Genetic polymorphisms and susceptibility to lung disease

Susceptibility to infection by bacterium such as Bacillus anthracis has a genetic basis in mice and may also have a genetic basis in humans. In the limited human cases of inhalation anthrax, studies suggest that not all individuals exposed to anthrax spores were infected, but rather, individuals with underlying lung disease, particularly asthma, sarcoidosis and tuberculosis, might be more susceptible. In this study, we determined if polymorphisms in genes important in innate immunity are associated with increased susceptibility to infectious and non-infectious lung diseases, particularly tuberculosis and sarcoidosis, respectively, and therefore might be a risk factor for inhalation anthrax. Examination of 45 non-synonymous polymorphisms in ten genes: p47phox (NCF1), p67phox (NCF2), p40phox (NCF4), p22phox (CYBA), gp91phox (CYBB), DUOX1, DUOX2, TLR2, TLR9 and alpha 1-antitrypsin (AAT) in a cohort of 95 lung disease individuals and 95 control individuals did not show an association of these polymorphisms with increased susceptibility to lung disease

Relevance of laboratory testing for the diagnosis of primary immunodeficiencies: a review of case-based examples of selected immunodeficiencies

The field of primary immunodeficiencies (PIDs) is one of several in the area of clinical immunology that has not been static, but rather has shown exponential growth due to enhanced physician, scientist and patient education and awareness, leading to identification of new diseases, new molecular diagnoses of existing clinical phenotypes, broadening of the spectrum of clinical and phenotypic presentations associated with a single or related gene defects, increased bioinformatics resources, and utilization of advanced diagnostic technology and methodology for disease diagnosis and management resulting in improved outcomes and survival. There are currently over 200 PIDs with at least 170 associated genetic defects identified, with several of these being reported in recent years. The enormous clinical and immunological heterogeneity in the PIDs makes diagnosis challenging, but there is no doubt that early and accurate diagnosis facilitates prompt intervention leading to decreased morbidity and mortality. Diagnosis of PIDs often requires correlation of data obtained from clinical and radiological findings with laboratory immunological analyses and genetic testing. The field of laboratory diagnostic immunology is also rapidly burgeoning, both in terms of novel technologies and applications, and knowledge of human immunology. Over the years, the classification of PIDs has been primarily based on the immunological defect(s) ("immunophenotype") with the relatively recent addition of genotype, though there are clinical classifications as well. There can be substantial overlap in terms of the broad immunophenotype and clinical features between PIDs, and therefore, it is relevant to refine, at a cellular and molecular level, unique immunological defects that allow for a specific and accurate diagnosis. The diagnostic testing armamentarium for PID includes flow cytometry - phenotyping and functional, cellular and molecular assays, protein analysis, and mutation identification by gene sequencing. The complexity and diversity of the laboratory diagnosis of PIDs necessitates many of the above-mentioned tests being performed in highly specialized reference laboratories. Despite these restrictions, there remains an urgent need for improved standardization and optimization of phenotypic and functional flow cytometry and protein-specific assays. A key component in the interpretation of immunological assays is the comparison of patient data to that obtained in a statistically-robust manner from age and gender-matched healthy donors. This review highlights a few of the laboratory assays available for the diagnostic work-up of broad categories of PIDs, based on immunophenotyping, followed by examples of disease-specific testing

Assessment of interactions between 205 breast cancer susceptibility loci and 13 established risk factors in relation to breast cancer risk, in the Breast Cancer Association Consortium.

BACKGROUND: Previous gene-environment interaction studies of breast cancer risk have provided sparse evidence of interactions. Using the largest available dataset to date, we performed a comprehensive assessment of potential effect modification of 205 common susceptibility variants by 13 established breast cancer risk factors, including replication of previously reported interactions. METHODS: Analyses were performed using 28 176 cases and 32 209 controls genotyped with iCOGS array and 44 109 cases and 48 145 controls genotyped using OncoArray from the Breast Cancer Association Consortium (BCAC). Gene-environment interactions were assessed using unconditional logistic regression and likelihood ratio tests for breast cancer risk overall and by estrogen-receptor (ER) status. Bayesian false discovery probability was used to assess the noteworthiness of the meta-analysed array-specific interactions. RESULTS: Noteworthy evidence of interaction at ≤1% prior probability was observed for three single nucleotide polymorphism (SNP)-risk factor pairs. SNP rs4442975 was associated with a greater reduction of risk of ER-positive breast cancer [odds ratio (OR)int = 0.85 (0.78-0.93), Pint = 2.8 x 10-4] and overall breast cancer [ORint = 0.85 (0.78-0.92), Pint = 7.4 x 10-5) in current users of estrogen-progesterone therapy compared with non-users. This finding was supported by replication using OncoArray data of the previously reported interaction between rs13387042 (r2 = 0.93 with rs4442975) and current estrogen-progesterone therapy for overall disease (Pint = 0.004). The two other interactions suggested stronger associations between SNP rs6596100 and ER-negative breast cancer with increasing parity and younger age at first birth. CONCLUSIONS: Overall, our study does not suggest strong effect modification of common breast cancer susceptibility variants by established risk factors

Association analysis identifies 65 new breast cancer risk loci

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.We thank all the individuals who took part in these studies and all the researchers, clinicians, technicians and administrative staff who have enabled this work to be carried out. Genotyping of the OncoArray was principally funded from three sources: the PERSPECTIVE project, funded by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research, the ‘Ministère de l’Économie, de la Science et de l’Innovation du Québec’ through Genome Québec, and the Quebec Breast Cancer Foundation; the NCI Genetic Associations and Mechanisms in Oncology (GAME-ON) initiative and Discovery, Biology and Risk of Inherited Variants in Breast Cancer (DRIVE) project (NIH Grants U19 CA148065 and X01HG007492); and Cancer Research UK (C1287/A10118 and C1287/A16563). BCAC is funded by Cancer Research UK (C1287/A16563), by the European Community’s Seventh Framework Programme under grant agreement 223175 (HEALTH-F2-2009-223175) (COGS) and by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreements 633784 (B-CAST) and 634935 (BRIDGES). Genotyping of the iCOGS array was funded by the European Union (HEALTH-F2-2009-223175), Cancer Research UK (C1287/A10710), the Canadian Institutes of Health Research for the ‘CIHR Team in Familial Risks of Breast Cancer’ program, and the Ministry of Economic Development, Innovation and Export Trade of Quebec, grant PSR-SIIRI-701. Combining of the GWAS data was supported in part by The National Institute of Health (NIH) Cancer Post-Cancer GWAS initiative grant U19 CA 148065 (DRIVE, part of the GAME-ON initiative)

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

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