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51. Genetic architecture of human thinness compared to severe obesity.

Author

Riveros-McKay, Fernando, Mistry, Vanisha, Bounds, Rebecca, Hendricks, Audrey, Keogh, Julia M., Thomas, Hannah, Henning, Elana, Corbin, Laura J., null, null, O’Rahilly, Stephen, Zeggini, Eleftheria, Wheeler, Eleanor, Barroso, Inês, and Farooqi, I. Sadaf

Subjects
OBESITY, GENES, GENETICS, GENOMES, BODY mass index, LEANNESS
Abstract

The variation in weight within a shared environment is largely attributable to genetic factors. Whilst many genes/loci confer susceptibility to obesity, little is known about the genetic architecture of healthy thinness. Here, we characterise the heritability of thinness which we found was comparable to that of severe obesity (h2 = 28.07 vs 32.33% respectively), although with incomplete genetic overlap (r = -0.49, 95% CI [-0.17, -0.82], p = 0.003). In a genome-wide association analysis of thinness (n = 1,471) vs severe obesity (n = 1,456), we identified 10 loci previously associated with obesity, and demonstrate enrichment for established BMI-associated loci (pbinomial = 3.05x10-5). Simulation analyses showed that different association results between the extremes were likely in agreement with additive effects across the BMI distribution, suggesting different effects on thinness and obesity could be due to their different degrees of extremeness. In further analyses, we detected a novel obesity and BMI-associated locus at PKHD1 (rs2784243, obese vs. thin p = 5.99x10-6, obese vs. controls p = 2.13x10-6 pBMI = 2.3x10-13), associations at loci recently discovered with much larger sample sizes (e.g. FAM150B and PRDM6-CEP120), and novel variants driving associations at previously established signals (e.g. rs205262 at the SNRPC/C6orf106 locus and rs112446794 at the PRDM6-CEP120 locus). Our ability to replicate loci found with much larger sample sizes demonstrates the value of clinical extremes and suggest that characterisation of the genetics of thinness may provide a more nuanced understanding of the genetic architecture of body weight regulation and may inform the identification of potential anti-obesity targets. [ABSTRACT FROM AUTHOR]

Published
2019
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52. A reference panel of 64,976 haplotypes for genotype imputation

Author

Neurogenetica, Brain, ZL Neuromusculaire Ziekten Medisch, Projectafdeling ALS, Regenerative Medicine and Stem Cells, Opleiding Neurologie, CMM Groep Kaaij, Infection & Immunity, Circulatory Health, McCarthy, Shane, Das, Sayantan, Kretzschmar, Warren, Delaneau, Olivier, Wood, Andrew R, Teumer, Alexander, Kang, Hyun Min, Fuchsberger, Christian, Danecek, Petr, Sharp, Kevin, Luo, Yang, Sidore, Carlo, Kwong, Alan, Timpson, Nicholas, Koskinen, Seppo, Vrieze, Scott, Scott, Laura J, Zhang, He, Mahajan, Anubha, Veldink, Jan, Peters, Ulrike, Pato, Carlos, van Duijn, Cornelia M, Gillies, Christopher E, Gandin, Ilaria, Mezzavilla, Massimo, Gilly, Arthur, Cocca, Massimiliano, Traglia, Michela, Angius, Andrea, Barrett, Jeffrey C, Boomsma, Dorrett, Branham, Kari, Breen, Gerome, Brummett, Chad M, Busonero, Fabio, Campbell, Harry, Chan, Andrew, Chen, Sai, Chew, Emily, Collins, Francis S, Corbin, Laura J, Smith, George Davey, Dedoussis, George, Dorr, Marcus, Farmaki, Aliki-Eleni, Van den Berg, Leonard H, Van Rheenen, Wouter, de Bakker, Paul I W, Dekker, Annelot, Haplotype Reference Consortium, Neurogenetica, Brain, ZL Neuromusculaire Ziekten Medisch, Projectafdeling ALS, Regenerative Medicine and Stem Cells, Opleiding Neurologie, CMM Groep Kaaij, Infection & Immunity, Circulatory Health, McCarthy, Shane, Das, Sayantan, Kretzschmar, Warren, Delaneau, Olivier, Wood, Andrew R, Teumer, Alexander, Kang, Hyun Min, Fuchsberger, Christian, Danecek, Petr, Sharp, Kevin, Luo, Yang, Sidore, Carlo, Kwong, Alan, Timpson, Nicholas, Koskinen, Seppo, Vrieze, Scott, Scott, Laura J, Zhang, He, Mahajan, Anubha, Veldink, Jan, Peters, Ulrike, Pato, Carlos, van Duijn, Cornelia M, Gillies, Christopher E, Gandin, Ilaria, Mezzavilla, Massimo, Gilly, Arthur, Cocca, Massimiliano, Traglia, Michela, Angius, Andrea, Barrett, Jeffrey C, Boomsma, Dorrett, Branham, Kari, Breen, Gerome, Brummett, Chad M, Busonero, Fabio, Campbell, Harry, Chan, Andrew, Chen, Sai, Chew, Emily, Collins, Francis S, Corbin, Laura J, Smith, George Davey, Dedoussis, George, Dorr, Marcus, Farmaki, Aliki-Eleni, Van den Berg, Leonard H, Van Rheenen, Wouter, de Bakker, Paul I W, Dekker, Annelot, and Haplotype Reference Consortium

Published
2016

58. A genome-wide association study of osteochondritis dissecans in the Thoroughbred

Author

Corbin, Laura J., primary, Blott, Sarah C., additional, Swinburne, June E., additional, Sibbons, Charlene, additional, Fox-Clipsham, Laura Y., additional, Helwegen, Maud, additional, Parkin, Tim D. H., additional, Newton, J. Richard, additional, Bramlage, Lawrence R., additional, McIlwraith, C. Wayne, additional, Bishop, Stephen C., additional, Woolliams, John A., additional, and Vaudin, Mark, additional

Published
2011
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59. Schizophrenia-associated variation at ZNF804Acorrelates with altered experience-dependent dynamics of sleep slow waves and spindles in healthy young adults

Author

Bartsch, Ullrich, Corbin, Laura J, Hellmich, Charlotte, Taylor, Michelle, Easey, Kayleigh E, Durant, Claire, Marston, Hugh M, Timpson, Nicholas J, and Jones, Matthew W

Abstract

The rs1344706 polymorphism in ZNF804Ais robustly associated with schizophrenia and schizophrenia is, in turn, associated with abnormal non-rapid eye movement (NREM) sleep neurophysiology. To examine whether rs1344706 is associated with intermediate neurophysiological traits in the absence of disease, we assessed the relationship between genotype, sleep neurophysiology, and sleep-dependent memory consolidation in healthy participants. We recruited healthy adult males with no history of psychiatric disorder from the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort. Participants were homozygous for either the schizophrenia-associated ‘A’ allele (N= 22) or the alternative ‘C’ allele (N= 18) at rs1344706. Actigraphy, polysomnography (PSG) and a motor sequence task (MST) were used to characterize daily activity patterns, sleep neurophysiology and sleep-dependent memory consolidation. Average MST learning and sleep-dependent performance improvements were similar across genotype groups, albeit more variable in the AA group. During sleep after learning, CC participants showed increased slow-wave (SW) and spindle amplitudes, plus augmented coupling of SW activity across recording electrodes. SW and spindles in those with the AA genotype were insensitive to learning, whilst SW coherence decreased following MST training. Accordingly, NREM neurophysiology robustly predicted the degree of overnight motor memory consolidation in CC carriers, but not in AA carriers. We describe evidence that rs1344706 polymorphism in ZNF804Ais associated with changes in the coordinated neural network activity that supports offline information processing during sleep in a healthy population. These findings highlight the utility of sleep neurophysiology in mapping the impacts of schizophrenia-associated common genetic variants on neural circuit oscillations and function.

Published
2021
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60. BMI as a Modifiable Risk Factor for Type 2 Diabetes: Refining and Understanding Causal Estimates Using Mendelian Randomization

Author

Corbin, Laura J, Richmond, Rebecca C, Wade, Kaitlin H, Burgess, Stephen, Bowden, Jack, Smith, George Davey, and Timpson, Nicholas J

Subjects
Diabetes Mellitus, Type 2, Risk Factors, Odds Ratio, Humans, Genetic Pleiotropy, Genetic Predisposition to Disease, Mendelian Randomization Analysis, Transcription Factor 7-Like 2 Protein, 3. Good health, Body Mass Index
Abstract

This study focused on resolving the relationship between BMI and type 2 diabetes. The availability of multiple variants associated with BMI offers a new chance to resolve the true causal effect of BMI on type 2 diabetes; however, the properties of these associations and their validity as genetic instruments need to be considered alongside established and new methods for undertaking Mendelian randomization (MR). We explore the potential for pleiotropic genetic variants to generate bias, revise existing estimates, and illustrate value in new analysis methods. A two-sample MR approach with 96 genetic variants was used with three different analysis methods, two of which (MR-Egger and the weighted median) have been developed specifically to address problems of invalid instrumental variables. We estimate an odds ratio for type 2 diabetes per unit increase in BMI (kg/m(2)) of between 1.19 and 1.38, with the most stable estimate using all instruments and a weighted median approach (1.26 [95% CI 1.17, 1.34]). TCF7L2(rs7903146) was identified as a complex effect or pleiotropic instrument, and removal of this variant resulted in convergence of causal effect estimates from different causal analysis methods. This indicated the potential for pleiotropy to affect estimates and differences in performance of alternative analytical methods. In a real type 2 diabetes-focused example, this study demonstrates the potential impact of invalid instruments on causal effect estimates and the potential for new approaches to mitigate the bias caused.

61. Loss-of-function mutations in the melanocortin 4 receptor in a UK birth cohort

Author

Wade, Kaitlin H, Lam, Brian YH, Melvin, Audrey, Pan, Warren, Corbin, Laura J, Hughes, David A, Rainbow, Kara, Chen, Jian-Hua, Duckett, Katie, Liu, Xiaoming, Mokrosiński, Jacek, Mörseburg, Alexander, Neaves, Sam, Williamson, Alice, Zhang, Chen, Farooqi, I Sadaf, Yeo, Giles SH, Timpson, Nicholas J, and O'Rahilly, Stephen

Subjects
2. Zero hunger, Adult, Male, Heterozygote, Adolescent, Body Weight, Infant, Newborn, Infant, United Kingdom, 3. Good health, Young Adult, Phenotype, Loss of Function Mutation, Child, Preschool, Humans, Receptor, Melanocortin, Type 4, Genetic Predisposition to Disease, Obesity, Child
Abstract

Mutations in the melanocortin 4 receptor gene (MC4R) are associated with obesity but little is known about the prevalence and impact of such mutations throughout human growth and development. We examined the MC4R coding sequence in 5,724 participants from the Avon Longitudinal Study of Parents and Children, functionally characterized all nonsynonymous MC4R variants and examined their association with anthropometric phenotypes from childhood to early adulthood. The frequency of heterozygous loss-of-function (LoF) mutations in MC4R was ~1 in 337 (0.30%), considerably higher than previous estimates. At age 18 years, mean differences in body weight, body mass index and fat mass between carriers and noncarriers of LoF mutations were 17.76 kg (95% CI 9.41, 26.10), 4.84 kg m-2 (95% CI 2.19, 7.49) and 14.78 kg (95% CI 8.56, 20.99), respectively. MC4R LoF mutations may be more common than previously reported and carriers of such variants may enter adult life with a substantial burden of excess adiposity.

62. The blood metabolome of incident kidney cancer: A case-control study nested within the MetKid consortium

Author

Guida, Florence, Tan, Vanessa Y, Corbin, Laura J, Smith-Byrne, Karl, Alcala, Karine, Langenberg, Claudia, Stewart, Isobel D, Butterworth, Adam S, Surendran, Praveen, Achaintre, David, Adamski, Jerzy, Amiano Exezarreta, Pilar, Bergmann, Manuela M, Bull, Caroline J, Dahm, Christina C, Gicquiau, Audrey, Giles, Graham G, Gunter, Marc J, Haller, Toomas, Langhammer, Arnulf, Larose, Tricia L, Ljungberg, Börje, Metspalu, Andres, Milne, Roger L, Muller, David C, Nøst, Therese H, Pettersen Sørgjerd, Elin, Prehn, Cornelia, Riboli, Elio, Rinaldi, Sabina, Rothwell, Joseph A, Scalbert, Augustin, Schmidt, Julie A, Severi, Gianluca, Sieri, Sabina, Vermeulen, Roel, Vincent, Emma E, Waldenberger, Melanie, Timpson, Nicholas J, and Johansson, Mattias

Subjects
2. Zero hunger, Male, Victoria, Incidence, Mendelian Randomization Analysis, Middle Aged, Risk Assessment, Kidney Neoplasms, 3. Good health, Body Mass Index, Europe, Risk Factors, Case-Control Studies, Metabolome, Humans, Metabolomics, Female, Obesity, Prospective Studies, Biomarkers, Aged
Abstract

BACKGROUND: Excess bodyweight and related metabolic perturbations have been implicated in kidney cancer aetiology, but the specific molecular mechanisms underlying these relationships are poorly understood. In this study, we sought to identify circulating metabolites that predispose kidney cancer and to evaluate the extent to which they are influenced by body mass index (BMI). METHODS AND FINDINGS: We assessed the association between circulating levels of 1,416 metabolites and incident kidney cancer using pre-diagnostic blood samples from up to 1,305 kidney cancer case-control pairs from 5 prospective cohort studies. Cases were diagnosed on average 8 years after blood collection. We found 25 metabolites robustly associated with kidney cancer risk. In particular, 14 glycerophospholipids (GPLs) were inversely associated with risk, including 8 phosphatidylcholines (PCs) and 2 plasmalogens. The PC with the strongest association was PC ae C34:3 with an odds ratio (OR) for 1 standard deviation (SD) increment of 0.75 (95% confidence interval [CI]: 0.68 to 0.83, p = 2.6 × 10-8). In contrast, 4 amino acids, including glutamate (OR for 1 SD = 1.39, 95% CI: 1.20 to 1.60, p = 1.6 × 10-5), were positively associated with risk. Adjusting for BMI partly attenuated the risk association for some-but not all-metabolites, whereas other known risk factors of kidney cancer, such as smoking and alcohol consumption, had minimal impact on the observed associations. A mendelian randomisation (MR) analysis of the influence of BMI on the blood metabolome highlighted that some metabolites associated with kidney cancer risk are influenced by BMI. Specifically, elevated BMI appeared to decrease levels of several GPLs that were also found inversely associated with kidney cancer risk (e.g., -0.17 SD change [ßBMI] in 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2) levels per SD change in BMI, p = 3.4 × 10-5). BMI was also associated with increased levels of glutamate (ßBMI: 0.12, p = 1.5 × 10-3). While our results were robust across the participating studies, they were limited to study participants of European descent, and it will, therefore, be important to evaluate if our findings can be generalised to populations with different genetic backgrounds. CONCLUSIONS: This study suggests a potentially important role of the blood metabolome in kidney cancer aetiology by highlighting a wide range of metabolites associated with the risk of developing kidney cancer and the extent to which changes in levels of these metabolites are driven by BMI-the principal modifiable risk factor of kidney cancer.

63. Genetic architecture of human thinness compared to severe obesity

Author

Farooqi, Ismaa Sadaf, Riveros-McKay, Fernando, Mistry, Vanisha, Bounds, Rebecca, Hendricks, Audrey, Keogh, Julia M, Thomas, Hannah, Henning, Elana, Corbin, Laura J, O'Rahilly, Stephen, Zeggini, Eleftheria, Wheeler, Eleanor, and Barroso, Ines

Subjects
2. Zero hunger, meta-analysis, obesity, genome signal processing, genome-wide association studies, genetic loci, human genetics, body mass index, childhood obesity
Abstract

The variation in weight within a shared environment is largely attributable to genetic factors. Whilst many genes/loci confer susceptibility to obesity, little is known about the genetic architecture of healthy thinness. Here, we characterise the heritability of thinness which we found was comparable to that of severe obesity (h2=28.07 vs 32.33% respectively), although with incomplete genetic overlap (r=-0.49, 95% CI [-0.17, -0.82], p=0.003). In a genome-wide association analysis of thinness (n=1,471) vs severe obesity (n=1,456), we identified 10 loci previously associated with obesity, and demonstrate enrichment for established BMI-associated loci (pbinomial=3.05x10-5). Simulation analyses showed that different association results between the extremes were likely in agreement with additive effects across the BMI distribution, suggesting different effects on thinness and obesity could be due to their different degrees of extremeness. In further analyses, we detected a novel obesity and BMI-associated locus at PKHD1 (rs2784243, obese vs. thin p=5.99x10-6, obese vs. controls p=2.13x10-6 pBMI=2.3x10-13), associations at loci recently discovered with much larger sample sizes (e.g. FAM150B and PRDM6-CEP120), and novel variants driving associations at previously established signals (e.g. rs205262 at the SNRPC/C6orf106 locus and rs112446794 at the PRDM6-CEP120 locus). Our ability to replicate loci found with much larger sample sizes demonstrates the value of clinical extremes and suggest that characterisation of the genetics of thinness may provide a more nuanced understanding of the genetic architecture of body weight regulation and may inform the identification of potential anti-obesity targets.

64. The blood metabolome of incident kidney cancer: A case–control study nested within the MetKid consortium

Author

Guida, Florence, Tan, Vanessa Y., Corbin, Laura J., Smith-Byrne, Karl, Alcala, Karine, Langenberg, Claudia, Stewart, Isobel D., Butterworth, Adam S., Surendran, Praveen, Achaintre, David, Adamski, Jerzy, Amiano, Pilar, Bergmann, Manuela M., Bull, Caroline J., Dahm, Christina C., Gicquiau, Audrey, Giles, Graham G., Gunter, Marc J., Haller, Toomas, Langhammer, Arnulf, Larose, Tricia L., Ljungberg, Börje, Metspalu, Andres, Milne, Roger L., Muller, David C., Nøst, Therese H., Pettersen Sørgjerd, Elin, Prehn, Cornelia, Riboli, Elio, Rinaldi, Sabina, Rothwell, Joseph A., Scalbert, Augustin, Schmidt, Julie A., Severi, Gianluca, Sieri, Sabina, Vermeulen, Roel, Vincent, Emma E., Waldenberger, Melanie, Timpson, Nicholas J., and Johansson, Mattias

Subjects
2. Zero hunger, Medicine and health sciences, Research and analysis methods, Biology and life sciences, 16. Peace & justice, 3. Good health, Research Article
Abstract

Background: Excess bodyweight and related metabolic perturbations have been implicated in kidney cancer aetiology, but the specific molecular mechanisms underlying these relationships are poorly understood. In this study, we sought to identify circulating metabolites that predispose kidney cancer and to evaluate the extent to which they are influenced by body mass index (BMI). Methods and findings: We assessed the association between circulating levels of 1,416 metabolites and incident kidney cancer using pre-diagnostic blood samples from up to 1,305 kidney cancer case–control pairs from 5 prospective cohort studies. Cases were diagnosed on average 8 years after blood collection. We found 25 metabolites robustly associated with kidney cancer risk. In particular, 14 glycerophospholipids (GPLs) were inversely associated with risk, including 8 phosphatidylcholines (PCs) and 2 plasmalogens. The PC with the strongest association was PC ae C34:3 with an odds ratio (OR) for 1 standard deviation (SD) increment of 0.75 (95% confidence interval [CI]: 0.68 to 0.83, p = 2.6 × 10−8). In contrast, 4 amino acids, including glutamate (OR for 1 SD = 1.39, 95% CI: 1.20 to 1.60, p = 1.6 × 10−5), were positively associated with risk. Adjusting for BMI partly attenuated the risk association for some—but not all—metabolites, whereas other known risk factors of kidney cancer, such as smoking and alcohol consumption, had minimal impact on the observed associations. A mendelian randomisation (MR) analysis of the influence of BMI on the blood metabolome highlighted that some metabolites associated with kidney cancer risk are influenced by BMI. Specifically, elevated BMI appeared to decrease levels of several GPLs that were also found inversely associated with kidney cancer risk (e.g., −0.17 SD change [ßBMI] in 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2) levels per SD change in BMI, p = 3.4 × 10−5). BMI was also associated with increased levels of glutamate (ßBMI: 0.12, p = 1.5 × 10−3). While our results were robust across the participating studies, they were limited to study participants of European descent, and it will, therefore, be important to evaluate if our findings can be generalised to populations with different genetic backgrounds. Conclusions: This study suggests a potentially important role of the blood metabolome in kidney cancer aetiology by highlighting a wide range of metabolites associated with the risk of developing kidney cancer and the extent to which changes in levels of these metabolites are driven by BMI—the principal modifiable risk factor of kidney cancer.

65. Formalising recall by genotype as an efficient approach to detailed phenotyping and causal inference

Author

Corbin, Laura J, Tan, Vanessa Y, Hughes, David A, Wade, Kaitlin H, Paul, Dirk S, Tansey, Katherine E, Butcher, Frances, Dudbridge, Frank, Howson, Joanna M, Jallow, Momodou W, John, Catherine, Kingston, Nathalie, Lindgren, Cecilia M, O'Donavan, Michael, O'Rahilly, Stephen, Owen, Michael J, Palmer, Colin NA, Pearson, Ewan R, Scott, Robert A, Van Heel, David A, Whittaker, John, Frayling, Tim, Tobin, Martin D, Wain, Louise V, Smith, George Davey, Evans, David M, Karpe, Fredrik, McCarthy, Mark I, Danesh, John, Franks, Paul W, and Timpson, Nicholas J

Subjects
Causality, Molecular Epidemiology, Phenotype, Genotype, Risk Factors, Genetic Variation, Humans, United Kingdom, 3. Good health, Genome-Wide Association Study
Abstract

Detailed phenotyping is required to deepen our understanding of the biological mechanisms behind genetic associations. In addition, the impact of potentially modifiable risk factors on disease requires analytical frameworks that allow causal inference. Here, we discuss the characteristics of Recall-by-Genotype (RbG) as a study design aimed at addressing both these needs. We describe two broad scenarios for the application of RbG: studies using single variants and those using multiple variants. We consider the efficacy and practicality of the RbG approach, provide a catalogue of UK-based resources for such studies and present an online RbG study planner.

66. The blood metabolome of incident kidney cancer: A case-control study nested within the MetKid consortium

Author

Guida, Florence, Tan, Vanessa Y, Corbin, Laura J, Smith-Byrne, Karl, Alcala, Karine, Langenberg, Claudia, Stewart, Isobel D, Butterworth, Adam S, Surendran, Praveen, Achaintre, David, Adamski, Jerzy, Amiano, Pilar, Bergmann, Manuela M, Bull, Caroline J, Dahm, Christina C, Gicquiau, Audrey, Giles, Graham G, Gunter, Marc J, Haller, Toomas, Langhammer, Arnulf, Larose, Tricia L, Ljungberg, Börje, Metspalu, Andres, Milne, Roger L, Muller, David C, Nøst, Therese H, Pettersen Sørgjerd, Elin, Prehn, Cornelia, Riboli, Elio, Rinaldi, Sabina, Rothwell, Joseph A, Scalbert, Augustin, Schmidt, Julie A, Severi, Gianluca, Sieri, Sabina, Vermeulen, Roel, Vincent, Emma E, Waldenberger, Melanie, Timpson, Nicholas J, and Johansson, Mattias

Subjects
2. Zero hunger, 3. Good health
Abstract

BackgroundExcess bodyweight and related metabolic perturbations have been implicated in kidney cancer aetiology, but the specific molecular mechanisms underlying these relationships are poorly understood. In this study, we sought to identify circulating metabolites that predispose kidney cancer and to evaluate the extent to which they are influenced by body mass index (BMI).Methods and findingsWe assessed the association between circulating levels of 1,416 metabolites and incident kidney cancer using pre-diagnostic blood samples from up to 1,305 kidney cancer case-control pairs from 5 prospective cohort studies. Cases were diagnosed on average 8 years after blood collection. We found 25 metabolites robustly associated with kidney cancer risk. In particular, 14 glycerophospholipids (GPLs) were inversely associated with risk, including 8 phosphatidylcholines (PCs) and 2 plasmalogens. The PC with the strongest association was PC ae C34:3 with an odds ratio (OR) for 1 standard deviation (SD) increment of 0.75 (95% confidence interval [CI]: 0.68 to 0.83, p = 2.6 × 10-8). In contrast, 4 amino acids, including glutamate (OR for 1 SD = 1.39, 95% CI: 1.20 to 1.60, p = 1.6 × 10-5), were positively associated with risk. Adjusting for BMI partly attenuated the risk association for some-but not all-metabolites, whereas other known risk factors of kidney cancer, such as smoking and alcohol consumption, had minimal impact on the observed associations. A mendelian randomisation (MR) analysis of the influence of BMI on the blood metabolome highlighted that some metabolites associated with kidney cancer risk are influenced by BMI. Specifically, elevated BMI appeared to decrease levels of several GPLs that were also found inversely associated with kidney cancer risk (e.g., -0.17 SD change [ßBMI] in 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2) levels per SD change in BMI, p = 3.4 × 10-5). BMI was also associated with increased levels of glutamate (ßBMI: 0.12, p = 1.5 × 10-3). While our results were robust across the participating studies, they were limited to study participants of European descent, and it will, therefore, be important to evaluate if our findings can be generalised to populations with different genetic backgrounds.ConclusionsThis study suggests a potentially important role of the blood metabolome in kidney cancer aetiology by highlighting a wide range of metabolites associated with the risk of developing kidney cancer and the extent to which changes in levels of these metabolites are driven by BMI-the principal modifiable risk factor of kidney cancer.

67. Higher body mass index raises immature platelet count: potential contribution to obesity-related thrombosis

Author

Lucy J. Goudswaard, Laura J. Corbin, Kate L. Burley, Andrew Mumford, Parsa Akbari, Nicole Soranzo, Adam S. Butterworth, Nicholas A. Watkins, Dimitri J. Pournaras, Jessica Harris, Nicholas J. Timpson, Ingeborg Hers, Goudswaard, Lucy J [0000-0003-1481-6871], Corbin, Laura J [0000-0002-4032-9500], Burley, Kate L [0000-0002-1569-5007], Mumford, Andrew [0000-0002-5523-511X], Soranzo, Nicole [0000-0003-1095-3852], Butterworth, Adam S [0000-0002-6915-9015], Hers, Ingeborg [0000-0002-6467-5715], and Apollo - University of Cambridge Repository

Subjects
Blood Platelets, Aggregation, obesity, Platelet Count, parasitic diseases, Mendelian randomization, Humans, epidemiology, Thrombosis, Hematology, General Medicine, immature platelets, Body Mass Index
Abstract

Higher body mass index (BMI) is a risk factor for thrombosis. Platelets are essential for hemostasis but contribute to thrombosis when activated pathologically. We hypothesized that higher BMI leads to changes in platelet characteristics, thereby increasing thrombotic risk. The effect of BMI on platelet traits (measured by Sysmex) was explored in 33 388 UK blood donors (INTERVAL study). Linear regression showed that higher BMI was positively associated with greater plateletcrit (PCT), platelet count (PLT), immature platelet count (IPC), and side fluorescence (SFL, a measure of mRNA content used to derive IPC). Mendelian randomization (MR), applied to estimate a causal effect with BMI proxied by a genetic risk score, provided causal estimates for a positive effect of BMI on both SFL and IPC, but there was little evidence for a causal effect of BMI on PCT or PLT. Follow-up analyses explored the functional relevance of platelet characteristics in a pre-operative cardiac cohort (COPTIC). Linear regression provided observational evidence for a positive association between IPC and agonist-induced whole blood platelet aggregation. Results indicate that higher BMI raises the number of immature platelets, which is associated with greater whole blood platelet aggregation in a cardiac cohort. Higher IPC could therefore contribute to obesity-related thrombosis.

Published
2022

68. The trans-ancestral genomic architecture of glycemic traits

Author

Albertine J. Oldehinkel, Wieland Kiess, Xueling Sim, Norihiro Kato, Philippe Froguel, Astrid van Hylckama Vlieg, Josée Dupuis, Nanette R. Lee, Symen Ligthart, Harry Campbell, Marta E. Alarcón-Riquelme, P. Eline Slagboom, Massimo Mangino, Tian Xie, Niek Verweij, James B. Meigs, Chaolong Wang, Michael Y. Tsai, Erik Ingelsson, Colin N. A. Palmer, Erik B. van den Akker, Fumihiko Matsuda, Rainer Rauramaa, Yi-Cheng Chang, Lars Lind, Stefan R. Bornstein, Mandy Vogel, Sven Bergmann, Ya X. Wang, Ching-Ti Liu, Annette Schürmann, Michael Boehnke, David J. Porteous, Kazuya Setoh, Qibin Qi, Ayse Demirkan, Francesco Cucca, Allan Linneberg, Claire J. Steves, Jun Liu, Leslie A. Lange, Noël P. Burtt, Diana Kuh, Cassandra N. Spracklen, Ken K. Ong, Charumathi Sabanayagam, Jost B. Jonas, Ele Ferrannini, Lawrence J. Beilin, Qing Duan, Blair H. Smith, Isobel D. Stewart, Alexander P. Reiner, Simon P. Mooijaart, Tim D. Spector, Paul W. Franks, E. Shyong Tai, Mark I. McCarthy, Anna L. Gloyn, D.I. Boomsma, Dennis Raven, Nicholas J. Timpson, Rona J. Strawbridge, George Dedoussis, Susan Redline, Jaeyoung Hong, Harald Grallert, Jagadish Vangipurapu, Rico Rueedi, Diane M. Becker, Marian Beekman, Claudia P. Cabrera, Johannes Waage, Jin Fang Chai, Yii-Der Ida Chen, Graciela E. Delgado, Thibaud S. Boutin, Yang Hai, Yoriko Heianza, Wei Zhao, Andres Metspalu, Tien Yin Wong, Mila Desi Anasanti, Inger Njølstad, Hans Bisgaard, Valeriya Lyssenko, Denis Rybin, Wanqing Wen, Torben Hansen, James F. Wilson, Sameline Grimsgaard, Annette Peters, Michele K. Evans, Damia Noce, Sarah C. Nelson, May E. Montasser, Nan Wang, Geltrude Mingrone, Gudny Eiriksdottir, Nicholas J. Wareham, Fouad Kandeel, Linda S. Adair, Kelvin Lam, Jaana Lindström, Eco J. C. de Geus, Debbie A Lawlor, Sara M. Willems, Xu Lin, Harold Snieder, Matt J. Neville, Naveed Sattar, Chelsea K. Raulerson, Paul M. Ridker, Jer-Yuarn Wu, Weihua Zhang, H. Janaka de Silva, Jana V. van Vliet-Ostaptchouk, Elena Tremoli, Toru Nabika, Jing Hua Zhao, Vilmundur Gudnason, Tao Huang, Robert C. Kaplan, Sohee Han, Mohammad Hadi Zafarmand, Aaron Leong, Yen-Feng Chiu, Kumaraswamy Naidu Chitrala, Ivana Kolcic, Franco Giulianini, Tao Wang, Lu Qi, Stephan J. L. Bakker, Laura J Corbin, Zoltán Kutalik, Bruna Gigante, Willa A. Hsueh, Peter J. van der Most, Tin Louie, Yujie Wang, Stella Trompet, Fernando Rivideneira, Yasumasa Ohyagi, Lynne E. Wagenknecht, Jerry L. Nadler, Michael Stumvoll, Mark O. Goodarzi, Sahoko Ichihara, Jeffrey R. O'Connell, Tomohiro Katsuya, Giorgio Pistis, Alice Stanton, Sirkka Keinänen-Kiukaanniemi, Momoko Horikoshi, Honglan Li, Tanja G. M. Vrijkotte, Caroline Hayward, Karen L. Mohlke, Carola Marzi, Girish N. Nadkarni, Laura J. Rasmussen-Torvik, Alain G. Bertoni, Andrew R. Wood, Annique Claringbould, Mi Yeong Hwang, Hugh Watkins, Heikki A. Koistinen, Mattias Frånberg, Jani Heikkinen, Elizabeth Selvin, Donald W. Bowden, Abbas Dehghan, Christian Fuchsberger, Audrey Y. Chu, Kent D. Taylor, Katherine A. Kentistou, Johanna Kuusisto, Jingyi Tan, Huaixing Li, Eric Boerwinkle, Catharina A. Hartman, Archie Campbell, Kari E. North, Oluf Pedersen, Sölve Elmståhl, Emil V. R. Appel, Chang-Hsun Hsieh, Dennis O. Mook-Kanamori, Rob M. van Dam, Pontiano Kaleebu, Corri Black, Jennifer A. Brody, Bengt Sennblad, Shaofeng Huo, M. Larissa Avilés-Santa, Ruth J. F. Loos, Patricia B. Munroe, Chien-Hsiun Chen, Liang Sun, Zorayr Arzumanyan, Rebecca Rohde, Yasuharu Tabara, Albert V. Smith, Betina H. Thuesen, Niels Grarup, Jorgen Engmann, Tatijana Zemunik, M. Arfan Ikram, Marit E. Jørgensen, Christian Herder, Ching-Yu Cheng, Serena Sanna, Damiano Baldassarre, Tarunveer S. Ahluwalia, Mark J. Caulfield, Anne Ndungu, Carl D. Langefeld, Lisa R. Yanek, Luigi Ferrucci, Ananda R. Wickremasinghe, Raymond Noordam, Trevor A. Mori, Tom Wilsgaard, Mika Kivimäki, Rita R. Kalyani, Alan B. Zonderman, Veronique Vitart, Patricia A. Peyser, Shuiqing Lai, Richa Saxena, Li-Ching Chang, Karin Leander, Wei Huang, Peter Vollenweider, Tanya M. Teslovich, Ying Wu, Shufa Du, Brian E. Cade, Patrik K. E. Magnusson, John C. Chambers, Stephen C. J. Parker, Tamar Sofer, Winfried März, Sharon L.R. Kardia, Peter K. Joshi, Neil R. Robertson, Anny H. Xiang, Fumihiko Takeuchi, N. Amin, Jouke-Jan Hottenga, Carol A. Wang, Stefan Gustafsson, Jung Ho Gong, Penny Gordon-Larsen, Yu-Tang Gao, Abhishek Nag, Gonneke Willemsen, Michael A. Province, Aliki-Eleni Farmaki, Segun Fatumo, Antje Körner, Pim van der Harst, Marie Loh, Kei Hang Katie Chan, Gonçalo R. Abecasis, Nicholette D. Palmer, Simin Liu, Ishminder K. Kooner, Javier Gayán, Arne Astrup, Laura J. Scott, Erwin P. Bottinger, Andrew Wong, Inga Prokopenko, Ping An, Markku Laakso, Matthias Blüher, Susan R. Heckbert, Thomas A. Buchanan, Tatsuaki Matsubara, Andrew P. Morris, Brian H. Chen, Kristi Läll, Teresa Tusie, Timo A. Lakka, Jie Yao, Michael Preuss, Teemu Kuulasmaa, Carlos Lorenzo, Stephen S. Rich, Marie Lauzon, Laura M. Raffield, Pankow S. James, Takahisa Kawaguchi, Kathleen A. Ryan, Wei Zheng, Igor Rudan, Thomas Sparsø, Hugoline G. de Haan, Sandosh Padmanabhan, Richard M. Watanabe, Alicia Huerta-Chagoya, Anette P. Gjesing, Andrew A. Hicks, Richard N. Bergman, Mitsuhiro Yokota, Heather M. Stringham, Bruce M. Psaty, Jian'an Luan, Anuj Goel, Eleanor Wheeler, Masahiro Nakatochi, Young-Jin Kim, Xiao-Ou Shu, Mickaël Canouil, Robert A. Scott, Marika Kaakinen, Mari Nelis, Adolfo Correa, Jaspal S. Kooner, Michiya Igase, Anubha Mahajan, Peter E. H. Schwarz, Craig E. Pennell, Claudia Schurmann, Xiaoran Chai, Ji Chen, Lori L. Bonnycastle, Peter S. Sever, Thorkild I. A. Sørensen, André G. Uitterlinden, Ilja M. Nolte, Gaëlle Marenne, Timothy M. Frayling, Bong-Jo Kim, Kerrin S. Small, Cecilia M. Lindgren, Bernhard O. Böhm, Shih-Yi Lin, Katharina E. Schraut, Cornelia M. van Duijn, Sanghoon Moon, Mark Walker, Chiea Chuen Khor, Ruifang Li-Gao, Qiuyin Cai, Neil Schneiderman, Ko Willems van Dijk, Ozren Polasek, W. Craig Johnson, Dermot F. Reilly, Inês Barroso, Anke Tönjes, Manjinder S. Sandhu, Wen B. Wei, Jose C. Florez, Lorraine Southam, Leif Groop, Lawrence F. Bielak, Peter Kovacs, Jianjun Liu, Jouko Saramies, Helen R. Warren, Man Li, Daniel I. Chasman, Eleftheria Zeggini, Xiaoshuai Zhang, Loic Yengo, Shi Jinxiu, Jirong Long, Xiuqing Guo, Meena Kumari, Leslie J. Raffel, Jill M. Norris, Henrik Vestergaard, Jing He, Peter P. Pramstaller, Diana van Heemst, Kevin Sandow, Marjo-Ritta Jarvelin, Carlos A. Aguilar-Salinas, Peitao Wu, Hortensia Moreno-Macías, Jerome I. Rotter, Kathryn Roll, Frits R. Rosendaal, Bernardo L. Horta, Heming Wang, Fernando Pires Hartwig, Richard A. Jensen, Matti Uusitupa, Rozenn N. Lemaitre, Paul R. H. J. Timmers, Timo Saaristo, Jaakko Tuomilehto, Reedik Mägi, Debashree Ray, J. Wouter Jukema, Claudia Langenberg, Marcus E. Kleber, Francis S. Collins, Klaus Bønnelykke, Lenore J. Launer, Arushi Varshney, Anders Hamsten, European Commission, Génétique, génomique fonctionnelle et biotechnologies (UMR 1078) (GGB), Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO)-Université de Brest (UBO)-EFS-Institut National de la Santé et de la Recherche Médicale (INSERM), Sanger Institute, Wellcome Trust, Marenne, Gaëlle [0000-0002-4363-7170], Varshney, Arushi [0000-0001-9177-9707], Corbin, Laura J [0000-0002-4032-9500], Parker, Stephen CJ [0000-0001-8122-0117], Langenberg, Claudia [0000-0002-5017-7344], Wheeler, Eleanor [0000-0002-8616-6444], Morris, Andrew P [0000-0002-6805-6014], Barroso, Inês [0000-0001-5800-4520], Apollo - University of Cambridge Repository, Lifelines Cohort Study, Meta-Analysis of Glucose and Insulin-related Traits Consortium (MAGIC), de Haan, H.G., van den Akker, E., van der Most, P.J., de Geus, EJC, van Dam, R.M., van Heemst, D., van Hylckama Vlieg, A., van Willems van Dijk, K., de Silva, H.J., van der Harst, P., van Duijn, C., Centre of Excellence in Complex Disease Genetics, HUS Abdominal Center, Institute for Molecular Medicine Finland, Leif Groop Research Group, HUS Internal Medicine and Rehabilitation, Department of Medicine, Department of Biochemistry and Developmental Biology, Helsinki University Hospital Area, University of Helsinki, Clinicum, Department of Public Health, Biological Psychology, APH - Health Behaviors & Chronic Diseases, APH - Personalized Medicine, Physiology, AMS - Musculoskeletal Health, AMS - Tissue Function & Regeneration, APH - Mental Health, Nutrition and Health, APH - Methodology, Epidemiology and Data Science, ACS - Atherosclerosis & ischemic syndromes, APH - Aging & Later Life, ARD - Amsterdam Reproduction and Development, Public and occupational health, Epidemiology, Internal Medicine, Life Course Epidemiology (LCE), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Groningen Institute for Organ Transplantation (GIOT), Groningen Kidney Center (GKC), Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Center for Liver, Digestive and Metabolic Diseases (CLDM), and Cardiovascular Centre (CVC)

Subjects
Blood Glucose, Disease risk, Multifactorial Inheritance, Glycated Hemoglobin A, [SDV]Life Sciences [q-bio], Meta-Analysis of Glucose and Insulin-related Traits Consortium (MAGIC), LOCI, Genome-wide association study, Type 2 diabetes, VARIANTS, GLUCOSE, Epigenesis, Genetic, chemistry.chemical_compound, 0302 clinical medicine, Mechanisms, WIDE ASSOCIATION, genetics, Gene-expression, HEMOGLOBIN, ComputingMilieux_MISCELLANEOUS, 11 Medical and Health Sciences, GENE-EXPRESSION, Genetics, Genetics & Heredity, 0303 health sciences, INSULIN-RESISTANCE, Genome, Loci, 1184 Genetics, developmental biology, physiology, Variants, ALSPAC, Physical Chromosome Mapping, Life Sciences & Biomedicine, Human, Quantitative Trait Loci, Wide association study, Biology, Quantitative trait locus, Article, White People, diseases, MECHANISMS, Quantitative Trait, 03 medical and health sciences, Insulin resistance, Quantitative Trait, Heritable, SDG 3 - Good Health and Well-being, Genetic, Lifelines Cohort Study, Diabetes mellitus, medicine, Humans, Hemoglobin, Heritable, METAANALYSIS, Alleles, 030304 developmental biology, Genetic association, Glycemic, Glycated Hemoglobin, Science & Technology, Genome, Human, Whites, Gene Expression Profiling, DISEASE RISK, Settore MED/13 - ENDOCRINOLOGIA, Insulin-resistance, 06 Biological Sciences, medicine.disease, Glucose, chemistry, Blood Glucose/genetics, European Continental Ancestry Group/genetics, Genome-Wide Association Study, Glycated Hemoglobin A/metabolism, Multifactorial Inheritance/genetics, Quantitative Trait Loci/genetics, Glycated hemoglobin, 030217 neurology & neurosurgery, Meta analysis, Epigenesis, Developmental Biology
Abstract

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P-8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.

Published
2021

69. Effects of adiposity on the human plasma proteome: Observational and Mendelian randomization estimates

Author

Laura J Corbin, Nicholas J. Timpson, Emanuele Di Angelantonio, Nicole Soranzo, Joshua A. Bell, David A. Hughes, George Davey Smith, Willem H. Ouwehand, Lucy J Goudswaard, Nicholas A. Watkins, Klaudia Walter, Ingeborg Hers, Adam S. Butterworth, David J. Roberts, John Danesh, Goudswaard, Lucy J [0000-0003-1481-6871], Hughes, David A [0000-0002-9644-8998], Corbin, Laura J [0000-0002-4032-9500], Walter, Klaudia [0000-0003-4448-0301], Ouwehand, Willem H [0000-0002-7744-1790], Butterworth, Adam S [0000-0002-6915-9015], Timpson, Nicholas J [0000-0002-7141-9189], and Apollo - University of Cambridge Repository

Subjects
Adult, Male, Proteome, Genome-wide association study, Disease, Bioinformatics, Body Mass Index, Cohort Studies, symbols.namesake, Surveys and Questionnaires, Mendelian randomization, Human proteome project, Humans, Medicine, Prospective Studies, Adiposity, business.industry, nutritional and metabolic diseases, Mendelian Randomization Analysis, Middle Aged, Bonferroni correction, symbols, Female, Observational study, business, Body mass index
Abstract

BACKGROUND: Variation in adiposity is associated with cardiometabolic disease outcomes, but mechanisms leading from this exposure to disease are unclear. This study aimed to estimate effects of body mass index (BMI) on an extensive set of circulating proteins. METHODS: We used SomaLogic proteomic data from up to 2737 healthy participants from the INTERVAL study. Associations between self-reported BMI and 3622 unique plasma proteins were explored using linear regression. These were complemented by Mendelian randomisation (MR) analyses using a genetic risk score (GRS) comprised of 654 BMI-associated polymorphisms from a recent genome-wide association study (GWAS) of adult BMI. A disease enrichment analysis was performed using DAVID Bioinformatics 6.8 for proteins which were altered by BMI. RESULTS: Observationally, BMI was associated with 1576 proteins (P < 1.4 × 10-5), with particularly strong evidence for a positive association with leptin and fatty acid-binding protein-4 (FABP4), and a negative association with sex hormone-binding globulin (SHBG). Observational estimates were likely confounded, but the GRS for BMI did not associate with measured confounders. MR analyses provided evidence for a causal relationship between BMI and eight proteins including leptin (0.63 standard deviation (SD) per SD BMI, 95% CI 0.48-0.79, P = 1.6 × 10-15), FABP4 (0.64 SD per SD BMI, 95% CI 0.46-0.83, P = 6.7 × 10-12) and SHBG (-0.45 SD per SD BMI, 95% CI -0.65 to -0.25, P = 1.4 × 10-5). There was agreement in the magnitude of observational and MR estimates (R2 = 0.33) and evidence that proteins most strongly altered by BMI were enriched for genes involved in cardiovascular disease. CONCLUSIONS: This study provides evidence for a broad impact of adiposity on the human proteome. Proteins strongly altered by BMI include those involved in regulating appetite, sex hormones and inflammation; such proteins are also enriched for cardiovascular disease-related genes. Altogether, results help focus attention onto new proteomic signatures of obesity-related disease.

Published
2020

70. Formalising recall by genotype as an efficient approach to detailed phenotyping and causal inference

Author

John C. Whittaker, Frank Dudbridge, Nicholas J. Timpson, Louise V. Wain, David A. van Heel, Laura J Corbin, Nathalie Kingston, Fredrik Karpe, George Davey Smith, Kaitlin H Wade, David M. Evans, Katherine E. Tansey, Martin D. Tobin, Joanna M. M. Howson, David A. Hughes, Catherine John, Paul W. Franks, John Danesh, Dirk S. Paul, Robert A. Scott, Ewan R. Pearson, Michael John Owen, Mark I. McCarthy, Vanessa Y Tan, Momodou W. Jallow, Colin N. A. Palmer, Stephen O'Rahilly, Timothy M. Frayling, Michael O'Donavan, Frances Butcher, Cecilia M. Lindgren, Corbin, Laura J [0000-0002-4032-9500], Paul, Dirk S [0000-0002-8230-0116], O'Donavan, Michael [0000-0001-7073-2379], O'Rahilly, Stephen [0000-0003-2199-4449], Owen, Michael J [0000-0003-4798-0862], Palmer, Colin NA [0000-0002-6415-6560], van Heel, David A [0000-0002-0637-2265], Tobin, Martin D [0000-0002-3596-7874], Wain, Louise V [0000-0003-4951-1867], McCarthy, Mark I [0000-0002-4393-0510], and Apollo - University of Cambridge Repository

Subjects
0301 basic medicine, Genotype, Computer science, Science, General Physics and Astronomy, Genome-wide association study, Review Article, 030105 genetics & heredity, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, Risk Factors, Humans, lcsh:Science, Medicinsk genetik, Molecular Epidemiology, Multidisciplinary, Recall, Genetic Variation, General Chemistry, Causality, Data science, United Kingdom, 030104 developmental biology, Phenotype, Causal inference, lcsh:Q, ICEP, Medical Genetics, Genome-Wide Association Study
Abstract

Detailed phenotyping is required to deepen our understanding of the biological mechanisms behind genetic associations. In addition, the impact of potentially modifiable risk factors on disease requires analytical frameworks that allow causal inference. Here, we discuss the characteristics of Recall-by-Genotype (RbG) as a study design aimed at addressing both these needs. We describe two broad scenarios for the application of RbG: studies using single variants and those using multiple variants. We consider the efficacy and practicality of the RbG approach, provide a catalogue of UK-based resources for such studies and present an online RbG study planner., Recall-by-Genotype (RbG) is an approach to recall participants from genetic studies based on their specific genotype for further, more extensive phenotyping. Here, the authors discuss examples of RbG as well as practical and ethical considerations and provide an online tool to aid in designing RbG studies.

Published
2018

71. A reference panel of 64,976 haplotypes for genotype imputation

Author

Steven A. McCarroll, Gerome Breen, Nicole Soranzo, Sayantan Das, Ilaria Gandin, Lukas Forer, David Schlessinger, Jonathan Marchini, Francesco Cucca, Anand Swaroop, Seppo Koskinen, Aliki-Eleni Farmaki, Alan Kwong, Gonçalo R. Abecasis, Marcus Dörr, Yang Luo, Andrew T. Chan, Andrea Angius, P. Eline Slagboom, Petr Danecek, Cinzia Sala, Ulrike Peters, Marcus A. Tuke, D.I. Boomsma, Jan H. Veldink, Nicholas J. Timpson, Scott I. Vrieze, Aarno Palotie, Matt McGue, Wouter van Rheenen, Melvin G. McInnis, Shawn Levy, George Dedoussis, Richard Durbin, Matthias Kretzler, Ross M. Fraser, C Sidore, David Melzer, Eleftheria Zeggini, Andrew R. Wood, Veikko Salomaa, Oddgeir L. Holmen, Alexander Teumer, Hyun Min Kang, James F. Wilson, Cristen J. Willer, Laura J. Scott, Richard M. Myers, George Davey Smith, Andrew T. Hattersley, Matthias Nauck, Matthew G. Sampson, Christian Fuchsberger, Josine L. Min, Leonard H. van den Berg, Thomas Meitinger, David Altshuler, John B. Vincent, Kevin Sharp, Jaakko Tuomilehto, Daniela Toniolo, Cornelia M. van Duijn, Samuli Ripatti, Stacey Gabriel, Michael Boehnke, Annelot M. Dekker, Francis S. Collins, He Zhang, Klaudia Walter, Deborah A. Nickerson, Nicola Pirastu, Morris A. Swertz, Kari Branham, Luigi Ferrucci, Sai Chen, Fabio Busonero, Paul I.W. de Bakker, Kristian Hveem, Charles Kooperberg, Michela Traglia, Warren W. Kretzschmar, Leif Groop, Tabitha A. Harrison, Sebastian Schoenherr, J. Brent Richards, Uwe Völker, Shane A. McCarthy, Harry Campbell, Massimo Mezzavilla, Chad M. Brummett, Mark I. McCarthy, Tim D. Spector, Dwight Stambolian, Massimiliano Cocca, Emily Y. Chew, Carlos N. Pato, Jeffrey C. Barrett, Christopher E. Gillies, William G. Iacono, Karen L. Mohlke, Olivier Delaneau, Laura J Corbin, Cisca Wijmenga, Anubha Mahajan, Timothy M. Frayling, James Lee, Michele T. Pato, Carl A. Anderson, Arthur Gilly, Kerrin S. Small, Paolo Gasparini, Biological Psychology, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, McCarthy, Shane [0000-0002-2715-4187], Das, Sayantan [0000-0001-6346-1590], Kretzschmar, Warren [0000-0002-2575-0807], Durbin, Richard [0000-0002-9130-1006], Apollo - University of Cambridge Repository, Epidemiology, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Mccarthy, Shane, Das, Sayantan, Kretzschmar, Warren, Delaneau, Olivier, Wood, Andrew R, Teumer, Alexander, Kang, Hyun Min, Fuchsberger, Christian, Danecek, Petr, Sharp, Kevin, Luo, Yang, Sidore, Carlo, Kwong, Alan, Timpson, Nichola, Koskinen, Seppo, Vrieze, Scott, Scott, Laura J, Zhang, He, Mahajan, Anubha, Veldink, Jan, Peters, Ulrike, Pato, Carlo, van Duijn, Cornelia M, Gillies, Christopher E, Gandin, Ilaria, Mezzavilla, Massimo, Gilly, Arthur, Cocca, Massimiliano, Traglia, Michela, Angius, Andrea, Barrett, Jeffrey C, Boomsma, Dorrett, Branham, Kari, Breen, Gerome, Brummett, Chad M, Busonero, Fabio, Campbell, Harry, Chan, Andrew, Chen, Sai, Chew, Emily, Collins, Francis S, Corbin, Laura J, Smith, George Davey, Dedoussis, George, Dorr, Marcu, Farmaki, Aliki Eleni, Ferrucci, Luigi, Forer, Luka, Fraser, Ross M, Gabriel, Stacey, Levy, Shawn, Groop, Leif, Harrison, Tabitha, Hattersley, Andrew, Holmen, Oddgeir L, Hveem, Kristian, Kretzler, Matthia, Lee, James C, Mcgue, Matt, Meitinger, Thoma, Melzer, David, Min, Josine L, Mohlke, Karen L, Vincent, John B, Nauck, Matthia, Nickerson, Deborah, Palotie, Aarno, Pato, Michele, Pirastu, Nicola, Mcinnis, Melvin, Richards, J. Brent, Sala, Cinzia, Salomaa, Veikko, Schlessinger, David, Schoenherr, Sebastian, Slagboom, P. Eline, Small, Kerrin, Spector, Timothy, Stambolian, Dwight, Tuke, Marcu, Tuomilehto, Jaakko, van den Berg, Leonard H, van Rheenen, Wouter, Volker, Uwe, Wijmenga, Cisca, Toniolo, Daniela, Zeggini, Eleftheria, Gasparini, Paolo, Sampson, Matthew G, Wilson, James F, Frayling, Timothy, de Bakker, Paul I. W, Swertz, Morris A, Mccarroll, Steven, Kooperberg, Charle, Dekker, Annelot, Altshuler, David, Willer, Cristen, Iacono, William, Ripatti, Samuli, Soranzo, Nicole, Walter, Klaudia, Swaroop, Anand, Cucca, Francesco, Anderson, Carl A, Myers, Richard M, Boehnke, Michael, Mccarthy, Mark I, and Durbin, Richard

Subjects
0301 basic medicine, Netherlands Twin Register (NTR), Genotype, Population, Genome-wide association study, Single-nucleotide polymorphism, Computational biology, Biology, Polymorphism, Single Nucleotide, SEQUENCE, Article, 03 medical and health sciences, 0302 clinical medicine, Reference Values, Journal Article, Genetics, Humans, GENOME-WIDE ASSOCIATION, education, Alleles, POPULATION, 030304 developmental biology, Genetic association, Whole genome sequencing, education.field_of_study, Internet, 0303 health sciences, Genotype imputation, Medicine (all), Haplotype, ALSPAC, Minor allele frequency, 030104 developmental biology, Genetic Techniques, Haplotypes, RARE VARIANTS, 030217 neurology & neurosurgery, Imputation (genetics), Genome-Wide Association Study
Abstract

We describe a reference panel of 64,976 human haplotypes at 39,235,157 SNPs constructed using whole genome sequence data from 20 studies of predominantly European ancestry. Using this resource leads to accurate genotype imputation at minor allele frequencies as low as 0.1%, a large increase in the number of SNPs tested in association studies and can help to discover and refine causal loci. We describe remote server resources that allow researchers to carry out imputation and phasing consistently and efficiently.

Published
2015

72. The trans-ancestral genomic architecture of glycemic traits.

Author

Chen J, Spracklen CN, Marenne G, Varshney A, Corbin LJ, Luan J, Willems SM, Wu Y, Zhang X, Horikoshi M, Boutin TS, Mägi R, Waage J, Li-Gao R, Chan KHK, Yao J, Anasanti MD, Chu AY, Claringbould A, Heikkinen J, Hong J, Hottenga JJ, Huo S, Kaakinen MA, Louie T, März W, Moreno-Macias H, Ndungu A, Nelson SC, Nolte IM, North KE, Raulerson CK, Ray D, Rohde R, Rybin D, Schurmann C, Sim X, Southam L, Stewart ID, Wang CA, Wang Y, Wu P, Zhang W, Ahluwalia TS, Appel EVR, Bielak LF, Brody JA, Burtt NP, Cabrera CP, Cade BE, Chai JF, Chai X, Chang LC, Chen CH, Chen BH, Chitrala KN, Chiu YF, de Haan HG, Delgado GE, Demirkan A, Duan Q, Engmann J, Fatumo SA, Gayán J, Giulianini F, Gong JH, Gustafsson S, Hai Y, Hartwig FP, He J, Heianza Y, Huang T, Huerta-Chagoya A, Hwang MY, Jensen RA, Kawaguchi T, Kentistou KA, Kim YJ, Kleber ME, Kooner IK, Lai S, Lange LA, Langefeld CD, Lauzon M, Li M, Ligthart S, Liu J, Loh M, Long J, Lyssenko V, Mangino M, Marzi C, Montasser ME, Nag A, Nakatochi M, Noce D, Noordam R, Pistis G, Preuss M, Raffield L, Rasmussen-Torvik LJ, Rich SS, Robertson NR, Rueedi R, Ryan K, Sanna S, Saxena R, Schraut KE, Sennblad B, Setoh K, Smith AV, Sparsø T, Strawbridge RJ, Takeuchi F, Tan J, Trompet S, van den Akker E, van der Most PJ, Verweij N, Vogel M, Wang H, Wang C, Wang N, Warren HR, Wen W, Wilsgaard T, Wong A, Wood AR, Xie T, Zafarmand MH, Zhao JH, Zhao W, Amin N, Arzumanyan Z, Astrup A, Bakker SJL, Baldassarre D, Beekman M, Bergman RN, Bertoni A, Blüher M, Bonnycastle LL, Bornstein SR, Bowden DW, Cai Q, Campbell A, Campbell H, Chang YC, de Geus EJC, Dehghan A, Du S, Eiriksdottir G, Farmaki AE, Frånberg M, Fuchsberger C, Gao Y, Gjesing AP, Goel A, Han S, Hartman CA, Herder C, Hicks AA, Hsieh CH, Hsueh WA, Ichihara S, Igase M, Ikram MA, Johnson WC, Jørgensen ME, Joshi PK, Kalyani RR, Kandeel FR, Katsuya T, Khor CC, Kiess W, Kolcic I, Kuulasmaa T, Kuusisto J, Läll K, Lam K, Lawlor DA, Lee NR, Lemaitre RN, Li H, Lin SY, Lindström J, Linneberg A, Liu J, Lorenzo C, Matsubara T, Matsuda F, Mingrone G, Mooijaart S, Moon S, Nabika T, Nadkarni GN, Nadler JL, Nelis M, Neville MJ, Norris JM, Ohyagi Y, Peters A, Peyser PA, Polasek O, Qi Q, Raven D, Reilly DF, Reiner A, Rivideneira F, Roll K, Rudan I, Sabanayagam C, Sandow K, Sattar N, Schürmann A, Shi J, Stringham HM, Taylor KD, Teslovich TM, Thuesen B, Timmers PRHJ, Tremoli E, Tsai MY, Uitterlinden A, van Dam RM, van Heemst D, van Hylckama Vlieg A, van Vliet-Ostaptchouk JV, Vangipurapu J, Vestergaard H, Wang T, Willems van Dijk K, Zemunik T, Abecasis GR, Adair LS, Aguilar-Salinas CA, Alarcón-Riquelme ME, An P, Aviles-Santa L, Becker DM, Beilin LJ, Bergmann S, Bisgaard H, Black C, Boehnke M, Boerwinkle E, Böhm BO, Bønnelykke K, Boomsma DI, Bottinger EP, Buchanan TA, Canouil M, Caulfield MJ, Chambers JC, Chasman DI, Chen YI, Cheng CY, Collins FS, Correa A, Cucca F, de Silva HJ, Dedoussis G, Elmståhl S, Evans MK, Ferrannini E, Ferrucci L, Florez JC, Franks PW, Frayling TM, Froguel P, Gigante B, Goodarzi MO, Gordon-Larsen P, Grallert H, Grarup N, Grimsgaard S, Groop L, Gudnason V, Guo X, Hamsten A, Hansen T, Hayward C, Heckbert SR, Horta BL, Huang W, Ingelsson E, James PS, Jarvelin MR, Jonas JB, Jukema JW, Kaleebu P, Kaplan R, Kardia SLR, Kato N, Keinanen-Kiukaanniemi SM, Kim BJ, Kivimaki M, Koistinen HA, Kooner JS, Körner A, Kovacs P, Kuh D, Kumari M, Kutalik Z, Laakso M, Lakka TA, Launer LJ, Leander K, Li H, Lin X, Lind L, Lindgren C, Liu S, Loos RJF, Magnusson PKE, Mahajan A, Metspalu A, Mook-Kanamori DO, Mori TA, Munroe PB, Njølstad I, O'Connell JR, Oldehinkel AJ, Ong KK, Padmanabhan S, Palmer CNA, Palmer ND, Pedersen O, Pennell CE, Porteous DJ, Pramstaller PP, Province MA, Psaty BM, Qi L, Raffel LJ, Rauramaa R, Redline S, Ridker PM, Rosendaal FR, Saaristo TE, Sandhu M, Saramies J, Schneiderman N, Schwarz P, Scott LJ, Selvin E, Sever P, Shu XO, Slagboom PE, Small KS, Smith BH, Snieder H, Sofer T, Sørensen TIA, Spector TD, Stanton A, Steves CJ, Stumvoll M, Sun L, Tabara Y, Tai ES, Timpson NJ, Tönjes A, Tuomilehto J, Tusie T, Uusitupa M, van der Harst P, van Duijn C, Vitart V, Vollenweider P, Vrijkotte TGM, Wagenknecht LE, Walker M, Wang YX, Wareham NJ, Watanabe RM, Watkins H, Wei WB, Wickremasinghe AR, Willemsen G, Wilson JF, Wong TY, Wu JY, Xiang AH, Yanek LR, Yengo L, Yokota M, Zeggini E, Zheng W, Zonderman AB, Rotter JI, Gloyn AL, McCarthy MI, Dupuis J, Meigs JB, Scott RA, Prokopenko I, Leong A, Liu CT, Parker SCJ, Mohlke KL, Langenberg C, Wheeler E, Morris AP, and Barroso I

Subjects
Alleles, Epigenesis, Genetic, Gene Expression Profiling, Genome, Human, Genome-Wide Association Study, Glycated Hemoglobin metabolism, Humans, Multifactorial Inheritance genetics, Physical Chromosome Mapping, Quantitative Trait Loci genetics, Blood Glucose genetics, Quantitative Trait, Heritable, White People genetics
Abstract

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10 -8 ), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution.

Published
2021
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73. A reference panel of 64,976 haplotypes for genotype imputation.

Author

McCarthy S, Das S, Kretzschmar W, Delaneau O, Wood AR, Teumer A, Kang HM, Fuchsberger C, Danecek P, Sharp K, Luo Y, Sidore C, Kwong A, Timpson N, Koskinen S, Vrieze S, Scott LJ, Zhang H, Mahajan A, Veldink J, Peters U, Pato C, van Duijn CM, Gillies CE, Gandin I, Mezzavilla M, Gilly A, Cocca M, Traglia M, Angius A, Barrett JC, Boomsma D, Branham K, Breen G, Brummett CM, Busonero F, Campbell H, Chan A, Chen S, Chew E, Collins FS, Corbin LJ, Smith GD, Dedoussis G, Dorr M, Farmaki AE, Ferrucci L, Forer L, Fraser RM, Gabriel S, Levy S, Groop L, Harrison T, Hattersley A, Holmen OL, Hveem K, Kretzler M, Lee JC, McGue M, Meitinger T, Melzer D, Min JL, Mohlke KL, Vincent JB, Nauck M, Nickerson D, Palotie A, Pato M, Pirastu N, McInnis M, Richards JB, Sala C, Salomaa V, Schlessinger D, Schoenherr S, Slagboom PE, Small K, Spector T, Stambolian D, Tuke M, Tuomilehto J, Van den Berg LH, Van Rheenen W, Volker U, Wijmenga C, Toniolo D, Zeggini E, Gasparini P, Sampson MG, Wilson JF, Frayling T, de Bakker PI, Swertz MA, McCarroll S, Kooperberg C, Dekker A, Altshuler D, Willer C, Iacono W, Ripatti S, Soranzo N, Walter K, Swaroop A, Cucca F, Anderson CA, Myers RM, Boehnke M, McCarthy MI, and Durbin R

Subjects
Alleles, Genetic Techniques, Genome-Wide Association Study, Humans, Internet, Reference Values, Genotype, Haplotypes, Polymorphism, Single Nucleotide
Abstract

We describe a reference panel of 64,976 human haplotypes at 39,235,157 SNPs constructed using whole-genome sequence data from 20 studies of predominantly European ancestry. Using this resource leads to accurate genotype imputation at minor allele frequencies as low as 0.1% and a large increase in the number of SNPs tested in association studies, and it can help to discover and refine causal loci. We describe remote server resources that allow researchers to carry out imputation and phasing consistently and efficiently.

Published
2016
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