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Fulltext The architecture of risk for type 2 diabetes: understanding Asia in the context of global findings

Abdullah N, Attia J, Oldmeadow C, Scott RJ, Holliday EG

Int J Endocrinol, 2014;2014:593982.
PMID: 24744783 DOI: 10.1155/2014/593982

The prevalence of Type 2 diabetes is rising rapidly in both developed and developing countries. Asia is developing as the epicentre of the escalating pandemic, reflecting rapid transitions in demography, migration, diet, and lifestyle patterns. The effective management of Type 2 diabetes in Asia may be complicated by differences in prevalence, risk factor profiles, genetic risk allele frequencies, and gene-environment interactions between different Asian countries, and between Asian and other continental populations. To reduce the worldwide burden of T2D, it will be important to understand the architecture of T2D susceptibility both within and between populations. This review will provide an overview of known genetic and nongenetic risk factors for T2D, placing the results from Asian studies in the context of broader global research. Given recent evidence from large-scale genetic studies of T2D, we place special emphasis on emerging knowledge about the genetic architecture of T2D and the potential contribution of genetic effects to population differences in risk.
Characterizing the genetic risk for type 2 diabetes in a Malaysian multi-ethnic cohort

Abdullah N, Abdul Murad NA, Attia J, Oldmeadow C, Mohd Haniff EA, Syafruddin SE, et al.

Diabet Med, 2015 Oct;32(10):1377-84.
PMID: 25711284 DOI: 10.1111/dme.12735

AIMS: To characterize the association with Type 2 diabetes of known Type 2 diabetes risk variants in people in Malaysia of Malay, Chinese and Indian ancestry who participated in the Malaysian Cohort project.
METHODS: We genotyped 1604 people of Malay ancestry (722 cases, 882 controls), 1654 of Chinese ancestry (819 cases, 835 controls) and 1728 of Indian ancestry (851 cases, 877 controls). First, 62 candidate single-nucleotide polymorphisms previously associated with Type 2 diabetes were assessed for association via logistic regression within ancestral groups and then across ancestral groups using a meta-analysis. Second, estimated odds ratios were assessed for excess directional concordance with previously studied populations. Third, a genetic risk score aggregating allele dosage across the candidate single-nucleotide polymorphisms was tested for association within and across ancestral groups.
RESULTS: After Bonferroni correction, seven individual single-nucleotide polymorphisms were associated with Type 2 diabetes in the combined Malaysian sample. We observed a highly significant excess in concordance of effect directions between Malaysian and previously studied populations. The genetic risk score was strongly associated with Type 2 diabetes in all Malaysian groups, explaining from 1.0 to 1.7% of total Type 2 diabetes risk variance.
CONCLUSION: This study suggests there is substantial overlap of the genetic risk alleles underlying Type 2 diabetes in Malaysian and other populations.
Study name: The Malaysian Cohort (TMC) project
Fulltext Differing Contributions of Classical Risk Factors to Type 2 Diabetes in Multi-Ethnic Malaysian Populations

Abdullah N, Murad NAA, Attia J, Oldmeadow C, Kamaruddin MA, Jalal NA, et al.

Int J Environ Res Public Health, 2018 Dec 10;15(12).
PMID: 30544761 DOI: 10.3390/ijerph15122813

The prevalence of type 2 diabetes is escalating rapidly in Asian countries, with the rapid increase likely attributable to a combination of genetic and lifestyle factors. Recent research suggests that common genetic risk variants contribute minimally to the rapidly rising prevalence. Rather, recent changes in dietary patterns and physical activity may be more important. This nested case-control study assessed the association and predictive utility of type 2 diabetes lifestyle risk factors in participants from Malaysia, an understudied Asian population with comparatively high disease prevalence. The study sample comprised 4077 participants from The Malaysian Cohort project and included sub-samples from the three major ancestral groups: Malay (n = 1323), Chinese (n = 1344) and Indian (n = 1410). Association of lifestyle factors with type 2 diabetes was assessed within and across ancestral groups using logistic regression. Predictive utility was quantified and compared between groups using the Area Under the Receiver-Operating Characteristic Curve (AUC). In predictive models including age, gender, waist-to-hip ratio, physical activity, location, family history of diabetes and average sleep duration, the AUC ranged from 0.76 to 0.85 across groups and was significantly higher in Chinese than Malays or Indians, likely reflecting anthropometric differences. This study suggests that obesity, advancing age, a family history of diabetes and living in a rural area are important drivers of the escalating prevalence of type 2 diabetes in Malaysia.
Predicting type 2 diabetes using genetic and environmental risk factors in a multi-ethnic Malaysian cohort

Abdullah N, Abdul Murad NA, Mohd Haniff EA, Syafruddin SE, Attia J, Oldmeadow C, et al.

Public Health, 2017 Aug;149:31-38.
PMID: 28528225 DOI: 10.1016/j.puhe.2017.04.003

OBJECTIVE: Malaysia has a high and rising prevalence of type 2 diabetes (T2D). While environmental (non-genetic) risk factors for the disease are well established, the role of genetic variations and gene-environment interactions remain understudied in this population. This study aimed to estimate the relative contributions of environmental and genetic risk factors to T2D in Malaysia and also to assess evidence for gene-environment interactions that may explain additional risk variation.
STUDY DESIGN: This was a case-control study including 1604 Malays, 1654 Chinese and 1728 Indians from the Malaysian Cohort Project.
METHODS: The proportion of T2D risk variance explained by known genetic and environmental factors was assessed by fitting multivariable logistic regression models and evaluating McFadden's pseudo R(2) and the area under the receiver-operating characteristic curve (AUC). Models with and without the genetic risk score (GRS) were compared using the log likelihood ratio Chi-squared test and AUCs. Multiplicative interaction between genetic and environmental risk factors was assessed via logistic regression within and across ancestral groups. Interactions were assessed for the GRS and its 62 constituent variants.
RESULTS: The models including environmental risk factors only had pseudo R(2) values of 16.5-28.3% and AUC of 0.75-0.83. Incorporating a genetic score aggregating 62 T2D-associated risk variants significantly increased the model fit (likelihood ratio P-value of 2.50 × 10(-4)-4.83 × 10(-12)) and increased the pseudo R(2) by about 1-2% and AUC by 1-3%. None of the gene-environment interactions reached significance after multiple testing adjustment, either for the GRS or individual variants. For individual variants, 33 out of 310 tested associations showed nominal statistical significance with 0.001
Fulltext No evidence that protein truncating variants in BRIP1 are associated with breast cancer risk: implications for gene panel testing

Easton DF, Lesueur F, Decker B, Michailidou K, Li J, Allen J, et al.

J Med Genet, 2016 May;53(5):298-309.
PMID: 26921362 DOI: 10.1136/jmedgenet-2015-103529

BACKGROUND: BRCA1 interacting protein C-terminal helicase 1 (BRIP1) is one of the Fanconi Anaemia Complementation (FANC) group family of DNA repair proteins. Biallelic mutations in BRIP1 are responsible for FANC group J, and previous studies have also suggested that rare protein truncating variants in BRIP1 are associated with an increased risk of breast cancer. These studies have led to inclusion of BRIP1 on targeted sequencing panels for breast cancer risk prediction.
METHODS: We evaluated a truncating variant, p.Arg798Ter (rs137852986), and 10 missense variants of BRIP1, in 48 144 cases and 43 607 controls of European origin, drawn from 41 studies participating in the Breast Cancer Association Consortium (BCAC). Additionally, we sequenced the coding regions of BRIP1 in 13 213 cases and 5242 controls from the UK, 1313 cases and 1123 controls from three population-based studies as part of the Breast Cancer Family Registry, and 1853 familial cases and 2001 controls from Australia.
RESULTS: The rare truncating allele of rs137852986 was observed in 23 cases and 18 controls in Europeans in BCAC (OR 1.09, 95% CI 0.58 to 2.03, p=0.79). Truncating variants were found in the sequencing studies in 34 cases (0.21%) and 19 controls (0.23%) (combined OR 0.90, 95% CI 0.48 to 1.70, p=0.75).
CONCLUSIONS: These results suggest that truncating variants in BRIP1, and in particular p.Arg798Ter, are not associated with a substantial increase in breast cancer risk. Such observations have important implications for the reporting of results from breast cancer screening panels.
Fulltext Polymorphisms in a Putative Enhancer at the 10q21.2 Breast Cancer Risk Locus Regulate NRBF2 Expression

Darabi H, McCue K, Beesley J, Michailidou K, Nord S, Kar S, et al.

Am J Hum Genet, 2015 Jul 02;97(1):22-34.
PMID: 26073781 DOI: 10.1016/j.ajhg.2015.05.002

Genome-wide association studies have identified SNPs near ZNF365 at 10q21.2 that are associated with both breast cancer risk and mammographic density. To identify the most likely causal SNPs, we fine mapped the association signal by genotyping 428 SNPs across the region in 89,050 European and 12,893 Asian case and control subjects from the Breast Cancer Association Consortium. We identified four independent sets of correlated, highly trait-associated variants (iCHAVs), three of which were located within ZNF365. The most strongly risk-associated SNP, rs10995201 in iCHAV1, showed clear evidence of association with both estrogen receptor (ER)-positive (OR = 0.85 [0.82-0.88]) and ER-negative (OR = 0.87 [0.82-0.91]) disease, and was also the SNP most strongly associated with percent mammographic density. iCHAV2 (lead SNP, chr10: 64,258,684:D) and iCHAV3 (lead SNP, rs7922449) were also associated with ER-positive (OR = 0.93 [0.91-0.95] and OR = 1.06 [1.03-1.09]) and ER-negative (OR = 0.95 [0.91-0.98] and OR = 1.08 [1.04-1.13]) disease. There was weaker evidence for iCHAV4, located 5' of ADO, associated only with ER-positive breast cancer (OR = 0.93 [0.90-0.96]). We found 12, 17, 18, and 2 candidate causal SNPs for breast cancer in iCHAVs 1-4, respectively. Chromosome conformation capture analysis showed that iCHAV2 interacts with the ZNF365 and NRBF2 (more than 600 kb away) promoters in normal and cancerous breast epithelial cells. Luciferase assays did not identify SNPs that affect transactivation of ZNF365, but identified a protective haplotype in iCHAV2, associated with silencing of the NRBF2 promoter, implicating this gene in the etiology of breast cancer.
Cross-Cancer Genome-Wide Association Study of Endometrial Cancer and Epithelial Ovarian Cancer Identifies Genetic Risk Regions Associated with Risk of Both Cancers

Glubb DM, Thompson DJ, Aben KKH, Alsulimani A, Amant F, Annibali D, et al.

Cancer Epidemiol Biomarkers Prev, 2021 Jan;30(1):217-228.
PMID: 33144283 DOI: 10.1158/1055-9965.EPI-20-0739

BACKGROUND: Accumulating evidence suggests a relationship between endometrial cancer and ovarian cancer. Independent genome-wide association studies (GWAS) for endometrial cancer and ovarian cancer have identified 16 and 27 risk regions, respectively, four of which overlap between the two cancers. We aimed to identify joint endometrial and ovarian cancer risk loci by performing a meta-analysis of GWAS summary statistics from these two cancers.
METHODS: Using LDScore regression, we explored the genetic correlation between endometrial cancer and ovarian cancer. To identify loci associated with the risk of both cancers, we implemented a pipeline of statistical genetic analyses (i.e., inverse-variance meta-analysis, colocalization, and M-values) and performed analyses stratified by subtype. Candidate target genes were then prioritized using functional genomic data.
RESULTS: Genetic correlation analysis revealed significant genetic correlation between the two cancers (rG = 0.43, P = 2.66 × 10-5). We found seven loci associated with risk for both cancers (P Bonferroni < 2.4 × 10-9). In addition, four novel subgenome-wide regions at 7p22.2, 7q22.1, 9p12, and 11q13.3 were identified (P < 5 × 10-7). Promoter-associated HiChIP chromatin loops from immortalized endometrium and ovarian cell lines and expression quantitative trait loci data highlighted candidate target genes for further investigation.
CONCLUSIONS: Using cross-cancer GWAS meta-analysis, we have identified several joint endometrial and ovarian cancer risk loci and candidate target genes for future functional analysis.
IMPACT: Our research highlights the shared genetic relationship between endometrial cancer and ovarian cancer. Further studies in larger sample sets are required to confirm our findings.
Fulltext Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes

Fachal L, Aschard H, Beesley J, Barnes DR, Allen J, Kar S, et al.

Nat Genet, 2020 01;52(1):56-73.
PMID: 31911677 DOI: 10.1038/s41588-019-0537-1

Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium and enriched genomic features to determine variants with high posterior probabilities of being causal. Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT pipeline for prioritizing genes as targets of those potentially causal variants, using gene expression (expression quantitative trait loci), chromatin interaction and functional annotations. Known cancer drivers, transcription factors and genes in the developmental, apoptosis, immune system and DNA integrity checkpoint gene ontology pathways were over-represented among the highest-confidence target genes.
Fulltext Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer

Milne RL, Kuchenbaecker KB, Michailidou K, Beesley J, Kar S, Lindström S, et al.

Nat Genet, 2017 Dec;49(12):1767-1778.
PMID: 29058716 DOI: 10.1038/ng.3785

Most common breast cancer susceptibility variants have been identified through genome-wide association studies (GWAS) of predominantly estrogen receptor (ER)-positive disease. We conducted a GWAS using 21,468 ER-negative cases and 100,594 controls combined with 18,908 BRCA1 mutation carriers (9,414 with breast cancer), all of European origin. We identified independent associations at P < 5 × 10-8 with ten variants at nine new loci. At P < 0.05, we replicated associations with 10 of 11 variants previously reported in ER-negative disease or BRCA1 mutation carrier GWAS and observed consistent associations with ER-negative disease for 105 susceptibility variants identified by other studies. These 125 variants explain approximately 16% of the familial risk of this breast cancer subtype. There was high genetic correlation (0.72) between risk of ER-negative breast cancer and breast cancer risk for BRCA1 mutation carriers. These findings may lead to improved risk prediction and inform further fine-mapping and functional work to better understand the biological basis of ER-negative breast cancer.
Fulltext Association analysis identifies 65 new breast cancer risk loci

Michailidou K, Lindström S, Dennis J, Beesley J, Hui S, Kar S, et al.

Nature, 2017 Nov 02;551(7678):92-94.
PMID: 29059683 DOI: 10.1038/nature24284

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

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