Rare variant association on unrelated individuals in case-control studies using aggregation tests: existing methods and current limitations.

Over the past years, progress made in next-generation sequencing technologies and bioinformatics have sparked a surge in association studies. Especially, genome-wide association studies (GWASs) have demonstrated their effectiveness in identifying disease associations with common genetic variants. Yet, rare variants can contribute to additional disease risk or trait heterogeneity. Because GWASs are underpowered for detecting association with such variants, numerous statistical methods have been recently proposed. Aggregation tests collapse multiple rare variants within a genetic region (e.g. gene, gene set, genomic loci) to test for association. An increasing number of studies using such methods successfully identified trait-associated rare variants and led to a better understanding of the underlying disease mechanism. In this review, we compare existing aggregation tests, their statistical features and scope of application, splitting them into the five classical classes: burden, adaptive burden, variance-component, omnibus and other. Finally, we describe some limitations of current aggregation tests, highlighting potential direction for further investigations.

Increased frequency and mortality in persons with neurological disorders during COVID-19.

Determining the frequency and outcomes of neurological disorders associated with coronavirus disease 2019 (COVID-19) is imperative for understanding risks and for recognition of emerging neurological disorders. We investigated the susceptibility and impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection among persons with premorbid neurological disorders, in addition to the post-infection incidence of neurological sequelae, in a case-control population-based cohort. Using health service data collected between 1 March 2020 and 30 June 2021, we constructed a cohort of SARS-CoV-2 RNA-positive (n = 177 892) and -negative (n = 177 800) adults who were age, sex and comorbidity matched and underwent RT-PCR testing at similar times. COVID-19-associated mortality rates were examined within the cohort. Neurological sequelae were analysed during the acute (<3 months) and the post-acute (3-9 months) phases post-infection. The risk of death was significantly greater in the SARS-CoV-2 RNA-positive (2140 per 100 000 person years) compared with RNA-negative (922 per 100 000 person years) over a follow-up of 9 months, particularly amongst those with premorbid neurological disorders: adjusted odds ratios (95% confidence interval) in persons with a prior history of parkinsonism, 1.65 (1.15-2.37); dementia, 1.30 (1.11-1.52); seizures, 1.91 (1.26-2.87); encephalopathy, 1.82 (1.02-3.23); and stroke, 1.74 (1.05-2.86). There was also a significantly increased risk for diagnosis of new neurological sequelae during the acute time phase after COVID-19, including encephalopathy, 2.0 (1.10-3.64); dementia, 1.36 (1.07-1.73); seizure, 1.77 (1.22-2.56); and brain fog, 1.96 (1.20-3.20). These risks persisted into the post-acute phase after COVID-19, during which inflammatory myopathy (2.57, 1.07-6.15) and coma (1.87, 1.22-2.87) also became significantly increased. Thus, persons with SARS-CoV-2 infection and premorbid neurological disorders are at greater risk of death, and SARS-CoV-2 infection was complicated by increased risk of new-onset neurological disorders in both the acute and post-acute phases of COVID-19.

Meta-analysis of the human upper respiratory tract microbiome reveals robust taxonomic associations with health and disease.

BACKGROUND: The human upper respiratory tract (URT) microbiome, like the gut microbiome, varies across individuals and between health and disease states. However, study-to-study heterogeneity in reported case-control results has made the identification of consistent and generalizable URT-disease associations difficult. RESULTS: In order to address this issue, we assembled 26 independent 16S rRNA gene amplicon sequencing data sets from case-control URT studies, with approximately 2-3 studies per respiratory condition and ten distinct conditions covering common chronic and acute respiratory diseases. We leveraged the healthy control data across studies to investigate URT associations with age, sex, and geographic location, in order to isolate these associations from health and disease states. CONCLUSIONS: We found several robust genus-level associations, across multiple independent studies, with either health or disease status. We identified disease associations specific to a particular respiratory condition and associations general to all conditions. Ultimately, we reveal robust associations between the URT microbiome, health, and disease, which hold across multiple studies and can help guide follow-up work on potential URT microbiome diagnostics and therapeutics.

Relation between the Dantu blood group variant and bacteraemia in Kenyan children: a population-based case control study.

BACKGROUND: The Dantu blood group variant protects against P. falciparum infections but its wider consequences have not been previously explored. Here, we investigate the impact of Dantu on susceptibility to bacteraemia. METHODS: We conducted a case-control study in children presenting with community-acquired bacteraemia to Kilifi County Hospital in Kenya between 1998 and 2010. We used logistic regression to test for associations between the Dantu marker SNP rs186873296 A>G and both all-cause and pathogen-specific bacteraemia under an additive model. We used date of admission as a proxy measure of malaria transmission intensity, given known differences in malaria prevalence over the course of the study. RESULTS: Dantu was associated with protection from all-cause bacteraemia (OR=0.81, p=0.014), the association being greatest in homozygotes (OR=0.30, p=0.013). This protection was shared across the major bacterial pathogens but, notably, was only significant during the era of high malaria-transmission pre-2003 (OR=0.79, p=0.023). CONCLUSIONS: Consistent with previous studies showing the indirect impact on bacteraemia risk of other malaria-associated red cell variants, our study also shows that Dantu is protective against bacteraemia via its effect on malaria risk. Dantu does not appear to be under balancing selection through an increased risk of bacterial infections.

Higher Levels of SARS-CoV-2 Genetic Variation in Immunocompromised Patients: A Retrospective Case-Control Study.

BACKGROUND: A severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection lasts longer in immunocompromised hosts than in immunocompetent patients. Prolonged infection is associated with a higher probability of selection for novel SARS-CoV-2 mutations, particularly in the spike protein, a critical target for vaccines and therapeutics. METHODS: From December 2020 to September 2022, respiratory samples from 444 immunocompromised patients and 234 health care workers positive for SARS-CoV-2, diagnosed at 2 hospitals in Paris, France, were analyzed using whole-genome sequencing using Nanopore technology. Custom scripts were developed to assess the SARS-CoV-2 genetic diversity between the 2 groups and within the host. RESULTS: Most infections were SARS-CoV-2 Delta or Omicron lineages. Viral genetic diversity was significantly higher in infections of immunocompromised patients than those of controls. Minor mutations were identified in viruses sequenced from immunocompromised individuals, which became signature mutations for newer SARS-CoV-2 variants as the epidemic progressed. Two patients were coinfected with Delta and Omicron variants. The follow-up of immunocompromised patients revealed that the SARS-CoV-2 genome evolution differed in the upper and lower respiratory tracts. CONCLUSIONS: This study found that SARS-CoV-2 infection in immunocompromised patients is associated with higher genetic diversity, which could lead to the emergence of new SARS-CoV-2 variants with possible immune evasion or different virulence characteristics.

penalizedclr: an R package for penalized conditional logistic regression for integration of multiple omics layers.

BACKGROUND: The matched case-control design, up until recently mostly pertinent to epidemiological studies, is becoming customary in biomedical applications as well. For instance, in omics studies, it is quite common to compare cancer and healthy tissue from the same patient. Furthermore, researchers today routinely collect data from various and variable sources that they wish to relate to the case-control status. This highlights the need to develop and implement statistical methods that can take these tendencies into account. RESULTS: We present an R package penalizedclr, that provides an implementation of the penalized conditional logistic regression model for analyzing matched case-control studies. It allows for different penalties for different blocks of covariates, and it is therefore particularly useful in the presence of multi-source omics data. Both L1 and L2 penalties are implemented. Additionally, the package implements stability selection for variable selection in the considered regression model. CONCLUSIONS: The proposed method fills a gap in the available software for fitting high-dimensional conditional logistic regression models accounting for the matched design and block structure of predictors/features. The output consists of a set of selected variables that are significantly associated with case-control status. These variables can then be investigated in terms of functional interpretation or validation in further, more targeted studies.

eSVD-DE: cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings.

BACKGROUND: Single-cell RNA-sequencing (scRNA) datasets are becoming increasingly popular in clinical and cohort studies, but there is a lack of methods to investigate differentially expressed (DE) genes among such datasets with numerous individuals. While numerous methods exist to find DE genes for scRNA data from limited individuals, differential-expression testing for large cohorts of case and control individuals using scRNA data poses unique challenges due to substantial effects of human variation, i.e., individual-level confounding covariates that are difficult to account for in the presence of sparsely-observed genes. RESULTS: We develop the eSVD-DE, a matrix factorization that pools information across genes and removes confounding covariate effects, followed by a novel two-sample test in mean expression between case and control individuals. In general, differential testing after dimension reduction yields an inflation of Type-1 errors. However, we overcome this by testing for differences between the case and control individuals' posterior mean distributions via a hierarchical model. In previously published datasets of various biological systems, eSVD-DE has more accuracy and power compared to other DE methods typically repurposed for analyzing cohort-wide differential expression. CONCLUSIONS: eSVD-DE proposes a novel and powerful way to test for DE genes among cohorts after performing a dimension reduction. Accurate identification of differential expression on the individual level, instead of the cell level, is important for linking scRNA-seq studies to our understanding of the human population.

Random forests for the analysis of matched case-control studies.

BACKGROUND: Conditional logistic regression trees have been proposed as a flexible alternative to the standard method of conditional logistic regression for the analysis of matched case-control studies. While they allow to avoid the strict assumption of linearity and automatically incorporate interactions, conditional logistic regression trees may suffer from a relatively high variability. Further machine learning methods for the analysis of matched case-control studies are missing because conventional machine learning methods cannot handle the matched structure of the data. RESULTS: A random forest method for the analysis of matched case-control studies based on conditional logistic regression trees is proposed, which overcomes the issue of high variability. It provides an accurate estimation of exposure effects while being more flexible in the functional form of covariate effects. The efficacy of the method is illustrated in a simulation study and within an application to real-world data from a matched case-control study on the effect of regular participation in cervical cancer screening on the development of cervical cancer. CONCLUSIONS: The proposed random forest method is a promising add-on to the toolbox for the analysis of matched case-control studies and addresses the need for machine-learning methods in this field. It provides a more flexible approach compared to the standard method of conditional logistic regression, but also compared to conditional logistic regression trees. It allows for non-linearity and the automatic inclusion of interaction effects and is suitable both for exploratory and explanatory analyses.

Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies.

In genome-wide association studies (GWAS) for thousands of phenotypes in biobanks, most binary phenotypes have substantially fewer cases than controls. Many widely used approaches for joint analysis of multiple phenotypes produce inflated type I error rates for such extremely unbalanced case-control phenotypes. In this research, we develop a method to jointly analyze multiple unbalanced case-control phenotypes to circumvent this issue. We first group multiple phenotypes into different clusters based on a hierarchical clustering method, then we merge phenotypes in each cluster into a single phenotype. In each cluster, we use the saddlepoint approximation to estimate the p value of an association test between the merged phenotype and a single nucleotide polymorphism (SNP) which eliminates the issue of inflated type I error rate of the test for extremely unbalanced case-control phenotypes. Finally, we use the Cauchy combination method to obtain an integrated p value for all clusters to test the association between multiple phenotypes and a SNP. We use extensive simulation studies to evaluate the performance of the proposed approach. The results show that the proposed approach can control type I error rate very well and is more powerful than other available methods. We also apply the proposed approach to phenotypes in category IX (diseases of the circulatory system) in the UK Biobank. We find that the proposed approach can identify more significant SNPs than the other viable methods we compared with.

Association Between Causative Pathogen and Occurrence of Infection-Related Glomerulonephritis in Infective Endocarditis.

Among patients with pathologically proven infective endocarditis, the association of pathogen with occurrence of infection-related glomerulonephritis (IRGN) was examined in 48 case patients with IRGN and 192 propensity score-matched controls. Bartonella was very strongly associated with IRGN (odds ratio, 38.2 [95% confidence interval, 6.7-718.8]; P < .001); other microorganisms were not.

Endovascular Versus Medical Therapy in Posterior Cerebral Artery Stroke: Role of Baseline NIHSS Score and Occlusion Site.

BACKGROUND: Acute ischemic stroke with isolated posterior cerebral artery occlusion (iPCAO) lacks management evidence from randomized trials. We aimed to evaluate whether the association between endovascular treatment (EVT) and outcomes in iPCAO acute ischemic stroke is modified by initial stroke severity (baseline National Institutes of Health Stroke Scale [NIHSS]) and arterial occlusion site. METHODS: Based on the multicenter, retrospective, case-control study of consecutive iPCAO acute ischemic stroke patients (PLATO study [Posterior Cerebral Artery Occlusion Stroke]), we assessed the heterogeneity of EVT outcomes compared with medical management (MM) for iPCAO, according to baseline NIHSS score (≤6 versus >6) and occlusion site (P1 versus P2), using multivariable regression modeling with interaction terms. The primary outcome was the favorable shift of 3-month modified Rankin Scale (mRS). Secondary outcomes included excellent outcome (mRS score 0-1), functional independence (mRS score 0-2), symptomatic intracranial hemorrhage, and mortality. RESULTS: From 1344 patients assessed for eligibility, 1059 were included (median age, 74 years; 43.7% women; 41.3% had intravenous thrombolysis): 364 receiving EVT and 695 receiving MM. Baseline stroke severity did not modify the association of EVT with 3-month mRS distribution (Pinteraction=0.312) but did with functional independence (Pinteraction=0.010), with a similar trend on excellent outcome (Pinteraction=0.069). EVT was associated with more favorable outcomes than MM in patients with baseline NIHSS score >6 (mRS score 0-1, 30.6% versus 17.7%; adjusted odds ratio [aOR], 2.01 [95% CI, 1.22-3.31]; mRS score 0 to 2, 46.1% versus 31.9%; aOR, 1.64 [95% CI, 1.08-2.51]) but not in those with NIHSS score ≤6 (mRS score 0-1, 43.8% versus 46.3%; aOR, 0.90 [95% CI, 0.49-1.64]; mRS score 0-2, 65.3% versus 74.3%; aOR, 0.55 [95% CI, 0.30-1.0]). EVT was associated with more symptomatic intracranial hemorrhage regardless of baseline NIHSS score (Pinteraction=0.467), while the mortality increase was more pronounced in patients with NIHSS score ≤6 (Pinteraction=0.044; NIHSS score ≤6: aOR, 7.95 [95% CI, 3.11-20.28]; NIHSS score >6: aOR, 1.98 [95% CI, 1.08-3.65]). Arterial occlusion site did not modify the association of EVT with outcomes compared with MM. CONCLUSIONS: Baseline clinical stroke severity, rather than the occlusion site, may be an important modifier of the association between EVT and outcomes in iPCAO. Only severely affected patients with iPCAO (NIHSS score >6) had more favorable disability outcomes with EVT than MM, despite increased mortality and symptomatic intracranial hemorrhage.

Pre-diagnostic plasma inflammatory proteins and risk of hepatocellular carcinoma in three population-based cohort studies from the United States and the United Kingdom.

Previous studies suggest a role for inflammation in hepatocarcinogenesis. However, no study has comprehensively evaluated associations between circulating inflammatory proteins and risk of hepatocellular carcinoma (HCC) among the general population. We conducted a nested case-control study in the Nurses' Health Study (NHS) and the Health Professionals Follow-up Study (HPFS) with 56 pairs of incident HCC cases and controls. External validation was performed in the UK Biobank (34 HCC cases and 48,471 non-HCC controls). Inflammatory protein levels were measured in pre-diagnostic plasma using the Olink® Inflammation Panel. We used conditional logistic regression to calculate multivariable odds ratios (ORs) with 95% confidence intervals (CIs) for associations between a 1-standard deviation (SD) increase in biomarker levels and HCC risk, considering a statistically significant threshold of false discovery rate (FDR)-adjusted p < .05. In the NHS/HPFS, among 70 analyzed proteins with call rates >80%, 15 proteins had significant associations with HCC risk (pFDR < .05). Two proteins (stem cell factor, OR per SD = 0.31, 95% CI = 0.16-0.58; tumor necrosis factor superfamily member 12, OR per SD = 0.51, 95% CI = 0.31-0.85) were inversely associated whereas 13 proteins were positively associated with risk of HCC; positive ORs per SD ranged from 1.73 for interleukin (IL)-10 to 2.35 for C-C motif chemokine-19. A total of 11 proteins were further replicated in the UK Biobank. Seven of the eight selected positively associated proteins also showed positive associations with HCC risk by enzyme-linked immunosorbent assay, with ORs ranging from 1.56 for IL-10 to 2.72 for hepatocyte growth factor. More studies are warranted to further investigate the roles of these observed inflammatory proteins in HCC etiology, early detection, risk stratification, and disease treatment.

The landscape of etiological patterns of hepatocellular carcinoma and intrahepatic cholangiocarcinoma in Thailand.

Thailand is among countries with the highest global incidence and mortality rates of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA). While viral hepatitis and liver fluke infections have been associated with HCC and iCCA, respectively, other environmental risk factors, overall risk factor commonality and combinatorial roles, and effects on survival have not been systematically examined. We conducted a TIGER-LC consortium-based population study covering all high-incidence areas of both malignancies across Thailand: 837 HCC, 1474 iCCA, and 1112 controls (2011-2019) were comprehensively queried on lifelong environmental exposures, lifestyle, and medical history. Multivariate logistic regression and Cox proportional hazards analyses were used to evaluate risk factors and associated survival patterns. Our models identified shared risk factors between HCC and iCCA, such as viral hepatitis infection, liver fluke infection, and diabetes, including novel and shared associations of agricultural pesticide exposure (OR range of 1.50; 95% CI: 1.06-2.11 to 2.91; 95% CI: 1.82-4.63) along with vulnerable sources of drinking water. Most patients had multiple risk factors, magnifying their risk considerably. Patients with lower risk levels had better survival in both HCC (HR 0.78; 95% CI: 0.64-0.96) and iCCA (HR 0.84; 95% CI: 0.70-0.99). Risk factor co-exposures and their common associations with HCC and iCCA in Thailand emphasize the importance for future prevention and control measures, especially in its large agricultural sector. The observed mortality patterns suggest ways to stratify patients for anticipated survivorship and develop plans to support medical care of longer-term survivors, including behavioral changes to reduce exposures.

Real World Data are Not Always Big Data: The Case for Primary Data Collection on Medication Use in Pregnancy in the Context of Birth Defects Research.

Many examples of the use of real-world data in the area of pharmacoepidemiology include "big data" such as insurance claims, medical records, or hospital discharge databases. However, "big" is not always better, particularly when studying outcomes with narrow windows of etiologic relevance. Birth defects are one such outcome, where specificity of exposure timing is critical. Studies with primary data collection can be designed to query details on the timing of medication use, as well as type, dose, frequency, duration, and indication, that can better characterize the "real world". Because birth defects are rare, etiologic studies are typically case-control in design, like the National Birth Defects Prevention Study, Birth Defects Study to Evaluate Pregnancy exposureS, and Slone Birth Defects Study. Recall bias can be a concern, but the ability to collect detailed information on both prescription and over-the-counter medication use and on other exposures such as diet, family history, and sociodemographic factors is a distinct advantage over claims and medical record data sources. Case-control studies with primary data collection are essential to advancing the pharmacoepidemiology of birth defects.

Marshall Joffe's Contributions to Causal Inference, Biostatistics, and Epidemiology.

We pay tribute to Marshall Joffe, PhD, and his substantial contributions to the field of causal inference with focus in biostatistics and epidemiology. By compiling narratives written by us, his colleagues, we not only present highlights of Marshall's research and their significance for causal inference but also offer a portrayal of Marshall's personal accomplishments and character. Our discussion of Marshall's research notably includes (but is not limited to) handling of posttreatment variables such as noncompliance, employing G-estimation for treatment effects on failure-time outcomes, estimating effects of time-varying exposures subject to time-dependent confounding, and developing a causal framework for case-control studies. We also provide a description of some of Marshall's unpublished work, which is accompanied by a bonus anecdote. We discuss future research directions related to Marshall's research. While Marshall's impact in causal inference and the world outside of it cannot be wholly captured by our words, we hope nonetheless to present some of what he has done for our field and what he has meant to us and to his loved ones.

Cohort Studies Versus Case-Control Studies on Night-Shift Work and Cancer Risk: The Importance of Exposure Assessment.

It is a general assumption that the prospective cohort study design is the gold standard approach and is superior to the case-control study design in epidemiology. However, there may be exceptions if the exposure is complex and requires collection of detailed information on many different aspects. Night-shift work, which impairs circadian rhythms, is an example of such a complex occupational exposure and may increase the risks of breast, prostate, and colorectal cancer. So far, for logistical reasons, investigators in cohort studies have assessed shift work rather crudely, lacking information on full occupational history and relevant shift-work metrics, and have presented mostly null findings. On the other hand, most cancer case-control studies have assessed the lifetime occupational histories of participants, including collection of detailed night-shift work metrics (e.g., type, duration, intensity), and tend to show positive associations. In this commentary, we debate why cohort studies with weak exposure assessment and other limitations might not necessarily be the preferred or less biased approach in assessing the carcinogenicity of night-shift work. Furthermore, we propose that risk-of-bias assessment and comparison of associations between studies with low versus high risks of bias be considered in future synthesis of the evidence.

Predicted 25-hydroxyvitamin D over the adult life and the risk of ovarian cancer.

The evidence from previous studies of serum 25-hydroxyvitamin D [25(OH)D] and ovarian cancer risk are not conclusive. However, 25(OH)D was generally only measured in late adulthood, which may not capture the etiologically relevant exposure periods. We investigated predicted 25(OH)D over the adult lifetime in relation to ovarian cancer risk in a population-based case-control study conducted from 2011 to 2016 in Montreal, Canada (490 cases, 896 controls). Predicted 25(OH)D was computed using previously validated regression models. Unconditional multivariable logistic regression models were used to estimate adjusted odds ratios (aOR) and 95% confidence intervals (CI) for average predicted 25(OH)D over the adult life and risk. In addition, the relative importance of different periods of past 25(OH)D exposure was explored using a weighted cumulative exposure (WCE) model. For each 20 nmol/L increase in average predicted 25(OH)D over the adult life, the aOR (95% CI) was 0.73 (0.55-0.96). In WCE analyses, the inverse association was strongest for exposures 5 to 20 years and 35 to 55 years prior to diagnosis, with aORs (95% CIs) of 0.82 (0.69-0.94) and 0.79 (0.66-1.02), respectively, for each 20 nmol/L increase in predicted 25(OH)D. These results support an inverse association between 25(OH)D in adulthood and ovarian cancer risk.

The Healthy Participant Effect: insights and results from a population-based case-control study on breast cancer.

Agreement to participate in case-control studies has become low. Healthy participant bias resulting from differential response proportions in cases and controls can distort results; however, the magnitude of bias is difficult to assess. We investigated the effect in a large population-based case-control study on breast cancer, with a participation rate of 43.4% and 64.1% for controls and cases. We performed a mortality follow-up in 2020 for 3,813 cases and 7,335 controls recruited between 2002-2005. Standardized mortality ratios (SMR) for overall mortality and selected causes of death were estimated. The mean age at recruitment was 63.1 years. The overall mortality for controls was 0.66 times lower (95%CI 0.62-0.69) than for the reference population. For causes of death other than breast cancer, SMRs were similar in cases and controls (0.70 and 0.64). Higher education was associated with lower SMRs in both cases and controls. Options for adjusting the healthy participant bias are limited if the true risk factor distribution in the underlying population is unknown. However, a relevant bias in this particular case-control study is considered unlikely since a similar healthy participant effect was observed for both controls and cases.

Efficient risk-based collection of biospecimens in cohort studies: Designing a prospective study of diagnostic performance for multicancer detection tests.

In cohort studies, it can be infeasible to collect specimens on an entire cohort. For example, to estimate sensitivity of multiple Multi-Cancer Detection (MCD) assays, we desire an extra 80mL of cell-free DNA (cfDNA) blood, but this much extra blood is too expensive for us to collect on everyone. We propose a novel epidemiologic study design that efficiently oversamples those at highest baseline disease risk from whom to collect specimens, to increase the number of future cases with cfDNA blood collection. The variance reduction ratio from our risk-based subsample versus a simple random (sub)sample (SRS) depends primarily on the ratio of risk model sensitivity to the fraction of the cohort selected for specimen collection subject to constraining the risk model specificity. In a simulation where we chose 34% of Prostate, Lung, Colorectal, and Ovarian Screening Trial cohort at highest risk of lung cancer for cfDNA blood collection, we could enrich the number of lung cancers 2.42-fold and the standard deviation of lung-cancer MCD sensitivity was 31-33% reduced versus SRS. Risk-based collection of specimens on a subsample of the cohort could be a feasible and efficient approach to collecting extra specimens for molecular epidemiology.

Population-based germline breast cancer gene association studies and meta-analysis to inform wider mainstream testing.

BACKGROUND: Germline genetic testing, previously restricted to familial and young-onset breast cancer, is now offered increasingly broadly to 'population-type' breast cancer patients in mainstream oncology clinics, with wide variation in the genes included. METHODS: Weighted meta-analysis was performed for three population-based case-control studies (BRIDGES, CARRIERS and UK Biobank) comprising in total 101,397 women with breast cancer and 312,944 women without breast cancer, to quantify for 37 putative breast cancer susceptibility genes (BCSGs) the frequency of pathogenic variants (PVs) in unselected, 'population-type' breast cancer cases and their association with breast cancer and its subtypes. RESULTS: Meta-analysed odds ratios (ORs) and frequencies of PVs in population-type breast cancer cases were generated for BRCA1 (OR= 8.73 (95% CI 7.47-10.20), 1 in 101), BRCA2 (OR=5.68 (5.13-6.30), 1 in 68) and PALB2 (OR= 4.30 (95% CI 3.68-5.03), 1 in 187). For both CHEK2 (OR=2.40 (95% CI 2.21-2.62), 1 in 73) and ATM (OR=2.16 (95%CI 1.93-2.41), 1 in 132) subgroup analysis showed stronger association with ER-positive disease. Magnitude of association and frequency of PVs were low for RAD51C (OR=1.53 (95%CI 1.15-2.04), 1 in 913), RAD51D (OR=1.76, (95%CI 1.15-2.41, 1 in 1079) and BARD1 (OR=2.34 (1.85-2.97), 1 in 672); frequencies and associations were moderately higher restricting to triple-negative breast cancers The PV-frequency in 'population-type' breast cancer cases was very low for 'syndromic' BCSGs TP53 (1 in 1844), STK11 (1 in 11,525), CDH1 (1 in 2668), PTEN (1 in 3755) and NF1 (1 in 1470), with metrics of association also modest ranging from OR=3.62 (95%CI 1.98-6.61) for TP53 down to OR=1.60 (95%CI 0.48-5.30) for STK11. CONCLUSIONS: These metrics reflecting 'population-type' breast cancer will be informative to defining the appropriate gene set as we continue to expand to germline testing out to more unselected population-type breast cancer cases.