Biology subjects

Khaw, K.-T.

Publications and source records attributed to Khaw, K.-T..

6 recordsLinked to original sources

bioRxiv2020

Cross-platform genetic discovery of small molecule products of metabolism and application to clinical outcomes

Circulating levels of small molecules or metabolites are highly heritable, but the impact of genetic differences in metabolism on human health is not well understood. In this cross-platform, genome-wide meta-analysis of 174 metabolite levels across six cohorts including up to 86,507 participants (70% unpublished data), we identify 499 (362 novel) genome-wide significant associations (p<4.9×10-10) at 144 (94 novel) genomic regions. We show that inheritance of blood metabolite levels in the general population is characterized by pleiotropy, allelic heterogeneity, rare and common variants with large effects, non-linear associations, and enrichment for nonsynonymous variation in transporter and enzyme encoding genes. The majority of identified genes are known to be involved in biochemical processes regulating metabolite levels and to cause monogenic inborn errors of metabolism linked to specific metabolites, such as ASNS (rs17345286, MAF=0.27) and asparagine levels. We illustrate the influence of metabolite-associated variants on human health including a shared signal at GLP2R (p.Asp470Asn) associated with higher citrulline levels, body mass index, fasting glucose-dependent insulinotropic peptide and type 2 diabetes risk, and demonstrate beta-arrestin signalling as the underlying mechanism in cellular models. We link genetically-higher serine levels to a 95% reduction in the likelihood of developing macular telangiectasia type 2 [odds ratio (95% confidence interval) per standard deviation higher levels 0.05 (0.03-0.08; p=9.5×10-30)]. We further demonstrate the predictive value of genetic variants identified for serine or glycine levels for this rare and difficult to diagnose degenerative retinal disease [area under the receiver operating characteristic curve: 0.73 (95% confidence interval: 0.70-0.75)], for which low serine availability, through generation of deoxysphingolipids, has recently been shown to be causally relevant. These results show that integration of human genomic variation with circulating small molecule data obtained across different measurement platforms enables efficient discovery of genetic regulators of human metabolism and translation into clinical insights.Competing Interest StatementA.S.B. has received grants from AstraZeneca, Biogen, Bioverativ, Merck, Novartis, and Sanofi. J. D. sits on the International Cardiovascular and Metabolic Advisory Board for Novartis (since 2010), the Steering Committee of UK Biobank (since 2011), the MRC International Advisory Group (ING) member, London (since 2013), the MRC High Throughput Science Omics Panel Member, London (since 2013), the Scientific Advisory Committee for Sanofi (since 2013), the International Cardiovascular and Metabolism Research and Development Portfolio Committee for Novartis and the Astra Zeneca Genomics Advisory Board (2018). E.B.F. is an employee and stock holder of Pfizer.View Full Text

genetics↗

bioRxiv2019

The effect of sample size on polygenic hazard models for prostate cancer

We aimed to determine the effect of sample size on performance of polygenic hazard score (PHS) models in predicting the age at onset of prostate cancer. Age and genotypes were obtained for 40,861 men from the PRACTICAL consortium. The dataset included 201,590 SNPs per subject, and was split into training (34,444 samples) and testing (6,417 samples) sets. Two PHS model-building strategies were investigated. Established-SNP model considered 65 SNPs that had been associated with prostate cancer in the literature. A stepwise SNP selection was used to develop Discovery-SNP models. The performance of each PHS model was calculated for random sizes of the training set (1 to 30 thousand). The performance of a representative Established-SNP model was estimated for random sizes of the testing set (0.5 to 6 thousand). Mean HR98/50 (hazard ratio of top 2% to the average in the test set) of the Established-SNP model increased from 1.73[95%CI: 1.69-1.77] to 2.41[2.40-2.43] when the number of training samples was increased from 1 to 30 thousand. The corresponding HR98/50 of the Discovery-SNP model increased from 1.05[0.93-1.18] to 2.19[2.16-2.23]. HR98/50 of a representative Established-SNP model using testing set sample sizes of 0.6 and 6 thousand observations were 1.78[1.70-1.85] and 1.73[1.71-1.76], respectively. We estimate that a study population of 20 to 30 thousand men is required to develop Discovery-SNP PHS models for prostate cancer. The required sample size could be reduced to 10 thousand samples, if a set of SNPs associated with the disease has already been established.\n\nAuthor summaryPolygenic hazard scores represent a recent advancement in polygenic prediction to model the age of onset of various diseases, such as Alzheimers disease or prostate cancer. These scores accumulate small effect sizes from several tens of genetic variants and can be used to establish an individuals risk of experiencing an event relative to a control population across time. The largest barrier to the development of polygenic hazard scores is the large number of study subjects needed to develop the underlying models. We sought to understand the effect of varying the total number of samples on the performance of a polygenic hazard score in the context of prostate cancer. We found that the performance of the score did not appreciably change beyond 20 to 30 thousand observations when developing the model from scratch. However, when the discovery of the genetic variants can be borrowed from those already identified in the literature to be associated with the disease, the required number of samples is reduced to 10 thousand with no appreciable detriment in performance. We hope that these results can guide the design of future studies of polygenic scores in other diseases and demonstrate the importance of genome-wide association studies.

genetics↗

bioRxiv2019

Long-Term Exposure to Elevated Lipoprotein(a) Levels, Parental Lifespan and Risk of Mortality

BackgroundElevated Lipoprotein(a) (Lp[a]) levels are associated with a broad range of atherosclerotic cardiovascular diseases (CVD). The impact of high Lp(a) levels on human longevity is however controversial. Our objectives were to determine whether genetically-determined Lp(a) levels are associated with parental lifespan and to assess the association between measured and genetically-determined Lp(a) levels and long-term all-cause and cardiovascular mortality.\n\nMethodsWe determined the association between a genetic risk score of 26 single nucleotide polymorphisms weighted for their impact on Lp(a) levels (wGRS) and parental lifespan (at least one long-lived parent; father still alive and older than 90 or fathers age of death [≥]90 or mother still alive and older than 93 or mothers age of death [≥]93) in 139,362 participants from the UK Biobank. A total of 17,686 participants were considered as having high parental lifespan. We also investigated the association between Lp(a) levels and all-cause and cardiovascular mortality in 18,720 participants from the EPIC-Norfolk study.\n\nResultsIn the UK Biobank, increases in the wGRS (weighted for a 50 mg/dL increase in Lp(a) levels) were inversely associated with a high parental lifespan (odds ratio=0.92, 95% confidence interval [CI]=0.89-0.94, p=2.7x10-8). During the 20-year follow-up of the EPIC-Norfolk study, 5686 participants died (2412 from CVD-related causes). Compared to participants with Lp(a) levels <50 mg/dL, those with Lp(a) levels [≥]50 mg/dL had an increased hazard ratio (HR) for all-cause (HR=1.17, 95% CI=1.08-1.27) and cardiovascular (HR=1.54, 95% CI=1.37-1.72) mortality. Compared to individuals with Lp(a) levels below the 50th percentile of the Lp(a) distribution (in whom event rates were 29.8% and 11.3%, respectively for all-cause and cardiovascular mortality), those with Lp(a) levels equal or above the 95th percentile of the population distribution ([≥]70 mg/dL) had HRs of 1.22 (95% CI=1.09-1.37, event rate 37.5%) and 1.71 (95% CI=1.46-2.00, event rate 20.0%), for all-cause mortality and cardiovascular mortality, respectively.\n\nConclusionsResults of this study suggest a potentially causal effect of Lp(a) on human longevity, support the use of parental lifespan as a tool to study the genetic determinants of human longevity, and provide a rationale for a trial of Lp(a)-lowering therapy in individuals with high Lp(a) levels.

genetics↗

bioRxiv2019

A genetic hazard score to personalize prostate cancer screening, applied to population data

BackgroundGenetic risk stratification may inform decisions of whether--and when--a man should undergo prostate cancer (PCa) screening. We previously validated a polygenic hazard score (PHS), a weighted sum of 54 single-nucleotide polymorphism genotypes, for accurate prediction of age of onset of aggressive PCa and improved screening performance. We now assess the potential impact of PHS-informed screening.\n\nMethodsUnited Kingdom population data were fit to a continuous model of age-specific PCa incidence. Using hazard ratios estimated from ProtecT trial data, age-specific incidence rates were calculated for percentiles of genetic risk. Incidence of higher-grade PCa (Gleason[≥]7) was estimated from age-specific data from the linked CAP trial. PHS and incidence data were combined to give a risk-equivalent age, when a man with a given PHS percentile will have risk of higher-grade PCa equivalent to that of a typical man at age 50 (50-years standard). Positive predictive value (PPV) of PSA testing was calculated using PHS-adjusted (PCa-risk-equivalent age) groups identified from ProtecT.\n\nResultsExpected age of onset of higher-grade PCa is modulated by 19 years between the 1st and 99th PHS percentiles. A man with PHS in the 99th percentile reaches 50-years-standard risk at age 41; conversely, a man in the 1st percentile reaches this risk at age 60. PPV of PSA was higher for men with higher PHS-adjusted age.\n\nConclusionsPHS informs PCa screening strategies with individualized estimates of risk-equivalent age for higher-grade PCa. Screening initiation could be adjusted according to a mans genetic hazard score, improving PPV of PSA screening.

genetics↗

bioRxiv2017

Association between urinary biomarkers of total sugars and sucrose intake and BMI in a cross-sectional study

Obesity is an important modifiable risk factors for chronic diseases. While there is increasing focus on the role of dietary sugars, there remains a paucity of data establishing the association between sugar intake and obesity in the general public. The objective of this study was to investigate associations of estimated sugar intake with odds for obesity in a representative samples of English adults. We used data from 434 participants of the 2005 Health Survey of England. Biomarkers for total sugar intake were measured in 24h urine samples and used to estimate intake. Linear and logistic regression analyses were used to investigate associations between estimated intake and measures of obesity (BMI, waist circumference and waist-to-hip ratio) and obesity risk., respectively. Estimated sugars intake was significantly associated with BMI, waist circumference and waist-to-hip ratio, and these associations remained significant after adjustment for estimated protein intake. Estimated sugars intake was also associated with increased odds for obesity based on BMI (OR 1.02; 95% CI 1.00; 1.04 per 10 g), waist-circumference (OR 1.03; 95% CI 1.01; 1.05) and waist-to-hip ratio (OR 1.04; 95% CI 1.02; 1.06); all OR estimates remained significant after adjusting for estimated protein intake. Our results show a significant association between biomarker-estimated total sugars intake and both measures of obesity and obesity risk, confirming positive associations between total sugar intake, measures of obesity and obesity risk. This biomarker could be used to monitor the efficacy of public health interventions.

epidemiology↗

bioRxiv2016

A genetic risk score to guide age-specific, personalized prostate cancer screening

BackgroundProstate-specific-antigen (PSA) screening resulted in reduced prostate cancer (PCa) mortality in a large clinical trial, but due to a high false-positive rate, among other concerns, many guidelines do not endorse universal screening and instead recommend an individualized decision based on each patients risk. Genetic risk may provide key information to guide the decisions of whether and at what age to screen an individual man for PCa.\n\nMethodsGenotype, PCa status, and age from 34,444 men of European ancestry from the PRACTICAL consortium database were analyzed to select single-nucleotide polymorphisms (SNPs) associated with prostate cancer diagnosis. These SNPs were then incorporated into a survival analysis to estimate their effects on age at PCa diagnosis. The resulting polygenic hazard score (PHS) is an assessment of individual genetic risk. The final model was validated in an independent dataset comprised of 6,417 men with screening PSA and genotype data. PHS was calculated for these men to test for prediction of PCa-free survival. PHS was also combined with age-specific PCa incidence data from the U.S. population to generate a PCa-Risk (PCaR) age that relates a given mans risk to that of the population average. PHS and PCaR age were evaluated for prediction of positive predictive value (PPV) of PSA screening.\n\nFindingsPHS calculated from 54 SNPs was very highly predictive of age at PCa diagnosis for men in the validation set (p =10-53). PPV of PSA screening varied from 0.18 to 0.52 for men with low and high genetic risk, respectively. PHS modulates PCa-free survival curves by an estimated 20 years between men in the 1st or 99th percentiles of genetic risk.\n\nInterpretationPolygenic hazard scores give personalized genetic risk estimates and can inform the decisions of whether and at what age to screen a man for PCa.\n\nFundingDepartment of Defense #W81XWH-13-1-0391

genetics↗