Clinical Pathology - Research Publications

Permanent URI for this collection

Search Results

Now showing 1 - 10 of 102
  • Item
    Thumbnail Image
    Intratumoral presence of the genotoxic gut bacteria pks+ E. coli, Enterotoxigenic Bacteroides fragilis, and Fusobacterium nucleatum and their association with clinicopathological and molecular features of colorectal cancer
    Joo, JE ; Chu, YL ; Georgeson, P ; Walker, R ; Mahmood, K ; Clendenning, M ; Meyers, AL ; Como, J ; Joseland, S ; Preston, SG ; Diepenhorst, N ; Toner, J ; Ingle, DJ ; Sherry, NL ; Metz, A ; Lynch, BM ; Milne, RL ; Southey, MC ; Hopper, JL ; Win, AK ; Macrae, FA ; Winship, IM ; Rosty, C ; Jenkins, MA ; Buchanan, DD (Springer Nature, 2024)
    Background: This study aimed to investigate clinicopathological and molecular tumour features associated with intratumoral pks+ Escherichia coli (pks+E.coli+), pks+E.coli- (non-E.coli bacteria harbouring the pks island), Enterotoxigenic Bacteroides fragilis (ETBF) and Fusobacterium nucleatum (F. nucleatum). Methods: We screened 1697 tumour-derived DNA samples from the Australasian Colorectal Cancer Family Registry, Melbourne Collaborative Cohort Study and the ANGELS study using targeted PCR. Results: Pks+E.coli+ was associated with male sex (P < 0.01) and APC:c.835-8 A > G somatic mutation (P = 0.03). The association between pks+E.coli+ and APC:c.835-8 A > G was specific to early-onset CRCs (diagnosed<45years, P = 0.02). The APC:c.835-A > G was not associated with pks+E.coli- (P = 0.36). F. nucleatum was associated with DNA mismatch repair deficiency (MMRd), BRAF:c.1799T>A p.V600E mutation, CpG island methylator phenotype, proximal tumour location, and high levels of tumour infiltrating lymphocytes (Ps < 0.01). In the stratified analysis by MMRd subgroups, F. nucleatum was associated with Lynch syndrome, MLH1 methylated and double MMR somatic mutated MMRd subgroups (Ps < 0.01). Conclusion: Intratumoral pks+E.coli+ but not pks+E.coli- are associated with CRCs harbouring the APC:c.835-8 A > G somatic mutation, suggesting that this mutation is specifically related to DNA damage from colibactin-producing E.coli exposures. F. nucleatum was associated with both hereditary and sporadic MMRd subtypes, suggesting the MMRd tumour microenvironment is important for F. nucleatum colonisation irrespective of its cause.
  • Item
    Thumbnail Image
    Modifiable lifestyle risk factors and survival after diagnosis with multiple myeloma
    Cheah, S ; Bassett, JK ; Bruinsma, FJ ; Hopper, J ; Jayasekara, H ; Joshua, D ; Macinnis, RJ ; Prince, HM ; Southey, MC ; Vajdic, CM ; van Leeuwen, MT ; Doo, NW ; Harrison, SJ ; English, DR ; Giles, GG ; Milne, RL (TAYLOR & FRANCIS LTD, 2023-10-03)
    BACKGROUND: While remaining incurable, median overall survival for MM now exceeds 5 years. Yet few studies have investigated how modifiable lifestyle factors influence survival. We investigate whether adiposity, diet, alcohol, or smoking are associated with MM-related fatality. RESEARCH DESIGN AND METHODS: We recruited 760 incident cases of MM via cancer registries in two Australian states during 2010-2016. Participants returned questionnaires on health and lifestyle. Follow-up ended in 2020. Flexible parametric survival models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for lifestyle exposures and risk of all-cause and MM-specific fatality. RESULTS: Higher pre-diagnosis Alternative Healthy Eating Index (AHEI) scores were associated with reduced MM-specific fatality (per 10-unit score, HR = 0.84, 95%CI = 0.70-0.99). Pre-diagnosis alcohol consumption was inversely associated with MM-specific fatality, compared with nondrinkers (0.1-20 g per day, HR = 0.59, 95%CI = 0.39-0.90; >20 g per day, HR = 0.67, 95%CI = 0.40-1.13). Tobacco smoking was associated with increased all-cause fatality compared with never smoking (former smokers: HR = 1.44, 95%CI = 1.10-1.88; current smokers: HR = 1.30, 95%CI = 0.80-2.10). There was no association between pre-enrollment body mass index (BMI) and MM-specific or all-cause fatality. CONCLUSIONS: Our findings support established recommendations for healthy diets and against smoking. Higher quality diet, as measured by the AHEI, may improve survival post diagnosis with MM.
  • Item
    No Preview Available
    Associations of height, body mass index, and weight gain with breast cancer risk in carriers of a pathogenic variant in BRCA1 or BRCA2: the BRCA1 and BRCA2 Cohort Consortium
    Kast, KM ; John, EL ; Hopper, J ; Andrieu, N ; Nogues, C ; Mouret-Fourme, E ; Lasset, C ; Fricker, J-P ; Berthet, P ; Mari, V ; Salle, LK ; Schmidt, M ; Ausems, MGEM ; Garcia, EBG ; van de Beek, IR ; Wevers, M ; Evans, DG ; Tischkowitz, M ; Lalloo, F ; Cook, J ; Izatt, L ; Tripathi, V ; Snape, K ; Musgrave, H ; Sharif, S ; Murray, JV ; Colonna, SV ; Andrulis, IL ; Daly, MB ; Southey, MC ; de la Hoya, M ; Osorio, A ; Foretova, L ; Berkova, D ; Gerdes, A-M ; Olah, E ; Jakubowska, A ; Singer, CF ; Tan, Y ; Augustinsson, A ; Rantala, J ; Simard, J ; Schmutzler, RK ; Milne, RL ; Phillips, K-A ; Terry, MB ; Goldgar, D ; van Leeuwen, FE ; Mooij, TM ; Antoniou, AC ; Easton, DF ; Rookus, MA ; Engel, C (BMC, 2023-06-20)
    INTRODUCTION: Height, body mass index (BMI), and weight gain are associated with breast cancer risk in the general population. It is unclear whether these associations also exist for carriers of pathogenic variants in the BRCA1 or BRCA2 genes. PATIENTS AND METHODS: An international pooled cohort of 8091 BRCA1/2 variant carriers was used for retrospective and prospective analyses separately for premenopausal and postmenopausal women. Cox regression was used to estimate breast cancer risk associations with height, BMI, and weight change. RESULTS: In the retrospective analysis, taller height was associated with risk of premenopausal breast cancer for BRCA2 variant carriers (HR 1.20 per 10 cm increase, 95% CI 1.04-1.38). Higher young-adult BMI was associated with lower premenopausal breast cancer risk for both BRCA1 (HR 0.75 per 5 kg/m2, 95% CI 0.66-0.84) and BRCA2 (HR 0.76, 95% CI 0.65-0.89) variant carriers in the retrospective analysis, with consistent, though not statistically significant, findings from the prospective analysis. In the prospective analysis, higher BMI and adult weight gain were associated with higher postmenopausal breast cancer risk for BRCA1 carriers (HR 1.20 per 5 kg/m2, 95% CI 1.02-1.42; and HR 1.10 per 5 kg weight gain, 95% CI 1.01-1.19, respectively). CONCLUSION: Anthropometric measures are associated with breast cancer risk for BRCA1 and BRCA2 variant carriers, with relative risk estimates that are generally consistent with those for women from the general population.
  • Item
    No Preview Available
    Physical activity, sedentary time and breast cancer risk: a Mendelian randomisation study
    Dixon-Suen, SC ; Lewis, SJ ; Martin, RM ; English, DR ; Boyle, T ; Giles, GG ; Michailidou, K ; Bolla, MK ; Wang, Q ; Dennis, J ; Lush, M ; Ahearn, TU ; Ambrosone, CB ; Andrulis, IL ; Anton-Culver, H ; Arndt, V ; Aronson, KJ ; Augustinsson, A ; Auvinen, P ; Beane Freeman, LE ; Becher, H ; Beckmann, MW ; Behrens, S ; Bermisheva, M ; Blomqvist, C ; Bogdanova, N ; Bojesen, SE ; Bonanni, B ; Brenner, H ; Bruening, T ; Buys, SS ; Camp, NJ ; Campa, D ; Canzian, F ; Castelao, JE ; Cessna, MH ; Chang-Claude, J ; Chanock, SJ ; Clarke, CL ; Conroy, DM ; Couch, FJ ; Cox, A ; Cross, SS ; Czene, K ; Daly, MB ; Devilee, P ; Doerk, T ; Dwek, M ; Eccles, DM ; Eliassen, AH ; Engel, C ; Eriksson, M ; Evans, DG ; Fasching, PA ; Fletcher, O ; Flyger, H ; Fritschi, L ; Gabrielson, M ; Gago-Dominguez, M ; Garcia-Closas, M ; Garcia-Saenz, JA ; Goldberg, MS ; Guenel, P ; Guendert, M ; Hahnen, E ; Haiman, CA ; Haeberle, L ; Hakansson, N ; Hall, P ; Hamann, U ; Hart, SN ; Harvie, M ; Hillemanns, P ; Hollestelle, A ; Hooning, MJ ; Hoppe, R ; Hopper, J ; Howell, A ; Hunter, DJ ; Jakubowska, A ; Janni, W ; John, EM ; Jung, A ; Kaaks, R ; Keeman, R ; Kitahara, CM ; Koutros, S ; Kraft, P ; Kristensen, VN ; Kubelka-Sabit, K ; Kurian, AW ; Lacey, J ; Lambrechts, D ; Le Marchand, L ; Lindblom, A ; Loibl, S ; Lubinski, J ; Mannermaa, A ; Manoochehri, M ; Margolin, S ; Martinez, ME ; Mavroudis, D ; Menon, U ; Mulligan, AM ; Murphy, RA ; Nevanlinna, H ; Nevelsteen, I ; Newman, WG ; Offit, K ; Olshan, AF ; Olsson, H ; Orr, N ; Patel, A ; Peto, J ; Plaseska-Karanfilska, D ; Presneau, N ; Rack, B ; Radice, P ; Rees-Punia, E ; Rennert, G ; Rennert, HS ; Romero, A ; Saloustros, E ; Sandler, DP ; Schmidt, MK ; Schmutzler, RK ; Schwentner, L ; Scott, C ; Shah, M ; Shu, X-O ; Simard, J ; Southey, MC ; Stone, J ; Surowy, H ; Swerdlow, AJ ; Tamimi, RM ; Tapper, WJ ; Taylor, JA ; Terry, MB ; Tollenaar, RAEM ; Troester, MA ; Truong, T ; Untch, M ; Vachon, CM ; Joseph, V ; Wappenschmidt, B ; Weinberg, CR ; Wolk, A ; Yannoukakos, D ; Zheng, W ; Ziogas, A ; Dunning, AM ; Pharoah, PDP ; Easton, DF ; Milne, RL ; Lynch, BM (BMJ PUBLISHING GROUP, 2022-10)
    OBJECTIVES: Physical inactivity and sedentary behaviour are associated with higher breast cancer risk in observational studies, but ascribing causality is difficult. Mendelian randomisation (MR) assesses causality by simulating randomised trial groups using genotype. We assessed whether lifelong physical activity or sedentary time, assessed using genotype, may be causally associated with breast cancer risk overall, pre/post-menopause, and by case-groups defined by tumour characteristics. METHODS: We performed two-sample inverse-variance-weighted MR using individual-level Breast Cancer Association Consortium case-control data from 130 957 European-ancestry women (69 838 invasive cases), and published UK Biobank data (n=91 105-377 234). Genetic instruments were single nucleotide polymorphisms (SNPs) associated in UK Biobank with wrist-worn accelerometer-measured overall physical activity (nsnps=5) or sedentary time (nsnps=6), or accelerometer-measured (nsnps=1) or self-reported (nsnps=5) vigorous physical activity. RESULTS: Greater genetically-predicted overall activity was associated with lower breast cancer overall risk (OR=0.59; 95% confidence interval (CI) 0.42 to 0.83 per-standard deviation (SD;~8 milligravities acceleration)) and for most case-groups. Genetically-predicted vigorous activity was associated with lower risk of pre/perimenopausal breast cancer (OR=0.62; 95% CI 0.45 to 0.87,≥3 vs. 0 self-reported days/week), with consistent estimates for most case-groups. Greater genetically-predicted sedentary time was associated with higher hormone-receptor-negative tumour risk (OR=1.77; 95% CI 1.07 to 2.92 per-SD (~7% time spent sedentary)), with elevated estimates for most case-groups. Results were robust to sensitivity analyses examining pleiotropy (including weighted-median-MR, MR-Egger). CONCLUSION: Our study provides strong evidence that greater overall physical activity, greater vigorous activity, and lower sedentary time are likely to reduce breast cancer risk. More widespread adoption of active lifestyles may reduce the burden from the most common cancer in women.
  • Item
    No Preview Available
    Distinct Reproductive Risk Profiles for Intrinsic-Like Breast Cancer Subtypes: Pooled Analysis of Population-Based Studies
    Jung, AY ; Ahearn, TU ; Behrens, S ; Middha, P ; Bolla, MK ; Wang, Q ; Arndt, V ; Aronson, KJ ; Augustinsson, A ; Freeman, LEB ; Becher, H ; Brenner, H ; Canzian, F ; Carey, LA ; Consortium, C ; Czene, K ; Eliassen, AH ; Eriksson, M ; Evans, DG ; Figueroa, JD ; Fritschi, L ; Gabrielson, M ; Giles, GG ; Guenel, P ; Hadjisavvas, A ; Haiman, CA ; Hakansson, N ; Hall, P ; Hamann, U ; Hoppe, R ; Hopper, JL ; Howell, A ; Hunter, DJ ; Huesing, A ; Kaaks, R ; Kosma, V-M ; Koutros, S ; Kraft, P ; Lacey, J ; Le Marchand, L ; Lissowska, J ; Loizidou, MA ; Mannermaa, A ; Maurer, T ; Murphy, RA ; Olshan, AF ; Olsson, H ; Patel, A ; Perou, CM ; Rennert, G ; Shibli, R ; Shu, X-O ; Southey, MC ; Stone, J ; Tamimi, RM ; Teras, LR ; Troester, MA ; Truong, T ; Vachon, CM ; Wang, SS ; Wolk, A ; Wu, AH ; Yang, XR ; Zheng, W ; Dunning, AM ; Pharoah, PDP ; Easton, DF ; Milne, RL ; Chatterjee, N ; Schmidt, MK ; Garcia-Closas, M ; Chang-Claude, J (OXFORD UNIV PRESS INC, 2022-12)
    BACKGROUND: Reproductive factors have been shown to be differentially associated with risk of estrogen receptor (ER)-positive and ER-negative breast cancer. However, their associations with intrinsic-like subtypes are less clear. METHODS: Analyses included up to 23 353 cases and 71 072 controls pooled from 31 population-based case-control or cohort studies in the Breast Cancer Association Consortium across 16 countries on 4 continents. Polytomous logistic regression was used to estimate the association between reproductive factors and risk of breast cancer by intrinsic-like subtypes (luminal A-like, luminal B-like, luminal B-HER2-like, HER2-enriched-like, and triple-negative breast cancer) and by invasiveness. All statistical tests were 2-sided. RESULTS: Compared with nulliparous women, parous women had a lower risk of luminal A-like, luminal B-like, luminal B-HER2-like, and HER2-enriched-like disease. This association was apparent only after approximately 10 years since last birth and became stronger with increasing time (odds ratio [OR] = 0.59, 95% confidence interval [CI] = 0.49 to 0.71; and OR = 0.36, 95% CI = 0.28 to 0.46 for multiparous women with luminal A-like tumors 20 to less than 25 years after last birth and 45 to less than 50 years after last birth, respectively). In contrast, parous women had a higher risk of triple-negative breast cancer right after their last birth (for multiparous women: OR = 3.12, 95% CI = 2.02 to 4.83) that was attenuated with time but persisted for decades (OR = 1.03, 95% CI = 0.79 to 1.34, for multiparous women 25 to less than 30 years after last birth). Older age at first birth (Pheterogeneity < .001 for triple-negative compared with luminal A-like breast cancer) and breastfeeding (Pheterogeneity < .001 for triple-negative compared with luminal A-like breast cancer) were associated with lower risk of triple-negative breast cancer but not with other disease subtypes. Younger age at menarche was associated with higher risk of all subtypes; older age at menopause was associated with higher risk of luminal A-like but not triple-negative breast cancer. Associations for in situ tumors were similar to luminal A-like. CONCLUSIONS: This large and comprehensive study demonstrates a distinct reproductive risk factor profile for triple-negative breast cancer compared with other subtypes, with implications for the understanding of disease etiology and risk prediction.
  • Item
    No Preview Available
    Does genetic predisposition modify the effect of lifestyle-related factors on DNA methylation?
    Yu, C ; Hodge, AM ; Wong, EM ; Joo, JE ; Makalic, E ; Schmidt, DF ; Buchanan, DD ; Severi, G ; Hopper, JL ; English, DR ; Giles, GG ; Milne, RL ; Southey, MC ; Dugue, P-A (TAYLOR & FRANCIS INC, 2022-12-02)
    Lifestyle-related phenotypes have been shown to be heritable and associated with DNA methylation. We aimed to investigate whether genetic predisposition to tobacco smoking, alcohol consumption, and higher body mass index (BMI) moderates the effect of these phenotypes on blood DNA methylation. We calculated polygenic scores (PGS) to quantify genetic predisposition to these phenotypes using training (N = 7,431) and validation (N = 4,307) samples. Using paired genetic-methylation data (N = 4,307), gene-environment interactions (i.e., PGS × lifestyle) were assessed using linear mixed-effects models with outcomes: 1) methylation at sites found to be strongly associated with smoking (1,061 CpGs), alcohol consumption (459 CpGs), and BMI (85 CpGs) and 2) two epigenetic ageing measures, PhenoAge and GrimAge. In the validation sample, PGS explained ~1.4% (P = 1 × 10-14), ~0.6% (P = 2 × 10-7), and ~8.7% (P = 7 × 10-87) of variance in smoking initiation, alcohol consumption, and BMI, respectively. Nominally significant interaction effects (P < 0.05) were found at 61, 14, and 7 CpGs for smoking, alcohol consumption, and BMI, respectively. There was strong evidence that all lifestyle-related phenotypes were positively associated with PhenoAge and GrimAge, except for alcohol consumption with PhenoAge. There was weak evidence that the association of smoking with GrimAge was attenuated in participants genetically predisposed to smoking (interaction term: -0.022, standard error [SE] = 0.012, P = 0.058) and that the association of alcohol consumption with PhenoAge was attenuated in those genetically predisposed to drink alcohol (interaction term: -0.030, SE = 0.015, P = 0.041). In conclusion, genetic susceptibility to unhealthy lifestyles did not strongly modify the association between observed lifestyle behaviour and blood DNA methylation. Potential associations were observed for epigenetic ageing measures, which should be replicated in additional studies.
  • Item
    No Preview Available
    Methylation scores for smoking, alcohol consumption and body mass index and risk of seven types of cancer
    Dugue, P-A ; Yu, C ; Hodge, AMM ; Wong, EM ; Joo, JEE ; Jung, C-H ; Schmidt, D ; Makalic, E ; Buchanan, DDD ; Severi, G ; English, DRR ; Hopper, JLL ; Milne, RLL ; Giles, GGG ; Southey, MCC (WILEY, 2023-08-01)
    Methylation marks of exposure to health risk factors may be useful markers of cancer risk as they might better capture current and past exposures than questionnaires, and reflect different individual responses to exposure. We used data from seven case-control studies nested within the Melbourne Collaborative Cohort Study of blood DNA methylation and risk of colorectal, gastric, kidney, lung, prostate and urothelial cancer, and B-cell lymphoma (N cases = 3123). Methylation scores (MS) for smoking, body mass index (BMI), and alcohol consumption were calculated based on published data as weighted averages of methylation values. Rate ratios (RR) and 95% confidence intervals for association with cancer risk were estimated using conditional logistic regression and expressed per SD increase of the MS, with and without adjustment for health-related confounders. The contribution of MS to discriminate cases from controls was evaluated using the area under the curve (AUC). After confounder adjustment, we observed: large associations (RR = 1.5-1.7) with lung cancer risk for smoking MS; moderate associations (RR = 1.2-1.3) with urothelial cancer risk for smoking MS and with mature B-cell neoplasm risk for BMI and alcohol MS; moderate to small associations (RR = 1.1-1.2) for BMI and alcohol MS with several cancer types and cancer overall. Generally small AUC increases were observed after inclusion of several MS in the same model (colorectal, gastric, kidney, urothelial cancers: +3%; lung cancer: +7%; B-cell neoplasms: +8%). Methylation scores for smoking, BMI and alcohol consumption show independent associations with cancer risk, and may provide some improvements in risk prediction.
  • Item
    Thumbnail Image
    Weight is More Informative than Body Mass Index for Predicting Postmenopausal Breast Cancer Risk: Prospective Family Study Cohort (ProF-SC)
    Ye, Z ; Li, S ; Dite, GS ; Nguyen, TL ; MacInnis, RJ ; Andrulis, IL ; Buys, SS ; Daly, MB ; John, EM ; Kurian, AW ; Genkinger, JM ; Chung, WK ; Phillips, K-A ; Thorne, H ; Winship, IM ; Milne, RL ; Dugue, P-A ; Southey, MC ; Giles, GG ; Terry, MB ; Hopper, JL (AMER ASSOC CANCER RESEARCH, 2022-03)
    UNLABELLED: We considered whether weight is more informative than body mass index (BMI) = weight/height2 when predicting breast cancer risk for postmenopausal women, and if the weight association differs by underlying familial risk. We studied 6,761 women postmenopausal at baseline with a wide range of familial risk from 2,364 families in the Prospective Family Study Cohort. Participants were followed for on average 11.45 years and there were 416 incident breast cancers. We used Cox regression to estimate risk associations with log-transformed weight and BMI after adjusting for underlying familial risk. We compared model fits using the Akaike information criterion (AIC) and nested models using the likelihood ratio test. The AIC for the weight-only model was 6.22 units lower than for the BMI-only model, and the log risk gradient was 23% greater. Adding BMI or height to weight did not improve fit (ΔAIC = 0.90 and 0.83, respectively; both P = 0.3). Conversely, adding weight to BMI or height gave better fits (ΔAIC = 5.32 and 11.64; P = 0.007 and 0.0002, respectively). Adding height improved only the BMI model (ΔAIC = 5.47; P = 0.006). There was no evidence that the BMI or weight associations differed by underlying familial risk (P > 0.2). Weight is more informative than BMI for predicting breast cancer risk, consistent with nonadipose as well as adipose tissue being etiologically relevant. The independent but multiplicative associations of weight and familial risk suggest that, in terms of absolute breast cancer risk, the association with weight is more important the greater a woman's underlying familial risk. PREVENTION RELEVANCE: Our results suggest that the relationship between BMI and breast cancer could be due to a relationship between weight and breast cancer, downgraded by inappropriately adjusting for height; potential importance of anthropometric measures other than total body fat; breast cancer risk associations with BMI and weight are across a continuum.
  • Item
    Thumbnail Image
    Segregation analysis of 17,425 population-based breast cancer families: Evidence for genetic susceptibility and risk prediction
    Li, S ; MacInnis, RJ ; Lee, A ; Nguyen-Dumont, T ; Dorling, L ; Carvalho, S ; Dite, GS ; Shah, M ; Luccarini, C ; Wang, Q ; Milne, RL ; Jenkins, MA ; Giles, GG ; Dunning, AM ; Pharoah, PDP ; Southey, MC ; Easton, DF ; Hopper, JL ; Antoniou, AC (CELL PRESS, 2022-10-06)
    Rare pathogenic variants in known breast cancer-susceptibility genes and known common susceptibility variants do not fully explain the familial aggregation of breast cancer. To investigate plausible genetic models for the residual familial aggregation, we studied 17,425 families ascertained through population-based probands, 86% of whom were screened for pathogenic variants in BRCA1, BRCA2, PALB2, CHEK2, ATM, and TP53 via gene-panel sequencing. We conducted complex segregation analyses and fitted genetic models in which breast cancer incidence depended on the effects of known susceptibility genes and other unidentified major genes and a normally distributed polygenic component. The proportion of familial variance explained by the six genes was 46% at age 20-29 years and decreased steadily with age thereafter. After allowing for these genes, the best fitting model for the residual familial variance included a recessive risk component with a combined genotype frequency of 1.7% (95% CI: 0.3%-5.4%) and a penetrance to age 80 years of 69% (95% CI: 38%-95%) for homozygotes, which may reflect the combined effects of multiple variants acting in a recessive manner, and a polygenic variance of 1.27 (95% CI: 0.94%-1.65), which did not vary with age. The proportion of the residual familial variance explained by the recessive risk component was 40% at age 20-29 years and decreased with age thereafter. The model predicted age-specific familial relative risks consistent with those observed by large epidemiological studies. The findings have implications for strategies to identify new breast cancer-susceptibility genes and improve disease-risk prediction, especially at a young age.
  • Item
    No Preview Available
    Genetic insights into biological mechanisms governing human ovarian ageing
    Ruth, KS ; Day, FR ; Hussain, J ; Martinez-Marchal, A ; Aiken, CE ; Azad, A ; Thompson, DJ ; Knoblochova, L ; Abe, H ; Tarry-Adkins, JL ; Gonzalez, JM ; Fontanillas, P ; Claringbould, A ; Bakker, OB ; Sulem, P ; Walters, RG ; Terao, C ; Turon, S ; Horikoshi, M ; Lin, K ; Onland-Moret, NC ; Sankar, A ; Hertz, EPT ; Timshel, PN ; Shukla, V ; Borup, R ; Olsen, KW ; Aguilera, P ; Ferrer-Roda, M ; Huang, Y ; Stankovic, S ; Timmers, PRHJ ; Ahearn, TU ; Alizadeh, BZ ; Naderi, E ; Andrulis, IL ; Arnold, AM ; Aronson, KJ ; Augustinsson, A ; Bandinelli, S ; Barbieri, CM ; Beaumont, RN ; Becher, H ; Beckmann, MW ; Benonisdottir, S ; Bergmann, S ; Bochud, M ; Boerwinkle, E ; Bojesen, SE ; Bolla, MK ; Boomsma, DI ; Bowker, N ; Brody, JA ; Broer, L ; Buring, JE ; Campbell, A ; Campbell, H ; Castelao, JE ; Catamo, E ; Chanock, SJ ; Chenevix-Trench, G ; Ciullo, M ; Corre, T ; Couch, FJ ; Cox, A ; Crisponi, L ; Cross, SS ; Cucca, F ; Czene, K ; Smith, GD ; de Geus, EJCN ; de Mutsert, R ; De Vivo, I ; Demerath, EW ; Dennis, J ; Dunning, AM ; Dwek, M ; Eriksson, M ; Esko, T ; Fasching, PA ; Faul, JD ; Ferrucci, L ; Franceschini, N ; Frayling, TM ; Gago-Dominguez, M ; Mezzavilla, M ; Garcia-Closas, M ; Gieger, C ; Giles, GG ; Grallert, H ; Gudbjartsson, DF ; Gudnason, V ; Guenel, P ; Haiman, CA ; Hakansson, N ; Hall, P ; Hayward, C ; He, C ; He, W ; Heiss, G ; Hoffding, MK ; Hopper, JL ; Hottenga, JJ ; Hu, F ; Hunter, D ; Ikram, MA ; Jackson, RD ; Joaquim, MDR ; John, EM ; Joshi, PK ; Karasik, D ; Kardia, SLR ; Kartsonaki, C ; Karlsson, R ; Kitahara, CM ; Kolcic, I ; Kooperberg, C ; Kraft, P ; Kurian, AW ; Kutalik, Z ; La Bianca, M ; LaChance, G ; Langenberg, C ; Launer, LJ ; Laven, JSE ; Lawlor, DA ; Le Marchand, L ; Li, J ; Lindblom, A ; Lindstrom, S ; Lindstrom, T ; Linet, M ; Liu, Y ; Liu, S ; Luan, J ; Magi, R ; Magnusson, PKE ; Mangino, M ; Mannermaa, A ; Marco, B ; Marten, J ; Martin, NG ; Mbarek, H ; McKnight, B ; Medland, SE ; Meisinger, C ; Meitinger, T ; Menni, C ; Metspalu, A ; Milani, L ; Milne, RL ; Montgomery, GW ; Mook-Kanamori, DO ; Mulas, A ; Mulligan, AM ; Murray, A ; Nalls, MA ; Newman, A ; Noordam, R ; Nutile, T ; Nyholt, DR ; Olshan, AF ; Olsson, H ; Painter, JN ; Patel, AV ; Pedersen, NL ; Perjakova, N ; Peters, A ; Peters, U ; Pharoah, PDP ; Polasek, O ; Porcu, E ; Psaty, BM ; Rahman, I ; Rennert, G ; Rennert, HS ; Ridker, PM ; Ring, SM ; Robino, A ; Rose, LM ; Rosendaal, FR ; Rossouw, J ; Rudan, I ; Rueedi, R ; Ruggiero, D ; Sala, CF ; Saloustros, E ; Sandler, DP ; Sanna, S ; Sawyer, EJ ; Sarnowski, C ; Schlessinger, D ; Schmidt, MK ; Schoemaker, MJ ; Schraut, KE ; Scott, C ; Shekari, S ; Shrikhande, A ; Smith, AV ; Smith, BH ; Smith, JA ; Sorice, R ; Southey, MC ; Spector, TD ; Spinelli, JJ ; Stampfer, M ; Stoeckl, D ; van Meurs, JBJ ; Strauch, K ; Styrkarsdottir, U ; Swerdlow, AJ ; Tanaka, T ; Teras, LR ; Teumer, A ; thorsteinsdottir, U ; Timpson, NJ ; Toniolo, D ; Traglia, M ; Troester, MA ; Truong, T ; Tyrrell, J ; Uitterlinden, AG ; Ulivi, S ; Vachon, CM ; Vitart, V ; Voelker, U ; Vollenweider, P ; Voelzke, H ; Wang, Q ; Wareham, NJ ; Weinberg, CR ; Weir, DR ; Wilcox, AN ; van Dijk, KW ; Willemsen, G ; Wilson, JF ; Wolffenbuttel, BHR ; Wolk, A ; Wood, AR ; Zhao, W ; Zygmunt, M ; Chen, Z ; Li, L ; Franke, L ; Burgess, S ; Deelen, P ; Pers, TH ; Grondahl, ML ; Andersen, CY ; Pujol, A ; Lopez-Contreras, AJ ; Daniel, JA ; Stefansson, K ; Chang-Claude, J ; van der Schouw, YT ; Lunetta, KL ; Chasman, DI ; Easton, DF ; Visser, JA ; Ozanne, SE ; Namekawa, SH ; Solc, P ; Murabito, JM ; Ong, KK ; Hoffmann, ER ; Murray, A ; Roig, I ; Perry, JRB (NATURE PORTFOLIO, 2021-08-19)
    Reproductive longevity is essential for fertility and influences healthy ageing in women1,2, but insights into its underlying biological mechanisms and treatments to preserve it are limited. Here we identify 290 genetic determinants of ovarian ageing, assessed using normal variation in age at natural menopause (ANM) in about 200,000 women of European ancestry. These common alleles were associated with clinical extremes of ANM; women in the top 1% of genetic susceptibility have an equivalent risk of premature ovarian insufficiency to those carrying monogenic FMR1 premutations3. The identified loci implicate a broad range of DNA damage response (DDR) processes and include loss-of-function variants in key DDR-associated genes. Integration with experimental models demonstrates that these DDR processes act across the life-course to shape the ovarian reserve and its rate of depletion. Furthermore, we demonstrate that experimental manipulation of DDR pathways highlighted by human genetics increases fertility and extends reproductive life in mice. Causal inference analyses using the identified genetic variants indicate that extending reproductive life in women improves bone health and reduces risk of type 2 diabetes, but increases the risk of hormone-sensitive cancers. These findings provide insight into the mechanisms that govern ovarian ageing, when they act, and how they might be targeted by therapeutic approaches to extend fertility and prevent disease.