickle cell anemia, similar to what is observed in other ethnicities.

Key Points Genome-wide association studies have identified many novel loci for hundreds of traits. Interestingly, numerous genetic loci have been associated with multiple seemingly distinct traits. These cross-phenotype (CP) associations highlight the relevance of pleiotropy in human disease. There is substantial evidence for CP associations in contemporary gene-mapping studies. Different types of pleiotropy (biological, mediated and spurious pleiotropy) can underlie a CP association. Various analytical approaches have been devised for detecting CP associations, especially methods that are based on summary statistics as opposed to individual-level data. Different methods have relative advantages and disadvantages and are distinguished by their underlying algorithms and by the types of phenotype data that they handle. Study design considerations are crucial for minimizing the identification of spurious CP associations. CP associations can highlight shared biological pathways and, when associated with different diseases, have clinical implications for diagnosis, counselling and treatment. Modern genomic studies are revealing widespread associations between single genetic variants and multiple distinct traits, including diseases. This Review discusses the biological underpinnings of such pleiotropy and the available bioinformatic tools for the detection and characterization of these effects, as well as the implications for understanding human disease. Genome-wide association studies have identified many variants that each affects multiple traits, particularly across autoimmune diseases, cancers and neuropsychiatric disorders, suggesting that pleiotropic effects on human complex traits may be widespread. However, systematic detection of such effects is challenging and requires new methodologies and frameworks for interpreting cross-phenotype results. In this Review, we discuss the evidence for pleiotropy in contemporary genetic mapping studies, new and established analytical approaches to identifying pleiotropic effects, sources of spurious cross-phenotype effects and study design considerations. We also outline the molecular and clinical implications of such findings and discuss future directions of research.

Schizophrenia is a common disease with a complex aetiology, probably involving multiple and heterogeneous genetic factors. Here, by analysing the exome sequences of 2,536 schizophrenia cases and 2,543 controls, we demonstrate a polygenic burden primarily arising from rare (less than 1 in 10,000), disruptive mutations distributed across many genes. Particularly enriched gene sets include the voltage-gated calcium ion channel and the signalling complex formed by the activity-regulated cytoskeleton-associated scaffold protein (ARC) of the postsynaptic density, sets previously implicated by genome-wide association and copy-number variation studies. Similar to reports in autism, targets of the fragile X mental retardation protein (FMRP, product of FMR1 ) are enriched for case mutations. No individual gene-based test achieves significance after correction for multiple testing and we do not detect any alleles of moderately low frequency (approximately 0.5 to 1 per cent) and moderately large effect. Taken together, these data suggest that population-based exome sequencing can discover risk alleles and complements established gene-mapping paradigms in neuropsychiatric disease. Exome sequence analysis of more than 5,000 schizophrenia cases and controls identifies a polygenic burden primarily arising from rare, disruptive mutations distributed across many genes, among which are those encoding voltage-gated calcium ion channels and the signalling complex formed by the ARC protein of the postsynaptic density; as in autism, mutations were also found in homologues of known targets of the fragile X mental retardation protein. Pathogenic mechanisms in schizophrenia Two major sequencing studies of the exome — the protein-coding portion of the genome — in schizophrenia sufferers and their relatives are published in this issue of Nature . Together they provide strong pointers to specific pathogenic mechanisms that disrupt the glutamatergic synapses in schizophrenia. In particular, mutations that influence the action of the scaffold protein ARC (activity-regulated cytoskeleton-associated protein) are prominently involved, as are mutations in targets of the fragile X mental retardation protein (FMRP). Defects in FMRP have previously been shown to be associated with autism spectrum disorders.

Healthy aging is thought to reflect the combined influence of environmental factors (lifestyle choices) and genetic factors. To explore the genetic contribution, we undertook a genome-wide association study of exceptional longevity (EL) in 1055 centenarians and 1267 controls. Using these data, we built a genetic model that includes 150 single-nucleotide polymorphisms (SNPs) and found that it could predict EL with 77% accuracy in an independent set of centenarians and controls. Further in silico analysis revealed that 90% of centenarians can be grouped into 19 clusters characterized by different combinations of SNP genotypes—or genetic signatures—of varying predictive value. The different signatures, which attest to the genetic complexity of EL, correlated with differences in the prevalence and age of onset of age-associated diseases (e.g., dementia, hypertension, and cardiovascular disease) and may help dissect this complex phenotype into subphenotypes of healthy aging.

The genetic architecture of post-traumatic stress disorder (PTSD) remains poorly understood with the vast majority of genetic association studies reporting on single candidate genes. We conducted a large genetic study in trauma-exposed European-American women (N=2538; 845 PTSD cases, 1693 controls) by testing 3742 SNPs across more than 300 genes and conducting polygenic analyses using results from the Psychiatric Genome-Wide Association Studies Consortium (PGC). We tested the association between each SNP and two measures of PTSD, a severity score and diagnosis. We found a significant association between PTSD (diagnosis) and SNPs (top SNP: rs363276, odds ratio (OR)=1.4, p=2.1E-05) in SLC18A2 (vesicular monoamine transporter 2). A haplotype analysis of 9 SNPs in SLC18A2, including rs363276, identified a risk haplotype (CGGCGGAAG, p=0.0046), and the same risk haplotype was associated with PTSD in an independent cohort of trauma-exposed African-Americans (p=0.049; N=748, men and women). SLC18A2 is involved in transporting monoamines to synaptic vesicles and has been implicated in a number of neuropsychiatric disorders including major depression. Eight genes previously associated with PTSD had SNPs with nominally significant associations (p<0.05). The polygenic analyses suggested that there are SNPs in common between PTSD severity and bipolar disorder. Our data are consistent with a genetic architecture for PTSD that is highly polygenic, influenced by numerous SNPs with weak effects, and may overlap with mood disorders. Genome-wide studies with very large samples sizes are needed to detect these types of effects.

Schizophrenia is a common disease with a complex aetiology, probably involving multiple and heterogeneous genetic factors. Here, by analysing the exome sequences of 2,536 schizophrenia cases and 2,543 controls, we demonstrate a polygenic burden primarily arising from rare (less than 1 in 10,000), disruptive mutations distributed across many genes. Particularly enriched gene sets include the voltage-gated calcium ion channel and the signalling complex formed by the activity-regulated cytoskeleton-associated scaffold protein (ARC) of the postsynaptic density, sets previously implicated by genome-wide association and copy-number variation studies. Similar to reports in autism, targets of the fragile X mental retardation protein (FMRP, product of FMR1) are enriched for case mutations. No individual gene-based test achieves significance after correction for multiple testing and we do not detect any alleles of moderately low frequency (approximately 0.5 to 1 per cent) and moderately large effect. Taken together, these data suggest that population-based exome sequencing can discover risk alleles and complements established gene-mapping paradigms in neuropsychiatric disease.

Abstract Background: Population stratification can cause spurious associations in a genome-wide association study (GWAS), and occurs when differences in allele frequencies of single nucleotide polymorphisms (SNPs) are due to ancestral differences between cases and controls rather than the trait of interest. Principal components analysis (PCA) is the established approach to detect population substructure using genome-wide data and to adjust the genetic association for stratification by including the top principal components in the analysis. An alternative solution is genetic matching of cases and controls that requires, however, well defined population strata for appropriate selection of cases and controls. Results: We developed a novel algorithm to cluster individuals into groups with similar ancestral backgrounds based on the principal components computed by PCA. We demonstrate the effectiveness of our algorithm in real and simulated data, and show that matching cases and controls using the clusters assigned by the algorithm substantially reduces population stratification bias. Through simulation we show that the power of our method is higher than adjustment for PCs in certain situations. Conclusions: In addition to reducing population stratification bias and improving power, matching creates a clean dataset free of population stratification which can then be used to build prediction models without including variables to adjust for ancestry. The cluster assignments also allow for the estimation of genetic heterogeneity by examining cluster specific effects.