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Cancer risk is determined by a complex interplay of environmental and heritable factors. Polygenic risk scores (PRS) provide a personalized genetic susceptibility profile that may be leveraged for disease prediction. Using data from the UK Biobank (413,753 individuals; 22,755 incident cancer cases), we quantify the added predictive value of integrating cancer-specific PRS with family history and modifiable risk factors for 16 cancers. We show that incorporating PRS measurably improves prediction accuracy for most cancers, but the magnitude of this improvement varies substantially. We also demonstrate that stratifying on levels of PRS identifies significantly divergent 5-year risk trajectories after accounting for family history and modifiable risk factors. At the population level, the top 20% of the PRS distribution accounts for 4.0% to 30.3% of incident cancer cases, exceeding the impact of many lifestyle-related factors. In summary, this study illustrates the potential for improving cancer risk assessment by integrating genetic risk scores. Predicting cancer risk requires large datasets and sophisticated models. Here the authors integrate polygenic risk scores and modifiable risk factors for multiple cancers in the UK Biobank, improving general risk prediction and distinguishing cases where genetic or lifestyle factors have stronger associations.

Key Points Although the historically different fields of quantitative genetics and epidemiology are converging to answer fundamental questions about genetic variation in risk underlying human diseases, the plethora of measures to quantify the contribution of variants to disease risk have differing terminology and assumptions, which obfuscate their use and interpretation. In this Analysis, we consider and contrast the most commonly used measures that assess disease risk contributed to the population by individual variants — the heritability of disease liability explained, approximate heritability explained, the sibling recurrence risk explained, the proportion of genetic variance explained on a logarthimic relative risk scale, the area under the receiver–operating curve (AUC) and the population attributable fraction (PAF) — and give numerical examples in breast cancer, Crohn's disease, rheumatoid arthritis and schizophrenia. We discuss the properties of these measures, show how they are connected to each other, consider the situations for which they are best suited and provide an online tool for their calculation. The most appropriate measure to use depends on the importance given to the frequency of a risk variant relative to its effect size on disease and on the baseline to which importance is expressed. These factors should be explicitly considered when assessing the contribution of genetic variants to disease. We recommend investigators to focus primarily on the heritability of liability or genetic variance on the logarthimic relative risk scale explained, as they give estimates that are less sensitive to rare high-risk variants than the other measures considered here. Moreover, we caution against using the PAF for genetic risk variants because it has various undesirable properties. The concept of individual loci providing an explanation for disease is less straightforward than it may seem at first sight, and we recommend investigators to undertake sensitivity analyses that explore how measures of the contribution of genetic variants to risk vary across a range of underlying assumptions. There are various measures to quantify the contribution of genetic variants to disease risk, but differing terminology and assumptions obfuscate their use and interpretation. In this Analysis, the authors consider and contrast six commonly used measures that assess disease risk of individual variants, and provide numerical examples in breast cancer, Crohn's disease, rheumatoid arthritis and schizophrenia. Our understanding of the genetic basis of disease has evolved from descriptions of overall heritability or familiality to the identification of large numbers of risk loci. One can quantify the impact of such loci on disease using a plethora of measures, which can guide future research decisions. However, different measures can attribute varying degrees of importance to a variant. In this Analysis, we consider and contrast the most commonly used measures — specifically, the heritability of disease liability, approximate heritability, sibling recurrence risk, overall genetic variance using a logarithmic relative risk scale, the area under the receiver–operating curve for risk prediction and the population attributable fraction — and give guidelines for their use that should be explicitly considered when assessing the contribution of genetic variants to disease.

Deciphering the shared genetic basis of distinct cancers has the potential to elucidate carcinogenic mechanisms and inform broadly applicable risk assessment efforts. Here, we undertake genome-wide association studies (GWAS) and comprehensive evaluations of heritability and pleiotropy across 18 cancer types in two large, population-based cohorts: the UK Biobank (408,786 European ancestry individuals; 48,961 cancer cases) and the Kaiser Permanente Genetic Epidemiology Research on Adult Health and Aging cohorts (66,526 European ancestry individuals; 16,001 cancer cases). The GWAS detect 21 genome-wide significant associations independent of previously reported results. Investigations of pleiotropy identify 12 cancer pairs exhibiting either positive or negative genetic correlations; 25 pleiotropic loci; and 100 independent pleiotropic variants, many of which are regulatory elements and/or influence cross-tissue gene expression. Our findings demonstrate widespread pleiotropy and offer further insight into the complex genetic architecture of cross-cancer susceptibility. Pleiotropic loci and genome-wide genetic correlations have identified shared heritability across some types of cancers. Here, the authors perform genome-wide association studies and characterize pan-cancer heritability and pleiotropy in individuals of European ancestry across 18 cancer types from two large cohorts.

Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy). For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes) that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov Chain Monte Carlo (MCMC) technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes can produce higher accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected six independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis and Peripheral Vascular Disease. We provide an R-package 'CPBayes' implementing the proposed method.

Patricia Mabry and coauthors discuss application of systems approaches in cancer research.

Prostate-specific antigen (PSA) screening for prostate cancer remains controversial because it increases overdiagnosis and overtreatment of clinically insignificant tumors. Accounting for genetic determinants of constitutive, non-cancer-related PSA variation has potential to improve screening utility. In this study, we discovered 128 genome-wide significant associations ( P  < 5 × 10 −8 ) in a multi-ancestry meta-analysis of 95,768 men and developed a PSA polygenic score (PGS PSA ) that explains 9.61% of constitutive PSA variation. We found that, in men of European ancestry, using PGS-adjusted PSA would avoid up to 31% of negative prostate biopsies but also result in 12% fewer biopsies in patients with prostate cancer, mostly with Gleason score <7 tumors. Genetically adjusted PSA was more predictive of aggressive prostate cancer (odds ratio (OR) = 3.44, P  = 6.2 × 10 −14 , area under the curve (AUC) = 0.755) than unadjusted PSA (OR = 3.31, P  = 1.1 × 10 −12 , AUC = 0.738) in 106 cases and 23,667 controls. Compared to a prostate cancer PGS alone (AUC = 0.712), including genetically adjusted PSA improved detection of aggressive disease (AUC = 0.786, P  = 7.2 × 10 −4 ). Our findings highlight the potential utility of incorporating PGS for personalized biomarkers in prostate cancer screening. Analyses of large population-based cohorts and clinical trials show that using polygenic scores to account for variability in PSA levels improves detection of prostate cancer, suggesting an approach for enhancing screening accuracy.

Assume that we are studying the potential association between a genetic variant and a binary trait. [...]assume we have measured a genetic or environmental covariate associated with the trait but independent of the variant of interest in the source population, so it is not a confounder (Figure 1b). Since these are independent in the source population, they will remain conditionally independent among cases or controls; but the variant and covariate will be correlated in the overall case-control sample (dashed line in Figure 1c).

Prostate cancer is the most commonly diagnosed neoplasm in American men. Although existing biomarkers may detect localized prostate cancer, additional strategies are necessary for improving detection and identifying aggressive disease that may require further intervention. One promising, minimally invasive biomarker is cell-free DNA (cfDNA), which consist of short DNA fragments released into circulation by dying or lysed cells that may reflect underlying cancer. Here we investigated whether differences in cfDNA concentration and cfDNA fragment size could improve the sensitivity for detecting more advanced and aggressive prostate cancer. This study included 268 individuals: 34 healthy controls, 112 men with localized prostate cancer who underwent radical prostatectomy (RP), and 122 men with metastatic castration-resistant prostate cancer (mCRPC). Plasma cfDNA concentration and fragment size were quantified with the Qubit 3.0 and the 2100 Bioanalyzer. The potential relationship between cfDNA concentration or fragment size and localized or mCRPC prostate cancer was evaluated with descriptive statistics, logistic regression, and area under the curve analysis with cross-validation. Plasma cfDNA concentrations were elevated in mCRPC patients in comparison to localized disease (OR 5ng/mL  = 1.34, P = 0.027) or to being a control (OR 5ng/mL  = 1.69, P = 0.034). Decreased average fragment size was associated with an increased risk of localized disease compared to controls (OR 5bp  = 0.77, P = 0.0008). This study suggests that while cfDNA concentration can identify mCRPC patients, it is unable to distinguish between healthy individuals and patients with localized prostate cancer. In addition to PSA, average cfDNA fragment size may be an alternative that can differentiate between healthy individuals and those with localized disease, but the low sensitivity and specificity results in an imperfect diagnostic marker. While quantification of cfDNA may provide a quick, cost-effective approach to help guide treatment decisions in advanced disease, its use is limited in the setting of localized prostate cancer.

Recent findings suggest that rare variants play an important role in both monogenic and common diseases. Due to their rarity, however, it remains unclear how to appropriately analyze the association between such variants and disease. A common approach entails combining rare variants together based on a priori information and analyzing them as a single group. Here one must make some assumptions about what to aggregate. Instead, we propose two approaches to empirically determine the most efficient grouping of rare variants. The first considers multiple possible groupings using existing information. The second is an agnostic \"step-up\" approach that determines an optimal grouping of rare variants analytically and does not rely on prior information. To evaluate these approaches, we undertook a simulation study using sequence data from genes in the one-carbon folate metabolic pathway. Our results show that using prior information to group rare variants is advantageous only when information is quite accurate, but the step-up approach works well across a broad range of plausible scenarios. This agnostic approach allows one to efficiently analyze the association between rare variants and disease while avoiding assumptions required by other approaches for grouping such variants.

Background Humans and viruses have co-evolved for millennia resulting in a complex host genetic architecture. Understanding the genetic mechanisms of immune response to viral infection provides insight into disease etiology and therapeutic opportunities. Methods We conducted a comprehensive study including genome-wide and transcriptome-wide association analyses to identify genetic loci associated with immunoglobulin G antibody response to 28 antigens for 16 viruses using serological data from 7924 European ancestry participants in the UK Biobank cohort. Results Signals in human leukocyte antigen (HLA) class II region dominated the landscape of viral antibody response, with 40 independent loci and 14 independent classical alleles, 7 of which exhibited pleiotropic effects across viral families. We identified specific amino acid (AA) residues that are associated with seroreactivity, the strongest associations presented in a range of AA positions within DRβ1 at positions 11, 13, 71, and 74 for Epstein-Barr virus (EBV), Varicella zoster virus (VZV), human herpesvirus 7, (HHV7), and Merkel cell polyomavirus (MCV). Genome-wide association analyses discovered 7 novel genetic loci outside the HLA associated with viral antibody response ( P  < 5.0 × 10 −8 ), including FUT2 (19q13.33) for human polyomavirus BK (BKV), STING1 (5q31.2) for MCV, and CXCR5 (11q23.3) and TBKBP1 (17q21.32) for HHV7. Transcriptome-wide association analyses identified 114 genes associated with response to viral infection, 12 outside of the HLA region, including ECSCR : P  = 5.0 × 10 −15 (MCV), NTN5 : P  = 1.1 × 10 −9 (BKV), and P2RY13 : P  = 1.1 × 10 −8 EBV nuclear antigen. We also demonstrated pleiotropy between viral response genes and complex diseases, from autoimmune disorders to cancer to neurodegenerative and psychiatric conditions. Conclusions Our study confirms the importance of the HLA region in host response to viral infection and elucidates novel genetic determinants beyond the HLA that contribute to host-virus interaction.