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The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes. Identifying which genes and pathways explain genetic associations is challenging. Here, the authors present DEPICT, a tool for gene prioritization, pathway analysis and tissue/cell-type enrichment analysis that can be used to generate testable hypotheses from genetic association studies.

Current genome-wide association studies do not yet capture sufficient diversity in populations and scope of phenotypes. To expand an atlas of genetic associations in non-European populations, we conducted 220 deep-phenotype genome-wide association studies (diseases, biomarkers and medication usage) in BioBank Japan ( n  = 179,000), by incorporating past medical history and text-mining of electronic medical records. Meta-analyses with the UK Biobank and FinnGen ( n total  = 628,000) identified ~5,000 new loci, which improved the resolution of the genomic map of human traits. This atlas elucidated the landscape of pleiotropy as represented by the major histocompatibility complex locus, where we conducted HLA fine-mapping. Finally, we performed statistical decomposition of matrices of phenome-wide summary statistics, and identified latent genetic components, which pinpointed responsible variants and biological mechanisms underlying current disease classifications across populations. The decomposed components enabled genetically informed subtyping of similar diseases (for example, allergic diseases). Our study suggests a potential avenue for hypothesis-free re-investigation of human diseases through genetics. Genome-wide analyses in BioBank Japan, UK Biobank and FinnGen identify ~5,000 new loci associated with 220 human traits. Statistical decomposition of matrices of phenome-wide summary statistics further highlights variants underpinning diseases across populations.

While polygenic risk scores (PRSs) are poised to be translated into clinical practice through prediction of inborn health risks 1 , a strategy to utilize genetics to prioritize modifiable risk factors driving heath outcome is warranted 2 . To this end, we investigated the association of the genetic susceptibility to complex traits with human lifespan in collaboration with three worldwide biobanks ( n total  = 675,898; BioBank Japan ( n  = 179,066), UK Biobank ( n  = 361,194) and FinnGen ( n  = 135,638)). In contrast to observational studies, in which discerning the cause-and-effect can be difficult, PRSs could help to identify the driver biomarkers affecting human lifespan. A high systolic blood pressure PRS was trans-ethnically associated with a shorter lifespan (hazard ratio = 1.03[1.02–1.04], P meta  = 3.9 × 10 −13 ) and parental lifespan (hazard ratio = 1.06[1.06–1.07], P  = 2.0 × 10 −86 ). The obesity PRS showed distinct effects on lifespan in Japanese and European individuals ( P heterogeneity  = 9.5 × 10 −8 for BMI). The causal effect of blood pressure and obesity on lifespan was further supported by Mendelian randomization studies. Beyond genotype–phenotype associations, our trans-biobank study offers a new value of PRSs in prioritization of risk factors that could be potential targets of medical treatment to improve population health. Cross-biobank analysis reveals that polygenic risk scores (PRS) for hypertension and obesity are associated with shorter lifespan, serving as a proof-of-principle that PRS could pinpoint causal risk factors that affect long-term health outcomes.

Inflammatory and infectious upper respiratory diseases (ICD-10: J30-J39), such as diseases of the sinonasal tract, pharynx and larynx, are growing health problems yet their genomic similarity is not known. We analyze genome-wide association to eight upper respiratory diseases (61,195 cases) among 260,405 FinnGen participants, meta-analyzing diseases in four groups based on an underlying genetic correlation structure. Aiming to understand which genetic loci contribute to susceptibility to upper respiratory diseases in general and its subtypes, we detect 41 independent genome-wide significant loci, distinguishing impact on sinonasal or pharyngeal diseases, or both. Fine-mapping implicated non-synonymous variants in nine genes, including three linked to immune-related diseases. Phenome-wide analysis implicated asthma and atopic dermatitis at sinonasal disease loci, and inflammatory bowel diseases and other immune-mediated disorders at pharyngeal disease loci. Upper respiratory diseases also genetically correlated with autoimmune diseases such as rheumatoid arthritis, autoimmune hypothyroidism, and psoriasis. Finally, we associated separate gene pathways in sinonasal and pharyngeal diseases that both contribute to type 2 immunological reaction. We show shared heritability among upper respiratory diseases that extends to several immune-mediated diseases with diverse mechanisms, such as type 2 high inflammation. The shared genetics between upper respiratory diseases have not been well studied. Here, the authors find shared and distinct genetic loci for pharyngeal and sinonasal inflammatory conditions, which show shared heritability with autoimmune conditions and immune deficiency, highlighting the TNFR2 pathway.

The diagnostic yield of exome and genome sequencing remains low (8–70%), due to incomplete knowledge on the genes that cause disease. To improve this, we use RNA-seq data from 31,499 samples to predict which genes cause specific disease phenotypes, and develop GeneNetwork Assisted Diagnostic Optimization (GADO). We show that this unbiased method, which does not rely upon specific knowledge on individual genes, is effective in both identifying previously unknown disease gene associations, and flagging genes that have previously been incorrectly implicated in disease. GADO can be run on www.genenetwork.nl by supplying HPO-terms and a list of genes that contain candidate variants. Finally, applying GADO to a cohort of 61 patients for whom exome-sequencing analysis had not resulted in a genetic diagnosis, yields likely causative genes for ten cases. A genetic diagnosis remains unattainable for many individuals with a rare disease because of incomplete knowledge about the genetic basis of many diseases. Here, the authors present the web-based tool GADO (GeneNetwork Assisted Diagnostic Optimization) that uses public RNA-seq data for prioritization of candidate genes.

The impact of genetic variation on overall disease burden has not been comprehensively evaluated. We introduce an approach to estimate the effect of genetic risk factors on disability-adjusted life years (DALYs; ‘lost healthy life years’). We use genetic information from 735,748 individuals and consider 80 diseases. Rare variants had the highest effect on DALYs at the individual level. Among common variants, rs3798220 ( LPA ) had the strongest individual-level effect, with 1.18 DALYs from carrying 1 versus 0 copies. Being in the top 10% versus the bottom 90% of a polygenic score for multisite chronic pain had an effect of 3.63 DALYs. Some common variants had a population-level effect comparable to modifiable risk factors such as high sodium intake and low physical activity. Attributable DALYs vary between males and females for some genetic exposures. Genetic risk factors can explain a sizable number of healthy life years lost both at the individual and population level. A new analysis combining data from large biobanks and the Global Burden of Disease study estimates that genetic risk factors significantly impact the number of healthy life years lost both at the individual and population level

With decades of electronic health records linked to genetic data, large biobanks provide unprecedented opportunities for systematically understanding the genetics of the natural history of complex diseases. Genome-wide survival association analysis can identify genetic variants associated with ages of onset, disease progression and lifespan. We propose an efficient and accurate frailty model approach for genome-wide survival association analysis of censored time-to-event (TTE) phenotypes by accounting for both population structure and relatedness. Our method utilizes state-of-the-art optimization strategies to reduce the computational cost. The saddlepoint approximation is used to allow for analysis of heavily censored phenotypes (>90%) and low frequency variants (down to minor allele count 20). We demonstrate the performance of our method through extensive simulation studies and analysis of five TTE phenotypes, including lifespan, with heavy censoring rates (90.9% to 99.8%) on ~400,000 UK Biobank participants with white British ancestry and ~180,000 individuals in FinnGen. We further analyzed 871 TTE phenotypes in the UK Biobank and presented the genome-wide scale phenome-wide association results with the PheWeb browser. The proliferation of large biobanks necessitates statistical methods designed for genetic analysis on biobank data. Here, the authors have developed a frailty model-based method for GWAS analysis of time-to-event phenotypes in large biobanks that accounts for relatedness in samples and censoring of phenotypes.

Rudolf Fehrmann, Lude Franke and colleagues report a method for capturing the variation present within mammalian transcriptomes in a limited number of 'transcriptional components' and demonstrate widespread correlation between gene copy number and expression levels. The method allows for the inference of candidate gene function and the identification of potential therapeutic targets in cancer. Many cancer-associated somatic copy number alterations (SCNAs) are known. Currently, one of the challenges is to identify the molecular downstream effects of these variants. Although several SCNAs are known to change gene expression levels, it is not clear whether each individual SCNA affects gene expression. We reanalyzed 77,840 expression profiles and observed a limited set of 'transcriptional components' that describe well-known biology, explain the vast majority of variation in gene expression and enable us to predict the biological function of genes. On correcting expression profiles for these components, we observed that the residual expression levels (in 'functional genomic mRNA' profiling) correlated strongly with copy number. DNA copy number correlated positively with expression levels for 99% of all abundantly expressed human genes, indicating global gene dosage sensitivity. By applying this method to 16,172 patient-derived tumor samples, we replicated many loci with aberrant copy numbers and identified recurrently disrupted genes in genomically unstable cancers.

Organic matter (OM) other than living phytoplankton is known to affect fluorometric in situ assessments of chlorophyll in lakes. For this reason, calibrating fluorometric measurements for OM error is important. In this study, chlorophyll (Chl) fluorescence was measured in situ in multiple Finnish lakes using two sondes equipped with Chl fluorometers (ex.470/em.650–700 nm). OM absorbance (A420) was measured from water samples, and one of the two sondes was also equipped with in situ fluorometer for OM (ex.350/em.430 nm). The sonde with Chl and OM fluorometers was also deployed continuously on an automated water quality monitoring station on Lake Konnevesi. For data from multiple lakes, inclusion of water colour estimates into the calibration model improved the predictability of Chl assessments markedly. When OM absorbance or in situ OM fluorescence was used in the calibration model, predictability between the in situ Chl and laboratory Chl a assessments was also enhanced. However, correction was not superior to the one done with the water colour estimate. Our results demonstrated that correction with water colour assessments or in situ measurements of OM fluorescence offers practical means to overcome the variation due to OM when assessing Chl in humic lakes in situ.

Protein-truncating variants (PTVs) affecting dyslipidemia risk may point to therapeutic targets for cardiometabolic disease. Our objective was to identify PTVs that were associated with both lipid levels and the risk of coronary artery disease (CAD) or type 2 diabetes (T2D) and assess their possible associations with risks of other diseases. To achieve this aim, we leveraged the enrichment of PTVs in the Finnish population and tested the association of low-frequency PTVs in 1,209 genes with serum lipid levels in the Finrisk Study (n = 23,435). We then tested which of the lipid-associated PTVs were also associated with the risks of T2D or CAD, as well as 2,683 disease endpoints curated in the FinnGen Study (n = 218,792). Two PTVs were associated with both lipid levels and the risk of CAD or T2D: triglyceride-lowering variants in ANGPTL8 (-24.0[-30.4 to -16.9] mg/dL per rs760351239-T allele, P = 3.4 × 10 −9 ) and ANGPTL4 (-14.4[-18.6 to -9.8] mg/dL per rs746226153-G allele, P = 4.3 × 10 −9 ). The risk of T2D was lower in carriers of the ANGPTL4 PTV (OR = 0.70[0.60–0.81], P = 2.2 × 10 −6 ) than noncarriers. The odds of CAD were 47% lower in carriers of a PTV in ANGPTL8 (OR = 0.53[0.37–0.76], P = 4.5 × 10 −4 ) than noncarriers. Finally, the phenome-wide scan of the ANGPTL8 PTV showed that the ANGPTL8 PTV carriers were less likely to use statin therapy (68,782 cases, OR = 0.52[0.40–0.68], P = 1.7 × 10 −6 ) compared to noncarriers. Our findings provide genetic evidence of potential long-term efficacy and safety of therapeutic targeting of dyslipidemias.