Towards Universal Screening for Colon Cancer: A Cheap, Reliable, Noninvasive Test Using Gene Expression Analysis of Rectal Swabs

Though colon cancer is the second leading cause of cancer deaths in the US, it is entirely preventable through early screening to detect and remove adenomatous polyps. Colonoscopy has long been regarded as the “gold standard” but is expensive, invasive, and uncomfortable, and only about half those considered at risk for colon cancer currently submit to colonoscopy or to less reliable alternatives such as fecal occult blood test. Here we describe the use of gene expression analysis to detect altered expression of certain genes associated with not only colon cancer but also polyps. The analysis can be performed on rectal swabs, with specimens provided in a routine doctor's office visit. The existence of this cheap and simple test, together with an active program to encourage individuals to submit to screening, could help eradicate colon cancer

A Bivariate Genome-Wide Approach to Metabolic Syndrome: STAMPEED Consortium

OBJECTIVE The metabolic syndrome (MetS) is defined as concomitant disorders of lipid and glucose metabolism, central obesity, and high blood pressure, with an increased risk of type 2 diabetes and cardiovascular disease. This study tests whether common genetic variants with pleiotropic effects account for some of the correlated architecture among five metabolic phenotypes that define MetS. RESEARCH DESIGN AND METHODS Seven studies of the STAMPEED consortium, comprising 22,161 participants of European ancestry, underwent genome-wide association analyses of metabolic traits using a panel of ∼2.5 million imputed single nucleotide polymorphisms (SNPs). Phenotypes were defined by the National Cholesterol Education Program (NCEP) criteria for MetS in pairwise combinations. Individuals exceeding the NCEP thresholds for both traits of a pair were considered affected. RESULTS Twenty-nine common variants were associated with MetS or a pair of traits. Variants in the genes LPL, CETP, APOA5 (and its cluster), GCKR (and its cluster), LIPC, TRIB1, LOC100128354/MTNR1B, ABCB11, and LOC100129150 were further tested for their association with individual qualitative and quantitative traits. None of the 16 top SNPs (one per gene) associated simultaneously with more than two individual traits. Of them 11 variants showed nominal associations with MetS per se. The effects of 16 top SNPs on the quantitative traits were relatively small, together explaining from ∼9% of the variance in triglycerides, 5.8% of high-density lipoprotein cholesterol, 3.6% of fasting glucose, and 1.4% of systolic blood pressure. CONCLUSIONS Qualitative and quantitative pleiotropic tests on pairs of traits indicate that a small portion of the covariation in these traits can be explained by the reported common genetic variants

The trans-ancestral genomic architecture of glycemic traits

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

Associations of autozygosity with a broad range of human phenotypes

In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (FROH) for >1.4 million individuals, we show that FROH is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: FROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44–66%] in the odds of having children. Finally, the effects of FROH are confirmed within full-sibling pairs, where the variation in FROH is independent of all environmental confounding

Associations of autozygosity with a broad range of human phenotypes

Abstract: In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (FROH) for >1.4 million individuals, we show that FROH is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: FROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44–66%] in the odds of having children. Finally, the effects of FROH are confirmed within full-sibling pairs, where the variation in FROH is independent of all environmental confounding

Associations of autozygosity with a broad range of human phenotypes

436In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (FROH) for >1.4 million individuals, we show that FROH is significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: FROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44–66%] in the odds of having children. Finally, the effects of FROH are confirmed within full-sibling pairs, where the variation in FROH is independent of all environmental confounding.restrictedrestrictedClark D. W.; Okada Y.; Moore K. H. S.; Mason D.; Pirastu N.; Gandin I.; Mattsson H.; Barnes C. L. K.; Lin K.; Zhao J. H.; Deelen P.; Rohde R.; Schurmann C.; Guo X.; Giulianini F.; Zhang W.; Medina-Gomez C.; Karlsson R.; Bao Y.; Bartz T. M.; Baumbach C.; Biino G.; Bixley M. J.; Brumat M.; Chai J. -F.; Corre T.; Cousminer D. L.; Dekker A. M.; Eccles D. A.; van Eijk K. R.; Fuchsberger C.; Gao H.; Germain M.; Gordon S. D.; de Haan H. G.; Harris S. E.; Hofer E.; Huerta-Chagoya A.; Igartua C.; Jansen I. E.; Jia Y.; Kacprowski T.; Karlsson T.; Kleber M. E.; Li S. A.; Li-Gao R.; Mahajan A.; Matsuda K.; Meidtner K.; Meng W.; Montasser M. E.; van der Most P. J.; Munz M.; Nutile T.; Palviainen T.; Prasad G.; Prasad R. B.; Priyanka T. D. S.; Rizzi F.; Salvi E.; Sapkota B. R.; Shriner D.; Skotte L.; Smart M. C.; Smith A. V.; van der Spek A.; Spracklen C. N.; Strawbridge R. J.; Tajuddin S. M.; Trompet S.; Turman C.; Verweij N.; Viberti C.; Wang L.; Warren H. R.; Wootton R. E.; Yanek L. R.; Yao J.; Yousri N. A.; Zhao W.; Adeyemo A. A.; Afaq S.; Aguilar-Salinas C. A.; Akiyama M.; Albert M. L.; Allison M. A.; Alver M.; Aung T.; Azizi F.; Bentley A. R.; Boeing H.; Boerwinkle E.; Borja J. B.; de Borst G. J.; Bottinger E. P.; Broer L.; Campbell H.; Chanock S.; Chee M. -L.; Chen G.; Chen Y. -D. I.; Chen Z.; Chiu Y. -F.; Cocca M.; Collins F. S.; Concas M. P.; Corley J.; Cugliari G.; van Dam R. M.; Damulina A.; Daneshpour M. S.; Day F. R.; Delgado G. E.; Dhana K.; Doney A. S. F.; Dorr M.; Doumatey A. P.; Dzimiri N.; Ebenesersdottir S. S.; Elliott J.; Elliott P.; Ewert R.; Felix J. F.; Fischer K.; Freedman B. I.; Girotto G.; Goel A.; Gogele M.; Goodarzi M. O.; Graff M.; Granot-Hershkovitz E.; Grodstein F.; Guarrera S.; Gudbjartsson D. F.; Guity K.; Gunnarsson B.; Guo Y.; Hagenaars S. P.; Haiman C. A.; Halevy A.; Harris T. B.; Hedayati M.; van Heel D. A.; Hirata M.; Hofer I.; Hsiung C. A.; Huang J.; Hung Y. -J.; Ikram M. A.; Jagadeesan A.; Jousilahti P.; Kamatani Y.; Kanai M.; Kerrison N. D.; Kessler T.; Khaw K. -T.; Khor C. C.; de Kleijn D. P. V.; Koh W. -P.; Kolcic I.; Kraft P.; Kramer B. K.; Kutalik Z.; Kuusisto J.; Langenberg C.; Launer L. J.; Lawlor D. A.; Lee I. -T.; Lee W. -J.; Lerch M. M.; Li L.; Liu J.; Loh M.; London S. J.; Loomis S.; Lu Y.; Luan J.; Magi R.; Manichaikul A. W.; Manunta P.; Masson G.; Matoba N.; Mei X. W.; Meisinger C.; Meitinger T.; Mezzavilla M.; Milani L.; Millwood I. Y.; Momozawa Y.; Moore A.; Morange P. -E.; Moreno-Macias H.; Mori T. A.; Morrison A. C.; Muka T.; Murakami Y.; Murray A. D.; de Mutsert R.; Mychaleckyj J. C.; Nalls M. A.; Nauck M.; Neville M. J.; Nolte I. M.; Ong K. K.; Orozco L.; Padmanabhan S.; Palsson G.; Pankow J. S.; Pattaro C.; Pattie A.; Polasek O.; Poulter N.; Pramstaller P. P.; Quintana-Murci L.; Raikkonen K.; Ralhan S.; Rao D. C.; van Rheenen W.; Rich S. S.; Ridker P. M.; Rietveld C. A.; Robino A.; van Rooij F. J. A.; Ruggiero D.; Saba Y.; Sabanayagam C.; Sabater-Lleal M.; Sala C. F.; Salomaa V.; Sandow K.; Schmidt H.; Scott L. J.; Scott W. R.; Sedaghati-Khayat B.; Sennblad B.; van Setten J.; Sever P. J.; Sheu W. H. -H.; Shi Y.; Shrestha S.; Shukla S. R.; Sigurdsson J. K.; Sikka T. T.; Singh J. R.; Smith B. H.; Stancakova A.; Stanton A.; Starr J. M.; Stefansdottir L.; Straker L.; Sulem P.; Sveinbjornsson G.; Swertz M. A.; Taylor A. M.; Taylor K. D.; Terzikhan N.; Tham Y. -C.; Thorleifsson G.; Thorsteinsdottir U.; Tillander A.; Tracy R. P.; Tusie-Luna T.; Tzoulaki I.; Vaccargiu S.; Vangipurapu J.; Veldink J. H.; Vitart V.; Volker U.; Vuoksimaa E.; Wakil S. M.; Waldenberger M.; Wander G. S.; Wang Y. X.; Wareham N. J.; Wild S.; Yajnik C. S.; Yuan J. -M.; Zeng L.; Zhang L.; Zhou J.; Amin N.; Asselbergs F. W.; Bakker S. J. L.; Becker D. M.; Lehne B.; Bennett D. A.; van den Berg L. H.; Berndt S. I.; Bharadwaj D.; Bielak L. F.; Bochud M.; Boehnke M.; Bouchard C.; Bradfield J. P.; Brody J. A.; Campbell A.; Carmi S.; Caulfield M. J.; Cesarini D.; Chambers J. C.; Chandak G. R.; Cheng C. -Y.; Ciullo M.; Cornelis M.; Cusi D.; Smith G. D.; Deary I. J.; Dorajoo R.; van Duijn C. M.; Ellinghaus D.; Erdmann J.; Eriksson J. G.; Evangelou E.; Evans M. K.; Faul J. D.; Feenstra B.; Feitosa M.; Foisy S.; Franke A.; Friedlander Y.; Gasparini P.; Gieger C.; Gonzalez C.; Goyette P.; Grant S. F. A.; Griffiths L. R.; Groop L.; Gudnason V.; Gyllensten U.; Hakonarson H.; Hamsten A.; van der Harst P.; Heng C. -K.; Hicks A. A.; Hochner H.; Huikuri H.; Hunt S. C.; Jaddoe V. W. V.; De Jager P. L.; Johannesson M.; Johansson A.; Jonas J. B.; Jukema J. W.; Junttila J.; Kaprio J.; Kardia S. L. R.; Karpe F.; Kumari M.; Laakso M.; van der Laan S. W.; Lahti J.; Laudes M.; Lea R. A.; Lieb W.; Lumley T.; Martin N. G.; Marz W.; Matullo G.; McCarthy M. I.; Medland S. E.; Merriman T. R.; Metspalu A.; Meyer B. F.; Mohlke K. L.; Montgomery G. W.; Mook-Kanamori D.; Munroe P. B.; North K. E.; Nyholt D. R.; O'connell J. R.; Ober C.; Oldehinkel A. J.; Palmas W.; Palmer C.; Pasterkamp G. G.; Patin E.; Pennell C. E.; Perusse L.; Peyser P. A.; Pirastu M.; Polderman T. J. C.; Porteous D. J.; Posthuma D.; Psaty B. M.; Rioux J. D.; Rivadeneira F.; Rotimi C.; Rotter J. I.; Rudan I.; Den Ruijter H. M.; Sanghera D. K.; Sattar N.; Schmidt R.; Schulze M. B.; Schunkert H.; Scott R. A.; Shuldiner A. R.; Sim X.; Small N.; Smith J. A.; Sotoodehnia N.; Tai E. -S.; Teumer A.; Timpson N. J.; Toniolo D.; Tregouet D. -A.; Tuomi T.; Vollenweider P.; Wang C. A.; Weir D. R.; Whitfield J. B.; Wijmenga C.; Wong T. -Y.; Wright J.; Yang J.; Yu L.; Zemel B. S.; Zonderman A. B.; Perola M.; Magnusson P. K. E.; Uitterlinden A. G.; Kooner J. S.; Chasman D. I.; Loos R. J. F.; Franceschini N.; Franke L.; Haley C. S.; Hayward C.; Walters R. G.; Perry J. R. B.; Esko T.; Helgason A.; Stefansson K.; Joshi P. K.; Kubo M.; Wilson J. F.Clark, D. W.; Okada, Y.; Moore, K. H. S.; Mason, D.; Pirastu, N.; Gandin, I.; Mattsson, H.; Barnes, C. L. K.; Lin, K.; Zhao, J. H.; Deelen, P.; Rohde, R.; Schurmann, C.; Guo, X.; Giulianini, F.; Zhang, W.; Medina-Gomez, C.; Karlsson, R.; Bao, Y.; Bartz, T. M.; Baumbach, C.; Biino, G.; Bixley, M. J.; Brumat, M.; Chai, J. -F.; Corre, T.; Cousminer, D. L.; Dekker, A. M.; Eccles, D. A.; van Eijk, K. R.; Fuchsberger, C.; Gao, H.; Germain, M.; Gordon, S. D.; de Haan, H. G.; Harris, S. E.; Hofer, E.; Huerta-Chagoya, A.; Igartua, C.; Jansen, I. E.; Jia, Y.; Kacprowski, T.; Karlsson, T.; Kleber, M. E.; Li, S. A.; Li-Gao, R.; Mahajan, A.; Matsuda, K.; Meidtner, K.; Meng, W.; Montasser, M. E.; van der Most, P. J.; Munz, M.; Nutile, T.; Palviainen, T.; Prasad, G.; Prasad, R. B.; Priyanka, T. D. S.; Rizzi, F.; Salvi, E.; Sapkota, B. R.; Shriner, D.; Skotte, L.; Smart, M. C.; Smith, A. V.; van der Spek, A.; Spracklen, C. N.; Strawbridge, R. J.; Tajuddin, S. M.; Trompet, S.; Turman, C.; Verweij, N.; Viberti, C.; Wang, L.; Warren, H. R.; Wootton, R. E.; Yanek, L. R.; Yao, J.; Yousri, N. A.; Zhao, W.; Adeyemo, A. A.; Afaq, S.; Aguilar-Salinas, C. A.; Akiyama, M.; Albert, M. L.; Allison, M. A.; Alver, M.; Aung, T.; Azizi, F.; Bentley, A. R.; Boeing, H.; Boerwinkle, E.; Borja, J. B.; de Borst, G. J.; Bottinger, E. P.; Broer, L.; Campbell, H.; Chanock, S.; Chee, M. -L.; Chen, G.; Chen, Y. -D. I.; Chen, Z.; Chiu, Y. -F.; Cocca, M.; Collins, F. S.; Concas, M. P.; Corley, J.; Cugliari, G.; van Dam, R. M.; Damulina, A.; Daneshpour, M. S.; Day, F. R.; Delgado, G. E.; Dhana, K.; Doney, A. S. F.; Dorr, M.; Doumatey, A. P.; Dzimiri, N.; Ebenesersdottir, S. S.; Elliott, J.; Elliott, P.; Ewert, R.; Felix, J. F.; Fischer, K.; Freedman, B. I.; Girotto, G.; Goel, A.; Gogele, M.; Goodarzi, M. O.; Graff, M.; Granot-Hershkovitz, E.; Grodstein, F.; Guarrera, S.; Gudbjartsson, D. F.; Guity, K.; Gunnarsson, B.; Guo, Y.; Hagenaars, S. P.; Haiman, C. A.; Halevy, A.; Harris, T. B.; Hedayati, M.; van Heel, D. A.; Hirata, M.; Hofer, I.; Hsiung, C. A.; Huang, J.; Hung, Y. -J.; Ikram, M. A.; Jagadeesan, A.; Jousilahti, P.; Kamatani, Y.; Kanai, M.; Kerrison, N. D.; Kessler, T.; Khaw, K. -T.; Khor, C. C.; de Kleijn, D. P. V.; Koh, W. -P.; Kolcic, I.; Kraft, P.; Kramer, B. K.; Kutalik, Z.; Kuusisto, J.; Langenberg, C.; Launer, L. J.; Lawlor, D. A.; Lee, I. -T.; Lee, W. -J.; Lerch, M. M.; Li, L.; Liu, J.; Loh, M.; London, S. J.; Loomis, S.; Lu, Y.; Luan, J.; Magi, R.; Manichaikul, A. W.; Manunta, P.; Masson, G.; Matoba, N.; Mei, X. W.; Meisinger, C.; Meitinger, T.; Mezzavilla, M.; Milani, L.; Millwood, I. Y.; Momozawa, Y.; Moore, A.; Morange, P. -E.; Moreno-Macias, H.; Mori, T. A.; Morrison, A. C.; Muka, T.; Murakami, Y.; Murray, A. D.; de Mutsert, R.; Mychaleckyj, J. C.; Nalls, M. A.; Nauck, M.; Neville, M. J.; Nolte, I. M.; Ong, K. K.; Orozco, L.; Padmanabhan, S.; Palsson, G.; Pankow, J. S.; Pattaro, C.; Pattie, A.; Polasek, O.; Poulter, N.; Pramstaller, P. P.; Quintana-Murci, L.; Raikkonen, K.; Ralhan, S.; Rao, D. C.; van Rheenen, W.; Rich, S. S.; Ridker, P. M.; Rietveld, C. A.; Robino, A.; van Rooij, F. J. A.; Ruggiero, D.; Saba, Y.; Sabanayagam, C.; Sabater-Lleal, M.; Sala, C. F.; Salomaa, V.; Sandow, K.; Schmidt, H.; Scott, L. J.; Scott, W. R.; Sedaghati-Khayat, B.; Sennblad, B.; van Setten, J.; Sever, P. J.; Sheu, W. H. -H.; Shi, Y.; Shrestha, S.; Shukla, S. R.; Sigurdsson, J. K.; Sikka, T. T.; Singh, J. R.; Smith, B. H.; Stancakova, A.; Stanton, A.; Starr, J. M.; Stefansdottir, L.; Straker, L.; Sulem, P.; Sveinbjornsson, G.; Swertz, M. A.; Taylor, A. M.; Taylor, K. D.; Terzikhan, N.; Tham, Y. -C.; Thorleifsson, G.; Thorsteinsdottir, U.; Tillander, A.; Tracy, R. P.; Tusie-Luna, T.; Tzoulaki, I.; Vaccargiu, S.; Vangipurapu, J.; Veldink, J. H.; Vitart, V.; Volker, U.; Vuoksimaa, E.; Wakil, S. M.; Waldenberger, M.; Wander, G. S.; Wang, Y. X.; Wareham, N. J.; Wild, S.; Yajnik, C. S.; Yuan, J. -M.; Zeng, L.; Zhang, L.; Zhou, J.; Amin, N.; Asselbergs, F. W.; Bakker, S. J. L.; Becker, D. M.; Lehne, B.; Bennett, D. A.; van den Berg, L. H.; Berndt, S. I.; Bharadwaj, D.; Bielak, L. F.; Bochud, M.; Boehnke, M.; Bouchard, C.; Bradfield, J. P.; Brody, J. A.; Campbell, A.; Carmi, S.; Caulfield, M. J.; Cesarini, D.; Chambers, J. C.; Chandak, G. R.; Cheng, C. -Y.; Ciullo, M.; Cornelis, M.; Cusi, D.; Smith, G. D.; Deary, I. J.; Dorajoo, R.; van Duijn, C. M.; Ellinghaus, D.; Erdmann, J.; Eriksson, J. G.; Evangelou, E.; Evans, M. K.; Faul, J. D.; Feenstra, B.; Feitosa, M.; Foisy, S.; Franke, A.; Friedlander, Y.; Gasparini, P.; Gieger, C.; Gonzalez, C.; Goyette, P.; Grant, S. F. A.; Griffiths, L. R.; Groop, L.; Gudnason, V.; Gyllensten, U.; Hakonarson, H.; Hamsten, A.; van der Harst, P.; Heng, C. -K.; Hicks, A. A.; Hochner, H.; Huikuri, H.; Hunt, S. C.; Jaddoe, V. W. V.; De Jager, P. L.; Johannesson, M.; Johansson, A.; Jonas, J. B.; Jukema, J. W.; Junttila, J.; Kaprio, J.; Kardia, S. L. R.; Karpe, F.; Kumari, M.; Laakso, M.; van der Laan, S. W.; Lahti, J.; Laudes, M.; Lea, R. A.; Lieb, W.; Lumley, T.; Martin, N. G.; Marz, W.; Matullo, G.; Mccarthy, M. I.; Medland, S. E.; Merriman, T. R.; Metspalu, A.; Meyer, B. F.; Mohlke, K. L.; Montgomery, G. W.; Mook-Kanamori, D.; Munroe, P. B.; North, K. E.; Nyholt, D. R.; O'Connell, J. R.; Ober, C.; Oldehinkel, A. J.; Palmas, W.; Palmer, C.; Pasterkamp, G. G.; Patin, E.; Pennell, C. E.; Perusse, L.; Peyser, P. A.; Pirastu, M.; Polderman, T. J. C.; Porteous, D. J.; Posthuma, D.; Psaty, B. M.; Rioux, J. D.; Rivadeneira, F.; Rotimi, C.; Rotter, J. I.; Rudan, I.; Den Ruijter, H. M.; Sanghera, D. K.; Sattar, N.; Schmidt, R.; Schulze, M. B.; Schunkert, H.; Scott, R. A.; Shuldiner, A. R.; Sim, X.; Small, N.; Smith, J. A.; Sotoodehnia, N.; Tai, E. -S.; Teumer, A.; Timpson, N. J.; Toniolo, D.; Tregouet, D. -A.; Tuomi, T.; Vollenweider, P.; Wang, C. A.; Weir, D. R.; Whitfield, J. B.; Wijmenga, C.; Wong, T. -Y.; Wright, J.; Yang, J.; Yu, L.; Zemel, B. S.; Zonderman, A. B.; Perola, M.; Magnusson, P. K. E.; Uitterlinden, A. G.; Kooner, J. S.; Chasman, D. I.; Loos, R. J. F.; Franceschini, N.; Franke, L.; Haley, C. S.; Hayward, C.; Walters, R. G.; Perry, J. R. B.; Esko, T.; Helgason, A.; Stefansson, K.; Joshi, P. K.; Kubo, M.; Wilson, J. F

The trans-ancestral genomic architecture of glycemic traits

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 &lt; 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

The trans-ancestral genomic architecture of glycemic traits

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 &lt; 5 x 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