Lin Fritschi

Bio: Lin Fritschi is an academic researcher from Curtin University. The author has contributed to research in topics: Population & Breast cancer. The author has an hindex of 61, co-authored 377 publications receiving 13182 citations. Previous affiliations of Lin Fritschi include Alfred Hospital & Sir Charles Gairdner Hospital.

Association analysis identifies 65 new breast cancer risk loci

Abstract: Breast cancer risk is influenced by rare coding variants in susceptibility genes, such as BRCA1, and many common, mostly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. Here we report the results of a genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry. We identified 65 new loci that are associated with overall breast cancer risk at P < 5 × 10-8. The majority of credible risk single-nucleotide polymorphisms in these loci fall in distal regulatory elements, and by integrating in silico data to predict target genes in breast cells at each locus, we demonstrate a strong overlap between candidate target genes and somatic driver genes in breast tumours. We also find that heritability of breast cancer due to all single-nucleotide polymorphisms in regulatory features was 2-5-fold enriched relative to the genome-wide average, with strong enrichment for particular transcription factor binding sites. These results provide further insight into genetic susceptibility to breast cancer and will improve the use of genetic risk scores for individualized screening and prevention.

Burden of Disease from Environmental Noise: Quantification of Healthy Life Years Lost in Europe

Abstract: The health impacts of environmental noise are a growing concern. At least one million healthy life years are lost every year from traffic-related noise in the western part of Europe. This publication summarises the evidence on the relationship between environmental noise and health effects, including cardiovascular disease, cognitive impairment, sleep disturbance, tinnitus, and annoyance. For each one, the environmental burden of disease methodology, based on exposure-response relationship, exposure distribution, background prevalence of disease and disability weights of the outcome, is applied to calculate the burden of disease in terms of disability-adjusted life-years. Data are still lacking for the rest of the WHO European Region. This publication provides policy-makers and their advisers with technical support in their quantitative risk assessment of environmental noise. International, national and local authorities can use the procedure for estimating burdens presented here to prioritize and plan environmental and public health policies.

Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes

Abstract: Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.

Considerations of circadian impact for defining 'shift work' in cancer studies: IARC working group report

Abstract: Based on the idea that electric light at night might account for a portion of the high and rising risk of breast cancer worldwide, it was predicted long ago that women working a non-day shift would be at higher risk compared with day-working women. This hypothesis has been extended more recently to prostate cancer. On the basis of limited human evidence and sufficient evidence in experimental animals, in 2007 the International Agency for Research on Cancer (IARC) classified 'shift work that involves circadian disruption' as a probable human carcinogen, group 2A. A limitation of the epidemiological studies carried out to date is in the definition of 'shift work.' IARC convened a workshop in April 2009 to consider how 'shift work' should be assessed and what domains of occupational history need to be quantified for more valid studies of shift work and cancer in the future. The working group identified several major domains of non-day shifts and shift schedules that should be captured in future studies: (1) shift system (start time of shift, number of hours per day, rotating or permanent, speed and direction of a rotating system, regular or irregular); (2) years on a particular non-day shift schedule (and cumulative exposure to the shift system over the subject's working life); and (3) shift intensity (time off between successive work days on the shift schedule). The group also recognised that for further domains to be identified, more research needs to be conducted on the impact of various shift schedules and routines on physiological and circadian rhythms of workers in real-world environments.

Physical Activity and Risks of Proximal and Distal Colon Cancers: A Systematic Review and Meta-analysis

Abstract: Background Although there is convincing epidemiological evidence that physical activity is associated with a reduced risk of colon cancer, it is unclear whether physical activity is differentially associated with the risks of proximal colon and distal colon cancers. We conducted a systematic review and meta-analysis to investigate this issue. Methods MEDLINE and EMBASE were searched for English-language cohort and case-control studies that examined associations between physical activity and the risks of proximal colon and distal colon cancers. A random-effects meta-analysis was conducted to estimate the summary relative risks (RRs) for the associations between physical activity and the risks of the two cancers. All statistical tests were two-sided. Results A total of 21 studies met the inclusion criteria. The summary relative risk of the main results from these studies indicated that the risk of proximal colon cancer was 27% lower among the most physically active people compared with the least active people (RR = 0.73, 95% confidence interval [CI] = 0.66 to 0.81). An almost identical result was found for distal colon cancer (RR = 0.74, 95% CI = 0.68 to 0.80). Conclusion The results of this systematic review and meta-analysis suggest that physical activity is associated with a reduced risk of both proximal colon and distal colon cancers, and that the magnitude of the association does not differ by subsite. Given this finding, future research on physical activity and colon cancer should focus on other aspects of the association that remain unclear, such as whether sedentary behavior and nonaerobic physical activity are associated with the risk of colon cancer.

mice: Multivariate Imputation by Chained Equations in R

Abstract: The R package mice imputes incomplete multivariate data by chained equations. The software mice 1.0 appeared in the year 2000 as an S-PLUS library, and in 2001 as an R package. mice 1.0 introduced predictor selection, passive imputation and automatic pooling. This article documents mice, which extends the functionality of mice 1.0 in several ways. In mice, the analysis of imputed data is made completely general, whereas the range of models under which pooling works is substantially extended. mice adds new functionality for imputing multilevel data, automatic predictor selection, data handling, post-processing imputed values, specialized pooling routines, model selection tools, and diagnostic graphs. Imputation of categorical data is improved in order to bypass problems caused by perfect prediction. Special attention is paid to transformations, sum scores, indices and interactions using passive imputation, and to the proper setup of the predictor matrix. mice can be downloaded from the Comprehensive R Archive Network. This article provides a hands-on, stepwise approach to solve applied incomplete data problems.

Integrative analysis of 111 reference human epigenomes

Abstract: The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

A systematic review of the literature

Abstract: OBJECTIVE — To systematically review the incidence of posttransplantation diabetes (PTD), risk factors for its development, prognostic implications, and optimal management. RESEARCH DESIGN AND METHODS — We searched databases (MEDLINE, EMBASE, the Cochrane Library, and others) from inception to September 2000, reviewed bibliographies in reports retrieved, contacted transplantation experts, and reviewed specialty journals. Two reviewers independently determined report inclusion (original studies, in all languages, of PTD in adults with no history of diabetes before transplantation), assessed study methods, and extracted data using a standardized form. Meta-regression was used to explain between-study differences in incidence. RESULTS — Nineteen studies with 3,611 patients were included. The 12-month cumulative incidence of PTD is lower (10% in most studies) than it was 3 decades ago. The type of immunosuppression explained 74% of the variability in incidence (P 0.0004). Risk factors were patient age, nonwhite ethnicity, glucocorticoid treatment for rejection, and immunosuppression with high-dose cyclosporine and tacrolimus. PTD was associated with decreased graft and patient survival in earlier studies; later studies showed improved outcomes. Randomized trials of treatment regimens have not been conducted. CONCLUSIONS — Physicians should consider modification of immunosuppressive regimens to decrease the risk of PTD in high-risk transplant recipients. Randomized trials are needed to evaluate the use of oral glucose-lowering agents in transplant recipients, paying particular attention to interactions with immunosuppressive drugs. Diabetes Care 25:583–592, 2002

The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019.

Abstract: The GWAS Catalog delivers a high-quality curated collection of all published genome-wide association studies enabling investigations to identify causal variants, understand disease mechanisms, and establish targets for novel therapies. The scope of the Catalog has also expanded to targeted and exome arrays with 1000 new associations added for these technologies. As of September 2018, the Catalog contains 5687 GWAS comprising 71673 variant-trait associations from 3567 publications. New content includes 284 full P-value summary statistics datasets for genome-wide and new targeted array studies, representing 6 × 109 individual variant-trait statistics. In the last 12 months, the Catalog's user interface was accessed by ∼90000 unique users who viewed >1 million pages. We have improved data access with the release of a new RESTful API to support high-throughput programmatic access, an improved web interface and a new summary statistics database. Summary statistics provision is supported by a new format proposed as a community standard for summary statistics data representation. This format was derived from our experience in standardizing heterogeneous submissions, mapping formats and in harmonizing content. Availability: https://www.ebi.ac.uk/gwas/.