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54. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index

Author

Speliotes, Elizabeth K, Willer, Cristen J, Berndt, Sonja I, Monda, Keri L, Thorleifsson, Gudmar, Jackson, Anne U, Allen, Hana Lango, Lindgren, Cecilia M, Luan, Jian'an, Mägi, Reedik, Randall, Joshua C, Vedantam, Sailaja, Winkler, Thomas W, Qi, Lu, Workalemahu, Tsegaselassie, Heid, Iris M, Steinthorsdottir, Valgerdur, Stringham, Heather M, Weedon, Michael N, Wheeler, Eleanor, Wood, Andrew R, Ferreira, Teresa, Weyant, Robert J, Segrè, Ayellet V, Estrada, Karol, Liang, Liming, Nemesh, James, Park, Ju-Hyun, Gustafsson, Stefan, Kilpeläinen, Tuomas O, Yang, Jian, Bouatia-Naji, Nabila, Esko, Tõnu, Feitosa, Mary F, Kutalik, Zoltán, Mangino, Massimo, Raychaudhuri, Soumya, Scherag, Andre, Smith, Albert Vernon, Welch, Ryan, Zhao, Jing Hua, Aben, Katja K, Absher, Devin M, Amin, Najaf, Dixon, Anna L, Fisher, Eva, Glazer, Nicole L, Goddard, Michael E, Heard-Costa, Nancy L, Hoesel, Volker, Hottenga, Jouke-Jan, Johansson, Åsa, Johnson, Toby, Ketkar, Shamika, Lamina, Claudia, Li, Shengxu, Moffatt, Miriam F, Myers, Richard H, Narisu, Narisu, Perry, John RB, Peters, Marjolein J, Preuss, Michael, Ripatti, Samuli, Rivadeneira, Fernando, Sandholt, Camilla, Scott, Laura J, Timpson, Nicholas J, Tyrer, Jonathan P, van Wingerden, Sophie, Watanabe, Richard M, White, Charles C, Wiklund, Fredrik, Barlassina, Christina, Chasman, Daniel I, Cooper, Matthew N, Jansson, John-Olov, Lawrence, Robert W, Pellikka, Niina, Prokopenko, Inga, Shi, Jianxin, Thiering, Elisabeth, Alavere, Helene, Alibrandi, Maria TS, Almgren, Peter, Arnold, Alice M, Aspelund, Thor, Atwood, Larry D, Balkau, Beverley, Balmforth, Anthony J, Bennett, Amanda J, Ben-Shlomo, Yoav, Bergman, Richard N, Bergmann, Sven, Biebermann, Heike, Blakemore, Alexandra IF, Boes, Tanja, Bonnycastle, Lori L, Bornstein, Stefan R, Brown, Morris J, and Buchanan, Thomas A

Subjects
Biological Sciences, Genetics, Prevention, Human Genome, Body Height, Body Mass Index, Body Size, Body Weight, Chromosome Mapping, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Obesity, Polymorphism, Single Nucleotide, White People, MAGIC, Procardis Consortium, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Obesity is globally prevalent and highly heritable, but its underlying genetic factors remain largely elusive. To identify genetic loci for obesity susceptibility, we examined associations between body mass index and ∼ 2.8 million SNPs in up to 123,865 individuals with targeted follow up of 42 SNPs in up to 125,931 additional individuals. We confirmed 14 known obesity susceptibility loci and identified 18 new loci associated with body mass index (P < 5 × 10⁻⁸), one of which includes a copy number variant near GPRC5B. Some loci (at MC4R, POMC, SH2B1 and BDNF) map near key hypothalamic regulators of energy balance, and one of these loci is near GIPR, an incretin receptor. Furthermore, genes in other newly associated loci may provide new insights into human body weight regulation.

Published
2010

55. Hundreds of variants clustered in genomic loci and biological pathways affect human height

Author

Lango Allen, Hana, Estrada, Karol, Lettre, Guillaume, Berndt, Sonja I, Weedon, Michael N, Rivadeneira, Fernando, Willer, Cristen J, Jackson, Anne U, Vedantam, Sailaja, Raychaudhuri, Soumya, Ferreira, Teresa, Wood, Andrew R, Weyant, Robert J, Segrè, Ayellet V, Speliotes, Elizabeth K, Wheeler, Eleanor, Soranzo, Nicole, Park, Ju-Hyun, Yang, Jian, Gudbjartsson, Daniel, Heard-Costa, Nancy L, Randall, Joshua C, Qi, Lu, Vernon Smith, Albert, Mägi, Reedik, Pastinen, Tomi, Liang, Liming, Heid, Iris M, Luan, Jian’an, Thorleifsson, Gudmar, Winkler, Thomas W, Goddard, Michael E, Sin Lo, Ken, Palmer, Cameron, Workalemahu, Tsegaselassie, Aulchenko, Yurii S, Johansson, Åsa, Carola Zillikens, M, Feitosa, Mary F, Esko, Tõnu, Johnson, Toby, Ketkar, Shamika, Kraft, Peter, Mangino, Massimo, Prokopenko, Inga, Absher, Devin, Albrecht, Eva, Ernst, Florian, Glazer, Nicole L, Hayward, Caroline, Hottenga, Jouke-Jan, Jacobs, Kevin B, Knowles, Joshua W, Kutalik, Zoltán, Monda, Keri L, Polasek, Ozren, Preuss, Michael, Rayner, Nigel W, Robertson, Neil R, Steinthorsdottir, Valgerdur, Tyrer, Jonathan P, Voight, Benjamin F, Wiklund, Fredrik, Xu, Jianfeng, Hua Zhao, Jing, Nyholt, Dale R, Pellikka, Niina, Perola, Markus, Perry, John RB, Surakka, Ida, Tammesoo, Mari-Liis, Altmaier, Elizabeth L, Amin, Najaf, Aspelund, Thor, Bhangale, Tushar, Boucher, Gabrielle, Chasman, Daniel I, Chen, Constance, Coin, Lachlan, Cooper, Matthew N, Dixon, Anna L, Gibson, Quince, Grundberg, Elin, Hao, Ke, Juhani Junttila, M, Kaplan, Lee M, Kettunen, Johannes, König, Inke R, Kwan, Tony, Lawrence, Robert W, Levinson, Douglas F, Lorentzon, Mattias, McKnight, Barbara, Morris, Andrew P, Müller, Martina, Suh Ngwa, Julius, Purcell, Shaun, Rafelt, Suzanne, Salem, Rany M, and Salvi, Erika

Subjects
Biological Sciences, Genetics, Health Sciences, Mathematical Sciences, Statistics, Human Genome, Clinical Research, Biotechnology, Aetiology, 2.1 Biological and endogenous factors, Body Height, Chromosomes, Human, Pair 3, Genetic Loci, Genetic Predisposition to Disease, Genome, Human, Genome-Wide Association Study, Humans, Metabolic Networks and Pathways, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, General Science & Technology
Abstract

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P

Published
2010

57. Integrating genome-wide association and expression quantitative trait loci (eQTL) analyses identifies genes affecting fertility in cattle and suggests a common set of genes regulating fertility in mammals

Author

Forutan, Mehrnush, primary, Engle, Bailey, additional, Chamberlain, Amanda, additional, Ross, Elizabeth, additional, Nguyen, Loan, additional, D’Occhio, Michael, additional, Snr, Alf Collins, additional, Kho, Elise, additional, Fordyce, Geoffry, additional, Speight, Shannon, additional, Goddard, Michael, additional, and Hayes, Ben, additional

Published
2023
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58. Lipoproteins act as vehicles for lipid antigen delivery and activation of invariant natural killer T cells

Author

Engelen, Suzanne E., primary, Ververs, Francesca A., additional, Markovska, Angela, additional, Lagerholm, B. Christoffer, additional, Kraaijenhof, Jordan M., additional, Yousif, Laura I.E., additional, Zurke, Yasemin-Xiomara, additional, Gulersonmez, Can M.C., additional, Kooijman, Sander, additional, Goddard, Michael, additional, van Eijkeren, Robert J., additional, Jervis, Peter J., additional, Besra, Gurdyal S., additional, Haitjema, Saskia, additional, Asselbergs, Folkert W., additional, Kalkhoven, Eric, additional, Ploegh, Hidde L., additional, Boes, Marianne, additional, Cerundolo, Vincenzo, additional, Hovingh, G.K., additional, Salio, Mariolina, additional, Stigter, Edwin C.A., additional, Rensen, Patrick C.N., additional, Monaco, Claudia, additional, and Schipper, Henk S., additional

Published
2023
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64. Supplementary Material from Inflammatory-Related Genetic Variants in Non–Muscle-Invasive Bladder Cancer Prognosis: A Multimarker Bayesian Assessment

Author

Masson-Lecomte, Alexandra, primary, López de Maturana, Evangelina, primary, Goddard, Michael E., primary, Picornell, Antoni, primary, Rava, Marta, primary, González-Neira, Anna, primary, Márquez, Mirari, primary, Carrato, Alfredo, primary, Tardon, Adonina, primary, Lloreta, Josep, primary, Garcia-Closas, Montserrat, primary, Silverman, Debra, primary, Rothman, Nathaniel, primary, Kogevinas, Manolis, primary, Allory, Yves, primary, Chanock, Stephen J., primary, Real, Francisco X., primary, and Malats, Núria, primary

Published
2023
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66. Lipoproteins act as vehicles for lipid antigen delivery and activation of invariant natural killer T-cells

Author

Infection & Immunity, CTI Boes, CDL Cluster Speciële Diagnostiek, Circulatory Health, Team Medisch, Zorg en O&O, Child Health, CMM Groep Burgering, Cancer, Cardiologie patientenzorg, Engelen, Suzanne E, Ververs, Francesca A, Markovska, Angela, Lagerholm, B Christoffer, Kraaijenhof, Jordan M, Yousif, Laura Ie, Zurke, Yasemin-Xiomara, Gulersonmez, Can Mc, Kooijman, Sander, Goddard, Michael, van Eijkeren, Robert J, Jervis, Peter J, Besra, Gurdyal S, Haitjema, Saskia, Asselbergs, Folkert W, Kalkhoven, Eric, Ploegh, Hidde L, Boes, Marianne, Cerundolo, Vincenzo, Hovingh, G Kees, Salio, Mariolina, Stigter, Edwin Ca, Rensen, Patrick Cn, Monaco, Claudia, Schipper, Henk S, Infection & Immunity, CTI Boes, CDL Cluster Speciële Diagnostiek, Circulatory Health, Team Medisch, Zorg en O&O, Child Health, CMM Groep Burgering, Cancer, Cardiologie patientenzorg, Engelen, Suzanne E, Ververs, Francesca A, Markovska, Angela, Lagerholm, B Christoffer, Kraaijenhof, Jordan M, Yousif, Laura Ie, Zurke, Yasemin-Xiomara, Gulersonmez, Can Mc, Kooijman, Sander, Goddard, Michael, van Eijkeren, Robert J, Jervis, Peter J, Besra, Gurdyal S, Haitjema, Saskia, Asselbergs, Folkert W, Kalkhoven, Eric, Ploegh, Hidde L, Boes, Marianne, Cerundolo, Vincenzo, Hovingh, G Kees, Salio, Mariolina, Stigter, Edwin Ca, Rensen, Patrick Cn, Monaco, Claudia, and Schipper, Henk S

Published
2023

71. Use of dry-matter intake recorded at multiple time periods during lactation increases the accuracy of genomic prediction for dry-matter intake and residual feed intake in dairy cattle

Author

Bolormaa, Sunduimijid, primary, Haile-Mariam, Mekonnen, additional, Marett, Leah C., additional, Miglior, Filippo, additional, Baes, Christine F., additional, Schenkel, Flavio S., additional, Connor, Erin E., additional, Manzanilla-Pech, Coralia I. V., additional, Wall, Eileen, additional, Coffey, Mike P., additional, Goddard, Michael E., additional, MacLeod, Iona M., additional, and Pryce, Jennie E., additional

Published
2023
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73. Additional file 4 of Genetic variation in histone modifications and gene expression identifies regulatory variants in the mammary gland of cattle

Author

Prowse-Wilkins, Claire P., Lopdell, Thomas J., Xiang, Ruidong, Vander Jagt, Christy J., Littlejohn, Mathew D., Chamberlain, Amanda J., and Goddard, Michael E.

Abstract

Additional file 4: Supplementary Table 6. The frequencies of the top 10 predicted transcription factor (TF) classes from the JASPAR 2018 CORE database. Supplementary Table 7. The frequencies of the top six predicted promotor element classes from the JASPAR 2018 POLII database.

Published
2023
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74. Additional file 2 of Genetic variation in histone modifications and gene expression identifies regulatory variants in the mammary gland of cattle

Author

Prowse-Wilkins, Claire P., Lopdell, Thomas J., Xiang, Ruidong, Vander Jagt, Christy J., Littlejohn, Mathew D., Chamberlain, Amanda J., and Goddard, Michael E.

Abstract

Additional file 2: Supplementary Table 2. The number of genes or exons with a heterozygous SNP (tSNP), the number of genes or exons with multiple tSNP and the number of genes or exons where the tSNP phenotypes within were significantly different (p

Published
2023
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75. Additional file 3 of Genetic variation in histone modifications and gene expression identifies regulatory variants in the mammary gland of cattle

Author

Prowse-Wilkins, Claire P., Lopdell, Thomas J., Xiang, Ruidong, Vander Jagt, Christy J., Littlejohn, Mathew D., Chamberlain, Amanda J., and Goddard, Michael E.

Abstract

Additional file 3: Supplementary Table 4. The number of peak-exon pairs with shared allele-specific QTL variants as well as the distance between peak-exon pairs and the average number of times a peak was associated with an exon and vice versa. Supplementary Table 5. The number of peak-exon pairs with shared traditional QTL as well as the distance between peak-exon pairs and the average number of times a peak was associated with an exon and vice versa.

Published
2023
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76. Ooi et al (2023) - Supplementary materials.docx

Author

Ooi, Ee Cheng, XIANG, RUIDONG, Chamberlain, Amanda, and GODDARD, MICHAEL

Subjects
Animal production not elsewhere classified
Abstract

Supplementary materials for a published paper: Archetypal clustering reveals physiological mechanisms linking milk yield and fertility in dairy cattle.

Published
2023
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88. Discussion

Author

Goddard, Michael, van der Werf, Julius, editor, Graser, Hans-Ulrich, editor, Frankham, Richard, editor, and Gondro, Cedric, editor

Published
2009
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92. Interferon regulatory factor-5-dependent CD11c+ macrophages contribute to the formation of rupture–prone atherosclerotic plaques

Author

Edsfeldt, Andreas, primary, Swart, Maarten, additional, Singh, Pratibha, additional, Dib, Lea, additional, Sun, Jiangming, additional, Cole, Jennifer E, additional, Park, Inhye, additional, Al-Sharify, Dania, additional, Persson, Ana, additional, Nitulescu, Mihaela, additional, Borges, Patricia Das Neves, additional, Kassiteridi, Christina, additional, Goddard, Michael E, additional, Lee, Regent, additional, Volkov, Petr, additional, Orho-Melander, Marju, additional, Maegdefessel, Lars, additional, Nilsson, Jan, additional, Udalova, Irina, additional, Goncalves, Isabel, additional, and Monaco, Claudia, additional

Published
2022
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97. Genes influencing milk production traits predominantly affect one of four biological pathways

Author

Goddard Michael, McPartlan Helen, and Chamberlain Amanda

Subjects
bivariate analysis, independent traits, pleiotropy, genome scan, false discovery rate, Animal culture, SF1-1100, Genetics, QH426-470
Abstract

Abstract In this study we introduce a method that accounts for false positive and false negative results in attempting to estimate the true proportion of quantitative trait loci that affect two different traits. This method was applied to data from a genome scan that was used to detect QTL for three independent milk production traits, Australian Selection Index (ASI), protein percentage (P%) and fat percentage corrected for protein percentage (F% – P%). These four different scenarios are attributed to four biological pathways: QTL that (1) increase or decrease total mammary gland production (affecting ASI only); (2) increase or decrease lactose synthesis resulting in the volume of milk being changed but without a change in protein or fat yield (affecting P% only); (3) increase or decrease protein synthesis while milk volume remains relatively constant (affecting ASI and P% in the same direction); (4) increase or decrease fat synthesis while the volume of milk remains relatively constant (affecting F% – P% only). The results indicate that of the positions that detected a gene, most affected one trait and not the others, though a small proportion (2.8%) affected ASI and P% in the same direction.

Published
2008
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98. Sharing of either phenotypes or genetic variants can increase the accuracy of genomic prediction of feed efficiency

Author

Bolormaa, Sunduimijid, MacLeod, Iona M., Khansefid, Majid, Marret, Leah, Wales, Bill, Miglior, Filippo, Baes, Christine F., Schenkel, Flavio S., Connor, Erin E., Manzanilla Pech, Coralia Ines V, Coffey, Mike, Stothard, Paul, Herman, Emily, Nieuwhof, Gert J, Goddard, Michael E, and Pryce, Jennie

Subjects
630 Agriculture, Genotype, Australia, General Medicine, Genomics, Polymorphism, Single Nucleotide, Phenotype, Genetics, 590 Animals (Zoology), Animals, Lactation, Animal Science and Zoology, Cattle, Female, Ecology, Evolution, Behavior and Systematics, Uncategorized, Genome-Wide Association Study
Abstract

Background Sharing individual phenotype and genotype data between countries is complex and fraught with potential errors, while sharing summary statistics of genome-wide association studies (GWAS) is relatively straightforward, and thus would be especially useful for traits that are expensive or difficult-to-measure, such as feed efficiency. Here we examined: (1) the sharing of individual cow data from international partners; and (2) the use of sequence variants selected from GWAS of international cow data to evaluate the accuracy of genomic estimated breeding values (GEBV) for residual feed intake (RFI) in Australian cows. Results GEBV for RFI were estimated using genomic best linear unbiased prediction (GBLUP) with 50k or high-density single nucleotide polymorphisms (SNPs), from a training population of 3797 individuals in univariate to trivariate analyses where the three traits were RFI phenotypes calculated using 584 Australian lactating cows (AUSc), 824 growing heifers (AUSh), and 2526 international lactating cows (OVE). Accuracies of GEBV in AUSc were evaluated by either cohort-by-birth-year or fourfold random cross-validations. GEBV of AUSc were also predicted using only the AUS training population with a weighted genomic relationship matrix constructed with SNPs from the 50k array and sequence variants selected from a meta-GWAS that included only international datasets. The genomic heritabilities estimated using the AUSc, OVE and AUSh datasets were moderate, ranging from 0.20 to 0.36. The genetic correlations (rg) of traits between heifers and cows ranged from 0.30 to 0.95 but were associated with large standard errors. The mean accuracies of GEBV in Australian cows were up to 0.32 and almost doubled when either overseas cows, or both overseas cows and AUS heifers were included in the training population. They also increased when selected sequence variants were combined with 50k SNPs, but with a smaller relative increase. Conclusions The accuracy of RFI GEBV increased when international data were used or when selected sequence variants were combined with 50k SNP array data. This suggests that if direct sharing of data is not feasible, a meta-analysis of summary GWAS statistics could provide selected SNPs for custom panels to use in genomic selection programs. However, since this finding is based on a small cross-validation study, confirmation through a larger study is recommended.

Published
2021

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