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Hugh Watkins and colleagues meta-analyze data from the UK Biobank along with recent genome-wide association studies for coronary artery disease. They identify 13 new loci that were genome-wide significant and 243 loci at a 5% false discovery rate. Genome-wide association studies (GWAS) in coronary artery disease (CAD) had identified 66 loci at 'genome-wide significance' ( P < 5 × 10 −8 ) at the time of this analysis, but a much larger number of putative loci at a false discovery rate (FDR) of 5% (refs. 1 , 2 , 3 , 4 ). Here we leverage an interim release of UK Biobank (UKBB) data to evaluate the validity of the FDR approach. We tested a CAD phenotype inclusive of angina (SOFT; n cases = 10,801) as well as a stricter definition without angina (HARD; n cases = 6,482) and selected cases with the former phenotype to conduct a meta-analysis using the two most recent CAD GWAS 2 , 3 . This approach identified 13 new loci at genome-wide significance, 12 of which were on our previous list of loci meeting the 5% FDR threshold 2 , thus providing strong support that the remaining loci identified by FDR represent genuine signals. The 304 independent variants associated at 5% FDR in this study explain 21.2% of CAD heritability and identify 243 loci that implicate pathways in blood vessel morphogenesis as well as lipid metabolism, nitric oxide signaling and inflammation.

To improve the 'personalized-medicine' approach to the treatment of depression, we need to identify biomarkers that, assessed before starting treatment, predict future response to antidepressants ('predictors'), as well as biomarkers that are targeted by antidepressants and change longitudinally during the treatment ('targets'). In this study, we tested the leukocyte mRNA expression levels of genes belonging to glucocorticoid receptor (GR) function (FKBP-4, FKBP-5, and GR), inflammation (interleukin (IL)-1α, IL-1β, IL-4, IL-6, IL-7, IL-8, IL-10, macrophage inhibiting factor (MIF), and tumor necrosis factor (TNF)-α), and neuroplasticity (brain-derived neurotrophic factor (BDNF), p11 and VGF), in healthy controls (n=34) and depressed patients (n=74), before and after 8 weeks of treatment with escitalopram or nortriptyline, as part of the Genome-based Therapeutic Drugs for Depression study. Non-responders had higher baseline mRNA levels of IL-1β (+33%), MIF (+48%), and TNF-α (+39%). Antidepressants reduced the levels of IL-1β (-6%) and MIF (-24%), and increased the levels of GR (+5%) and p11 (+8%), but these changes were not associated with treatment response. In contrast, successful antidepressant response was associated with a reduction in the levels of IL-6 (-9%) and of FKBP5 (-11%), and with an increase in the levels of BDNF (+48%) and VGF (+20%)-that is, response was associated with changes in genes that did not predict, at the baseline, the response. Our findings indicate a dissociation between 'predictors' and 'targets' of antidepressant responders. Indeed, while higher levels of proinflammatory cytokines predict lack of future response to antidepressants, changes in inflammation associated with antidepressant response are not reflected by all cytokines at the same time. In contrast, modulation of the GR complex and of neuroplasticity is needed to observe a therapeutic antidepressant effect.

More accurate and precise phenotyping strategies are necessary to empower high-resolution linkage mapping and genome-wide association studies and for training genomic selection models in plant improvement. Within this framework, the objective of modern phenotyping is to increase the accuracy, precision and throughput of phenotypic estimation at all levels of biological organization while reducing costs and minimizing labor through automation, remote sensing, improved data integration and experimental design. Much like the efforts to optimize genotyping during the 1980s and 1990s, designing effective phenotyping initiatives today requires multi-faceted collaborations between biologists, computer scientists, statisticians and engineers. Robust phenotyping systems are needed to characterize the full suite of genetic factors that contribute to quantitative phenotypic variation across cells, organs and tissues, developmental stages, years, environments, species and research programs. Next-generation phenotyping generates significantly more data than previously and requires novel data management, access and storage systems, increased use of ontologies to facilitate data integration, and new statistical tools for enhancing experimental design and extracting biologically meaningful signal from environmental and experimental noise. To ensure relevance, the implementation of efficient and informative phenotyping experiments also requires familiarity with diverse germplasm resources, population structures, and target populations of environments. Today, phenotyping is quickly emerging as the major operational bottleneck limiting the power of genetic analysis and genomic prediction. The challenge for the next generation of quantitative geneticists and plant breeders is not only to understand the genetic basis of complex trait variation, but also to use that knowledge to efficiently synthesize twenty-first century crop varieties.

To efficiently transform genetic associations into drug targets requires evidence that a particular gene, and its encoded protein, contribute causally to a disease. To achieve this, we employ a three-step proteome-by-phenome Mendelian Randomization (MR) approach. In step one, 154 protein quantitative trait loci (pQTLs) were identified and independently replicated. From these pQTLs, 64 replicated locally-acting variants were used as instrumental variables for proteome-by-phenome MR across 846 traits (step two). When its assumptions are met, proteome-by-phenome MR, is equivalent to simultaneously running many randomized controlled trials. Step 2 yielded 38 proteins that significantly predicted variation in traits and diseases in 509 instances. Step 3 revealed that amongst the 271 instances from GeneAtlas (UK Biobank), 77 showed little evidence of pleiotropy (HEIDI), and 92 evidence of colocalization (eCAVIAR). Results were wide ranging: including, for example, new evidence for a causal role of tyrosine-protein phosphatase non-receptor type substrate 1 (SHPS1; SIRPA) in schizophrenia, and a new finding that intestinal fatty acid binding protein (FABP2) abundance contributes to the pathogenesis of cardiovascular disease. We also demonstrated confirmatory evidence for the causal role of four further proteins (FGF5, IL6R, LPL, LTA) in cardiovascular disease risk.

Selection and prioritization of phenotype-centric variants remains a challenging part of variant analysis and interpretation in clinical exome sequencing. Phenotype-driven shortlisting of patient-specific gene lists can avoid missed diagnosis. Here, we analyzed the relevance of using primary Human Phenotype Ontology identifiers (HPO IDs) in prioritizing Mendelian disease genes across 30 in-house, 10 previously reported, and 10 recently published cases using three popular web-based gene prioritization tools (OMIMExplorer, VarElect & Phenolyzer). We assessed partial HPO-based gene prioritization using randomly chosen and top 10%, 30%, and 50% HPO IDs based on information content and found high variance within rank ratios across the former vs the latter. This signified that randomly selected less-specific HPO IDs for a given disease phenotype performed poorly by ranking probe gene farther away from the top rank. In contrast, the use of top 10%, 30%, and 50% HPO IDs individually could rank the probe gene among the top 1% in the ranked list of genes that was equivalent to the results when the full list of HPO IDs were used. Hence, we conclude that use of just the top 10% of HPO IDs chosen based on information content is sufficient for ranking the probe gene at top position. Our findings provide practical guidance for utilizing structured phenotype semantics and web-based gene-ranking tools to aid in identifying known as well unknown candidate gene associations in Mendelian disorders.

Genetics evolved as a field of science after 1900 with new theories being derived from experiments obtained in fruit flies, bacteria, and viruses. This personal account suggests that the origins of human genetics can best be traced to the years 1949 to 1959. Several genetic scientific advances in genetics in 1949 yielded results directly relating to humans for the first time, except for a few earlier observations. In 1949 the first textbook of human genetics was published, the American Journal of Human Genetics was founded, and in the previous year the American Society of Human Genetics. In 1940 in Britain a textbook entitled Introduction to Medical Genetics served as a foundation for introducing genetic aspects into medicine. The introduction of new methods for analyzing chromosomes and new biochemical assays using cultured cells in 1959 and subsequent years revealed that many human diseases, including cancer, have genetic causes. It became possible to arrive at a precise cause-related genetic diagnosis. As a result the risk of occurrence or re-occurrence of a disease within a family could be assessed correctly. Genetic counseling as a new concept became a basis for improved patient care. Taken together the advances in medically orientated genetic research and patient care since 1949 have resulted in human genetics being both, a basic medical and a basic biological science. Prior to 1949 genetics was not generally viewed in a medical context. Although monogenic human diseases were recognized in 1902, their occurrence and distribution were considered mainly at the population level.

As high-throughput sequencing is increasingly applied to the molecular diagnosis of rare Mendelian disorders, a large number of patients with diverse phenotypes have their genetic and phenotypic data pooled together to uncover new gene-phenotype relations. We introduce Phenogenon, a statistical tool that combines, Human Phenotype Ontology (HPO) annotated patient phenotypes, gnomAD allele population frequency, and Combined Annotation Dependent Depletion (CADD) score for variant pathogenicity, in order to jointly predict the mode of inheritance and gene-phenotype associations. We ran Phenogenon on our cohort of 3,290 patients who had undergone whole exome sequencing. Among the top associations, we recapitulated previously known, such as \"SRD5A3-Abnormal full-field electroretinogram-recessive\" and \"GRHL2 -Nail dystrophy-recessive\", and discovered one potentially novel, \"RRAGA-Abnormality of the skin-dominant\". We also developed an interactive web interface available at https://phenogenon.phenopolis.org to visualise and explore the results.

Recall by genotype (RbG) studies aim to better understand the phenotypes that correspond to genetic variants of interest, by recruiting carriers of such variants for further phenotyping. RbG approaches pose major ethical and legal challenges related to the disclosure of possibly unwanted genetic information. The Cooperative Health Research in South Tyrol (CHRIS) study is a longitudinal cohort study based in South Tyrol, Italy. Demand has grown for CHRIS study participants to be enrolled in RbG studies, thus making the design of a suitable ethical framework a pressing need. We here report upon the design of a pilot RbG study conducted with CHRIS study participants. By reviewing the literature and by consulting relevant stakeholders (CHRIS participants, clinical geneticists, ethics board, GPs), we identified key ethical issues in RbG approaches (e.g. complexity of the context, communication of genetic results, measures to further protect participants). The design of the pilot was based on a feasibility assessment, the selection of a suitable test case within the ProtectMove Research Unit on reduced penetrance of hereditary movement disorders, and the development of appropriate recruitment and communication strategies. An empirical study was embedded in the pilot study with the aim of understanding participants’ views on RbG. Our experience with the pilot study in CHRIS allowed us to contribute to the development of best practices and policies for RbG studies by drawing recommendations: addressing the possibility of RbG in the original consent, implementing tailored communication strategies, engaging stakeholders, designing embedded empirical studies, and sharing research experiences and methodology.

Brendan Bulik-Sullivan, Benjamin Neale, Hilary Finucane, Alkes Price and colleagues introduce a new technique for estimating genetic correlation that requires only genome-wide association summary statistics and that is not biased by sample overlap. Using this method, they find genetic correlations between anorexia nervosa and schizophrenia, and between educational attainment and autism spectrum disorder. Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual-level genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique—cross-trait LD Score regression—for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity, and educational attainment and several diseases. These results highlight the power of genome-wide analyses, as there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.