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212 results on '"Goddard, Michael"'

4. Assessing the contribution of rare variants to complex trait heritability from whole-genome sequence data

Author

Wainschtein, Pierrick, Jain, Deepti, Zheng, Zhili, Cupples, L Adrienne, Shadyab, Aladdin H, McKnight, Barbara, Shoemaker, Benjamin M, Mitchell, Braxton D, Psaty, Bruce M, Kooperberg, Charles, Liu, Ching-Ti, Albert, Christine M, Roden, Dan, Chasman, Daniel I, Darbar, Dawood, Lloyd-Jones, Donald M, Arnett, Donna K, Regan, Elizabeth A, Boerwinkle, Eric, Rotter, Jerome I, O’Connell, Jeffrey R, Yanek, Lisa R, de Andrade, Mariza, Allison, Matthew A, McDonald, Merry-Lynn N, Chung, Mina K, Fornage, Myriam, Chami, Nathalie, Smith, Nicholas L, Ellinor, Patrick T, Vasan, Ramachandran S, Mathias, Rasika A, Loos, Ruth JF, Rich, Stephen S, Lubitz, Steven A, Heckbert, Susan R, Redline, Susan, Guo, Xiuqing, Chen, Y-D Ida, Laurie, Cecelia A, Hernandez, Ryan D, McGarvey, Stephen T, Goddard, Michael E, Laurie, Cathy C, North, Kari E, Lange, Leslie A, Weir, Bruce S, Yengo, Loic, Yang, Jian, and Visscher, Peter M

Subjects
Human Genome, Genetics, 2.1 Biological and endogenous factors, Aetiology, Generic health relevance, Alleles, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Multifactorial Inheritance, Polymorphism, Single Nucleotide, TOPMed Anthropometry Working Group, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Analyses of data from genome-wide association studies on unrelated individuals have shown that, for human traits and diseases, approximately one-third to two-thirds of heritability is captured by common SNPs. However, it is not known whether the remaining heritability is due to the imperfect tagging of causal variants by common SNPs, in particular whether the causal variants are rare, or whether it is overestimated due to bias in inference from pedigree data. Here we estimated heritability for height and body mass index (BMI) from whole-genome sequence data on 25,465 unrelated individuals of European ancestry. The estimated heritability was 0.68 (standard error 0.10) for height and 0.30 (standard error 0.10) for body mass index. Low minor allele frequency variants in low linkage disequilibrium (LD) with neighboring variants were enriched for heritability, to a greater extent for protein-altering variants, consistent with negative selection. Our results imply that rare variants, in particular those in regions of low linkage disequilibrium, are a major source of the still missing heritability of complex traits and disease.

Published
2022

5. Assortative mating biases marker-based heritability estimators

Author

Border, Richard, O’Rourke, Sean, de Candia, Teresa, Goddard, Michael E, Visscher, Peter M, Yengo, Loic, Jones, Matt, and Keller, Matthew C

Subjects
Biological Sciences, Genetics, Biological Psychology, Statistics, Mathematical Sciences, Psychology, Algorithms, Bias, Computer Simulation, Female, Genetics, Population, Genome-Wide Association Study, Humans, Likelihood Functions, Linkage Disequilibrium, Male, Mendelian Randomization Analysis, Models, Genetic, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait, Heritable, Reproduction
Abstract

Many traits are subject to assortative mating, with recent molecular genetic findings confirming longstanding theoretical predictions that assortative mating induces long range dependence across causal variants. However, all marker-based heritability estimators implicitly assume mating is random. We provide mathematical and simulation-based evidence demonstrating that both method-of-moments and likelihood-based estimators are biased in the presence of assortative mating and derive corrected heritability estimators for traits subject to assortment. Finally, we demonstrate that the empirical patterns of estimates across methods and sample sizes for real traits subject to assortative mating are congruent with expected assortative mating-induced biases. For example, marker-based heritability estimates for height are 14% - 23% higher than corrected estimates using UK Biobank data.

Published
2022

7. A saturated map of common genetic variants associated with human height

Author

Yengo, Loïc, Vedantam, Sailaja, Marouli, Eirini, Sidorenko, Julia, Bartell, Eric, Sakaue, Saori, Graff, Marielisa, Eliasen, Anders U., Jiang, Yunxuan, Raghavan, Sridharan, Miao, Jenkai, Arias, Joshua D., Graham, Sarah E., Mukamel, Ronen E., Spracklen, Cassandra N., Yin, Xianyong, Chen, Shyh-Huei, Ferreira, Teresa, Highland, Heather H., Ji, Yingjie, Karaderi, Tugce, Lin, Kuang, Lüll, Kreete, Malden, Deborah E., Medina-Gomez, Carolina, Machado, Moara, Moore, Amy, Rüeger, Sina, Sim, Xueling, Vrieze, Scott, Ahluwalia, Tarunveer S., Akiyama, Masato, Allison, Matthew A., Alvarez, Marcus, Andersen, Mette K., Ani, Alireza, Appadurai, Vivek, Arbeeva, Liubov, Bhaskar, Seema, Bielak, Lawrence F., Bollepalli, Sailalitha, Bonnycastle, Lori L., Bork-Jensen, Jette, Bradfield, Jonathan P., Bradford, Yuki, Braund, Peter S., Brody, Jennifer A., Burgdorf, Kristoffer S., Cade, Brian E., Cai, Hui, Cai, Qiuyin, Campbell, Archie, Cañadas-Garre, Marisa, Catamo, Eulalia, Chai, Jin-Fang, Chai, Xiaoran, Chang, Li-Ching, Chang, Yi-Cheng, Chen, Chien-Hsiun, Chesi, Alessandra, Choi, Seung Hoan, Chung, Ren-Hua, Cocca, Massimiliano, Concas, Maria Pina, Couture, Christian, Cuellar-Partida, Gabriel, Danning, Rebecca, Daw, E. Warwick, Degenhard, Frauke, Delgado, Graciela E., Delitala, Alessandro, Demirkan, Ayse, Deng, Xuan, Devineni, Poornima, Dietl, Alexander, Dimitriou, Maria, Dimitrov, Latchezar, Dorajoo, Rajkumar, Ekici, Arif B., Engmann, Jorgen E., Fairhurst-Hunter, Zammy, Farmaki, Aliki-Eleni, Faul, Jessica D., Fernandez-Lopez, Juan-Carlos, Forer, Lukas, Francescatto, Margherita, Freitag-Wolf, Sandra, Fuchsberger, Christian, Galesloot, Tessel E., Gao, Yan, Gao, Zishan, Geller, Frank, Giannakopoulou, Olga, Giulianini, Franco, Gjesing, Anette P., Goel, Anuj, Gordon, Scott D., Gorski, Mathias, Grove, Jakob, Guo, Xiuqing, Gustafsson, Stefan, Haessler, Jeffrey, Hansen, Thomas F., Havulinna, Aki S., Haworth, Simon J., He, Jing, Heard-Costa, Nancy, Hebbar, Prashantha, Hindy, George, Ho, Yuk-Lam A., Hofer, Edith, Holliday, Elizabeth, Horn, Katrin, Hornsby, Whitney E., Hottenga, Jouke-Jan, Huang, Hongyan, Huang, Jie, Huerta-Chagoya, Alicia, Huffman, Jennifer E., Hung, Yi-Jen, Huo, Shaofeng, Hwang, Mi Yeong, Iha, Hiroyuki, Ikeda, Daisuke D., Isono, Masato, Jackson, Anne U., Jäger, Susanne, Jansen, Iris E., Johansson, Ingegerd, Jonas, Jost B., Jonsson, Anna, Jørgensen, Torben, Kalafati, Ioanna-Panagiota, Kanai, Masahiro, Kanoni, Stavroula, Kårhus, Line L., Kasturiratne, Anuradhani, Katsuya, Tomohiro, Kawaguchi, Takahisa, Kember, Rachel L., Kentistou, Katherine A., Kim, Han-Na, Kim, Young Jin, Kleber, Marcus E., Knol, Maria J., Kurbasic, Azra, Lauzon, Marie, Le, Phuong, Lea, Rodney, Lee, Jong-Young, Leonard, Hampton L., Li, Shengchao A., Li, Xiaohui, Li, Xiaoyin, Liang, Jingjing, Lin, Honghuang, Lin, Shih-Yi, Liu, Jun, Liu, Xueping, Lo, Ken Sin, Long, Jirong, Lores-Motta, Laura, Luan, Jian’an, Lyssenko, Valeriya, Lyytikäinen, Leo-Pekka, Mahajan, Anubha, Mamakou, Vasiliki, Mangino, Massimo, Manichaikul, Ani, Marten, Jonathan, Mattheisen, Manuel, Mavarani, Laven, McDaid, Aaron F., Meidtner, Karina, Melendez, Tori L., Mercader, Josep M., Milaneschi, Yuri, Miller, Jason E., Millwood, Iona Y., Mishra, Pashupati P., Mitchell, Ruth E., Møllehave, Line T., Morgan, Anna, Mucha, Soeren, Munz, Matthias, Nakatochi, Masahiro, Nelson, Christopher P., Nethander, Maria, Nho, Chu Won, Nielsen, Aneta A., Nolte, Ilja M., Nongmaithem, Suraj S., Noordam, Raymond, Ntalla, Ioanna, Nutile, Teresa, Pandit, Anita, Christofidou, Paraskevi, Pärna, Katri, Pauper, Marc, Petersen, Eva R. B., Petersen, Liselotte V., Pitkänen, Niina, Polašek, Ozren, Poveda, Alaitz, Preuss, Michael H., Pyarajan, Saiju, Raffield, Laura M., Rakugi, Hiromi, Ramirez, Julia, Rasheed, Asif, Raven, Dennis, Rayner, Nigel W., Riveros, Carlos, Rohde, Rebecca, Ruggiero, Daniela, Ruotsalainen, Sanni E., Ryan, Kathleen A., Sabater-Lleal, Maria, Saxena, Richa, Scholz, Markus, Sendamarai, Anoop, Shen, Botong, Shi, Jingchunzi, Shin, Jae Hun, Sidore, Carlo, Sitlani, Colleen M., Slieker, Roderick C., Smit, Roelof A. J., Smith, Albert V., Smith, Jennifer A., Smyth, Laura J., Southam, Lorraine, Steinthorsdottir, Valgerdur, Sun, Liang, Takeuchi, Fumihiko, Tallapragada, Divya Sri Priyanka, Taylor, Kent D., Tayo, Bamidele O., Tcheandjieu, Catherine, Terzikhan, Natalie, Tesolin, Paola, Teumer, Alexander, Theusch, Elizabeth, Thompson, Deborah J., Thorleifsson, Gudmar, Timmers, Paul R. H. J., Trompet, Stella, Turman, Constance, Vaccargiu, Simona, van der Laan, Sander W., van der Most, Peter J., van Klinken, Jan B., van Setten, Jessica, Verma, Shefali S., Verweij, Niek, Veturi, Yogasudha, Wang, Carol A., Wang, Chaolong, Wang, Lihua, Wang, Zhe, Warren, Helen R., Bin Wei, Wen, Wickremasinghe, Ananda R., Wielscher, Matthias, Wiggins, Kerri L., Winsvold, Bendik S., Wong, Andrew, Wu, Yang, Wuttke, Matthias, Xia, Rui, Xie, Tian, Yamamoto, Ken, Yang, Jingyun, Yao, Jie, Young, Hannah, Yousri, Noha A., Yu, Lei, Zeng, Lingyao, Zhang, Weihua, Zhang, Xinyuan, Zhao, Jing-Hua, Zhao, Wei, Zhou, Wei, Zimmermann, Martina E., Zoledziewska, Magdalena, Adair, Linda S., Adams, Hieab H. H., Aguilar-Salinas, Carlos A., Al-Mulla, Fahd, Arnett, Donna K., Asselbergs, Folkert W., Åsvold, Bjørn Olav, Attia, John, Banas, Bernhard, Bandinelli, Stefania, Bennett, David A., Bergler, Tobias, Bharadwaj, Dwaipayan, Biino, Ginevra, Bisgaard, Hans, Boerwinkle, Eric, Böger, Carsten A., Bønnelykke, Klaus, Boomsma, Dorret I., Børglum, Anders D., Borja, Judith B., Bouchard, Claude, Bowden, Donald W., Brandslund, Ivan, Brumpton, Ben, Buring, Julie E., Caulfield, Mark J., Chambers, John C., Chandak, Giriraj R., Chanock, Stephen J., Chaturvedi, Nish, Chen, Yii-Der Ida, Chen, Zhengming, Cheng, Ching-Yu, Christophersen, Ingrid E., Ciullo, Marina, Cole, John W., Collins, Francis S., Cooper, Richard S., Cruz, Miguel, Cucca, Francesco, Cupples, L. Adrienne, Cutler, Michael J., Damrauer, Scott M., Dantoft, Thomas M., de Borst, Gert J., de Groot, Lisette C. P. G. M., De Jager, Philip L., de Kleijn, Dominique P. V., Janaka de Silva, H., Dedoussis, George V., den Hollander, Anneke I., Du, Shufa, Easton, Douglas F., Elders, Petra J. M., Eliassen, A. Heather, Ellinor, Patrick T., Elmståhl, Sölve, Erdmann, Jeanette, Evans, Michele K., Fatkin, Diane, Feenstra, Bjarke, Feitosa, Mary F., Ferrucci, Luigi, Ford, Ian, Fornage, Myriam, Franke, Andre, Franks, Paul W., Freedman, Barry I., Gasparini, Paolo, Gieger, Christian, Girotto, Giorgia, Goddard, Michael E., Golightly, Yvonne M., Gonzalez-Villalpando, Clicerio, Gordon-Larsen, Penny, Grallert, Harald, Grant, Struan F. A., Grarup, Niels, Griffiths, Lyn, Gudnason, Vilmundur, Haiman, Christopher, Hakonarson, Hakon, Hansen, Torben, Hartman, Catharina A., Hattersley, Andrew T., Hayward, Caroline, Heckbert, Susan R., Heng, Chew-Kiat, Hengstenberg, Christian, Hewitt, Alex W., Hishigaki, Haretsugu, Hoyng, Carel B., Huang, Paul L., Huang, Wei, Hunt, Steven C., Hveem, Kristian, Hyppönen, Elina, Iacono, William G., Ichihara, Sahoko, Ikram, M. Arfan, Isasi, Carmen R., Jackson, Rebecca D., Jarvelin, Marjo-Riitta, Jin, Zi-Bing, Jöckel, Karl-Heinz, Joshi, Peter K., Jousilahti, Pekka, Jukema, J. Wouter, Kähönen, Mika, Kamatani, Yoichiro, Kang, Kui Dong, Kaprio, Jaakko, Kardia, Sharon L. R., Karpe, Fredrik, Kato, Norihiro, Kee, Frank, Kessler, Thorsten, Khera, Amit V., Khor, Chiea Chuen, Kiemeney, Lambertus A. L. M., Kim, Bong-Jo, Kim, Eung Kweon, Kim, Hyung-Lae, Kirchhof, Paulus, Kivimaki, Mika, Koh, Woon-Puay, Koistinen, Heikki A., Kolovou, Genovefa D., Kooner, Jaspal S., Kooperberg, Charles, Köttgen, Anna, Kovacs, Peter, Kraaijeveld, Adriaan, Kraft, Peter, Krauss, Ronald M., Kumari, Meena, Kutalik, Zoltan, Laakso, Markku, Lange, Leslie A., Langenberg, Claudia, Launer, Lenore J., Le Marchand, Loic, Lee, Hyejin, Lee, Nanette R., Lehtimäki, Terho, Li, Huaixing, Li, Liming, Lieb, Wolfgang, Lin, Xu, Lind, Lars, Linneberg, Allan, Liu, Ching-Ti, Liu, Jianjun, Loeffler, Markus, London, Barry, Lubitz, Steven A., Lye, Stephen J., Mackey, David A., Mägi, Reedik, Magnusson, Patrik K. E., Marcus, Gregory M., Vidal, Pedro Marques, Martin, Nicholas G., März, Winfried, Matsuda, Fumihiko, McGarrah, Robert W., McGue, Matt, McKnight, Amy Jayne, Medland, Sarah E., Mellström, Dan, Metspalu, Andres, Mitchell, Braxton D., Mitchell, Paul, Mook-Kanamori, Dennis O., Morris, Andrew D., Mucci, Lorelei A., Munroe, Patricia B., Nalls, Mike A., Nazarian, Saman, Nelson, Amanda E., Neville, Matt J., Newton-Cheh, Christopher, Nielsen, Christopher S., Nöthen, Markus M., Ohlsson, Claes, Oldehinkel, Albertine J., Orozco, Lorena, Pahkala, Katja, Pajukanta, Päivi, Palmer, Colin N. A., Parra, Esteban J., Pattaro, Cristian, Pedersen, Oluf, Pennell, Craig E., Penninx, Brenda W. J. H., Perusse, Louis, Peters, Annette, Peyser, Patricia A., Porteous, David J., Posthuma, Danielle, Power, Chris, Pramstaller, Peter P., Province, Michael A., Qi, Qibin, Qu, Jia, Rader, Daniel J., Raitakari, Olli T., Ralhan, Sarju, Rallidis, Loukianos S., Rao, Dabeeru C., Redline, Susan, Reilly, Dermot F., Reiner, Alexander P., Rhee, Sang Youl, Ridker, Paul M., Rienstra, Michiel, Ripatti, Samuli, Ritchie, Marylyn D., Roden, Dan M., Rosendaal, Frits R., Rotter, Jerome I., Rudan, Igor, Rutters, Femke, Sabanayagam, Charumathi, Saleheen, Danish, Salomaa, Veikko, Samani, Nilesh J., Sanghera, Dharambir K., Sattar, Naveed, Schmidt, Börge, Schmidt, Helena, Schmidt, Reinhold, Schulze, Matthias B., Schunkert, Heribert, Scott, Laura J., Scott, Rodney J., Sever, Peter, Shiroma, Eric J., Shoemaker, M. Benjamin, Shu, Xiao-Ou, Simonsick, Eleanor M., Sims, Mario, Singh, Jai Rup, Singleton, Andrew B., Sinner, Moritz F., Smith, J. Gustav, Snieder, Harold, Spector, Tim D., Stampfer, Meir J., Stark, Klaus J., Strachan, David P., ‘t Hart, Leen M., Tabara, Yasuharu, Tang, Hua, Tardif, Jean-Claude, Thanaraj, Thangavel A., Timpson, Nicholas J., Tönjes, Anke, Tremblay, Angelo, Tuomi, Tiinamaija, Tuomilehto, Jaakko, Tusié-Luna, Maria-Teresa, Uitterlinden, Andre G., van Dam, Rob M., van der Harst, Pim, Van der Velde, Nathalie, van Duijn, Cornelia M., van Schoor, Natasja M., Vitart, Veronique, Völker, Uwe, Vollenweider, Peter, Völzke, Henry, Wacher-Rodarte, Niels H., Walker, Mark, Wang, Ya Xing, Wareham, Nicholas J., Watanabe, Richard M., Watkins, Hugh, Weir, David R., Werge, Thomas M., Widen, Elisabeth, Wilkens, Lynne R., Willemsen, Gonneke, Willett, Walter C., Wilson, James F., Wong, Tien-Yin, Woo, Jeong-Taek, Wright, Alan F., Wu, Jer-Yuarn, Xu, Huichun, Yajnik, Chittaranjan S., Yokota, Mitsuhiro, Yuan, Jian-Min, Zeggini, Eleftheria, Zemel, Babette S., Zheng, Wei, Zhu, Xiaofeng, Zmuda, Joseph M., Zonderman, Alan B., Zwart, John-Anker, Chasman, Daniel I., Cho, Yoon Shin, Heid, Iris M., McCarthy, Mark I., Ng, Maggie C. Y., O’Donnell, Christopher J., Rivadeneira, Fernando, Thorsteinsdottir, Unnur, Sun, Yan V., Tai, E. Shyong, Boehnke, Michael, Deloukas, Panos, Justice, Anne E., Lindgren, Cecilia M., Loos, Ruth J. F., Mohlke, Karen L., North, Kari E., Stefansson, Kari, Walters, Robin G., Winkler, Thomas W., Young, Kristin L., Loh, Po-Ru, Yang, Jian, Esko, Tõnu, Assimes, Themistocles L., Auton, Adam, Abecasis, Goncalo R., Willer, Cristen J., Locke, Adam E., Berndt, Sonja I., Lettre, Guillaume, Frayling, Timothy M., Okada, Yukinori, Wood, Andrew R., Visscher, Peter M., and Hirschhorn, Joel N.

Published
2022
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13. Allele-specific binding variants causing ChIP-seq peak height of histone modification are not enriched in expression QTL annotations.

Author

Ghoreishifar, Mohammad, Chamberlain, Amanda J., Xiang, Ruidong, Prowse-Wilkins, Claire P., Lopdell, Thomas J., Littlejohn, Mathew D., Pryce, Jennie E., and Goddard, Michael E.

Subjects
GENE expression, LOCUS (Genetics), LINKAGE disequilibrium, NUCLEOTIDE sequence, IMMUNOPRECIPITATION, SUPPORT vector machines
Abstract

Background: Genome sequence variants affecting complex traits (quantitative trait loci, QTL) are enriched in functional regions of the genome, such as those marked by certain histone modifications. These variants are believed to influence gene expression. However, due to the linkage disequilibrium among nearby variants, pinpointing the precise location of QTL is challenging. We aimed to identify allele-specific binding (ASB) QTL (asbQTL) that cause variation in the level of histone modification, as measured by the height of peaks assayed by ChIP-seq (chromatin immunoprecipitation sequencing). We identified DNA sequences that predict the difference between alleles in ChIP-seq peak height in H3K4me3 and H3K27ac histone modifications in the mammary glands of cows. Results: We used a gapped k-mer support vector machine, a novel best linear unbiased prediction model, and a multiple linear regression model that combines the other two approaches to predict variant impacts on peak height. For each method, a subset of 1000 sites with the highest magnitude of predicted ASB was considered as candidate asbQTL. The accuracy of this prediction was measured by the proportion where the predicted direction matched the observed direction. Prediction accuracy ranged between 0.59 and 0.74, suggesting that these 1000 sites are enriched for asbQTL. Using independent data, we investigated functional enrichment in the candidate asbQTL set and three control groups, including non-causal ASB sites, non-ASB variants under a peak, and SNPs (single nucleotide polymorphisms) not under a peak. For H3K4me3, a higher proportion of the candidate asbQTL were confirmed as ASB when compared to the non-causal ASB sites (P < 0.01). However, these candidate asbQTL did not enrich for the other annotations, including expression QTL (eQTL), allele-specific expression QTL (aseQTL) and sites conserved across mammals (P > 0.05). Conclusions: We identified putatively causal sites for asbQTL using the DNA sequence surrounding these sites. Our results suggest that many sites influencing histone modifications may not directly affect gene expression. However, it is important to acknowledge that distinguishing between putative causal ASB sites and other non-causal ASB sites in high linkage disequilibrium with the causal sites regarding their impact on gene expression may be challenging due to limitations in statistical power. [ABSTRACT FROM AUTHOR]

Published
2024
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14. Genome-wide association and expression quantitative trait loci in cattle reveals common genes regulating mammalian fertility.

Author

Forutan, Mehrnush, Engle, Bailey N., Chamberlain, Amanda J., Ross, Elizabeth M., Nguyen, Loan T., D'Occhio, Michael J., Snr, Alf Collins, Kho, Elise A., Fordyce, Geoffry, Speight, Shannon, Goddard, Michael E., and Hayes, Ben J.

Subjects
LOCUS (Genetics), CATTLE crossbreeding, CATTLE fertility, HEIFERS, GENOME-wide association studies, MAMMAL fertility, FERTILITY, GENETIC variation
Abstract

Most genetic variants associated with fertility in mammals fall in non-coding regions of the genome and it is unclear how these variants affect fertility. Here we use genome-wide association summary statistics for Heifer puberty (pubertal or not at 600 days) from 27,707 Bos indicus, Bos taurus and crossbred cattle; multi-trait GWAS signals from 2119 indicine cattle for four fertility traits, including days to calving, age at first calving, pregnancy status, and foetus age in weeks (assessed by rectal palpation of the foetus); and expression quantitative trait locus for whole blood from 489 indicine cattle, to identify 87 putatively functional genes affecting cattle fertility. Our analysis reveals a significant overlap between the set of cattle and previously reported human fertility-related genes, impling the existence of a shared pool of genes that regulate fertility in mammals. These findings are crucial for developing approaches to improve fertility in cattle and potentially other mammals. The authors identify the genetic variants and genes associated with four fertility-related traits in a well-phenotyped cattle population. [ABSTRACT FROM AUTHOR]

Published
2024
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17. A common regulatory haplotype doubles lactoferrin concentration in milk.

Author

Lopdell, Thomas J., Trevarton, Alexander J., Moody, Janelle, Prowse-Wilkins, Claire, Knowles, Sarah, Tiplady, Kathryn, Chamberlain, Amanda J., Goddard, Michael E., Spelman, Richard J., Lehnert, Klaus, Snell, Russell G., Davis, Stephen R., and Littlejohn, Mathew D.

Subjects
LACTOFERRIN, HAPLOTYPES, LOCUS (Genetics), WHEY proteins, GENE mapping, MILK
Abstract

Background: Bovine lactoferrin (Lf) is an iron absorbing whey protein with antibacterial, antiviral, and antifungal activity. Lactoferrin is economically valuable and has an extremely variable concentration in milk, partly driven by environmental influences such as milking frequency, involution, or mastitis. A significant genetic influence has also been previously observed to regulate lactoferrin content in milk. Here, we conducted genetic mapping of lactoferrin protein concentration in conjunction with RNA-seq, ChIP-seq, and ATAC-seq data to pinpoint candidate causative variants that regulate lactoferrin concentrations in milk. Results: We identified a highly-significant lactoferrin protein quantitative trait locus (pQTL), as well as a cislactotransferrin (LTF) expression QTL (cis-eQTL) mapping to the LTF locus. Using ChIP-seq and ATAC-seq datasets representing lactating mammary tissue samples, we also report a number of regions where the openness of chromatin is under genetic influence. Several of these also show highly significant QTL with genetic signatures similar to those highlighted through pQTL and eQTL analysis. By performing correlation analysis between these QTL, we revealed an ATAC-seq peak in the putative promotor region of LTF, that highlights a set of 115 high-frequency variants that are potentially responsible for these effects. One of the 115 variants (rs110000337), which maps within the ATAC-seq peak, was predicted to alter binding sites of transcription factors known to be involved in lactation-related pathways. Conclusions: Here, we report a regulatory haplotype of 115 variants with conspicuously large impacts on milk lactoferrin concentration. These findings could enable the selection of animals for high-producing specialist herds. [ABSTRACT FROM AUTHOR]

Published
2024
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18. Phantom epistasis between unlinked loci

Author

Hemani, Gibran, Powell, Joseph E., Wang, Huanwei, Shakhbazov, Konstantin, Westra, Harm-Jan, Esko, Tonu, Henders, Anjali K., McRae, Allan F., Martin, Nicholas G., Metspalu, Andres, Franke, Lude, Montgomery, Grant W., Goddard, Michael E., Gibson, Greg, Yang, Jian, and Visscher, Peter M.

Published
2021
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24. Psychiatric genome-wide association study analyses implicate neuronal, immune and histone pathways

Author

O'Dushlaine, Colm, Rossin, Lizzy, Lee, Phil H, Duncan, Laramie, Parikshak, Neelroop N, Newhouse, Stephen, Ripke, Stephan, Neale, Benjamin M, Purcell, Shaun M, Posthuma, Danielle, Nurnberger, John I, Lee, S Hong, Faraone, Stephen V, Perlis, Roy H, Mowry, Bryan J, Thapar, Anita, Goddard, Michael E, Witte, John S, Absher, Devin, Agartz, Ingrid, Akil, Huda, Amin, Farooq, Andreassen, Ole A, Anjorin, Adebayo, Anney, Richard, Anttila, Verneri, Arking, Dan E, Asherson, Philip, Azevedo, Maria H, Backlund, Lena, Badner, Judith A, Bailey, Anthony J, Banaschewski, Tobias, Barchas, Jack D, Barnes, Michael R, Barrett, Thomas B, Bass, Nicholas, Battaglia, Agatino, Bauer, Michael, Bayes, Monica, Bellivier, Frank, Bergen, Sarah E, Berrettini, Wade, Betancur, Catalina, Bettecken, Thomas, Biederman, Joseph, Binder, Elisabeth B, Black, Donald W, Blackwood, Douglas HR, Bloss, Cinnamon S, Boehnke, Michael, Boomsma, Dorret I, Breuer, Rene, Bruggeman, Richard, Cormican, Paul, Buccola, Nancy G, Buitelaar, Jan K, Bunney, William E, Buxbaum, Joseph D, Byerley, William F, Byrne, Enda M, Caesar, Sian, Cahn, Wiepke, Cantor, Rita M, Casas, Miguel, Chakravarti, Aravinda, Chambert, Kimberly, Choudhury, Khalid, Cichon, Sven, Mattheisen, Manuel, Cloninger, C Robert, Collier, David A, Cook, Edwin H, Coon, Hilary, Cormand, Bru, Corvin, Aiden, Coryell, William H, Craig, David W, Craig, Ian W, Crosbie, Jennifer, Cuccaro, Michael L, Curtis, David, Czamara, Darina, Datta, Susmita, Dawson, Geraldine, Day, Richard, De Geus, Eco J, Degenhardt, Franziska, Djurovic, Srdjan, Donohoe, Gary J, Doyle, Alysa E, Duan, Jubao, Dudbridge, Frank, Duketis, Eftichia, Ebstein, Richard P, Edenberg, Howard J, Elia, Josephine, Ennis, Sean, Etain, Bruno, and Fanous, Ayman

Subjects
Human Genome, Brain Disorders, Serious Mental Illness, Schizophrenia, Genetics, Depression, Mental Health, Aetiology, 2.1 Biological and endogenous factors, Mental health, Good Health and Well Being, Brain, Databases, Genetic, Genetic Predisposition to Disease, Genome-Wide Association Study, Histones, Humans, Mental Disorders, Signal Transduction, Network and Pathway Analysis Subgroup of Psychiatric Genomics Consortium, Neurosciences, Psychology, Cognitive Sciences, Neurology & Neurosurgery
Abstract

Genome-wide association studies (GWAS) of psychiatric disorders have identified multiple genetic associations with such disorders, but better methods are needed to derive the underlying biological mechanisms that these signals indicate. We sought to identify biological pathways in GWAS data from over 60,000 participants from the Psychiatric Genomics Consortium. We developed an analysis framework to rank pathways that requires only summary statistics. We combined this score across disorders to find common pathways across three adult psychiatric disorders: schizophrenia, major depression and bipolar disorder. Histone methylation processes showed the strongest association, and we also found statistically significant evidence for associations with multiple immune and neuronal signaling pathways and with the postsynaptic density. Our study indicates that risk variants for psychiatric disorders aggregate in particular biological pathways and that these pathways are frequently shared between disorders. Our results confirm known mechanisms and suggest several novel insights into the etiology of psychiatric disorders.

Published
2015

35. Application of Genetics and Genomics in Livestock Production.

Author

Burrow, Heather and Goddard, Michael

Subjects
LIVESTOCK productivity, GENETICS, GENOTYPE-environment interaction, CATTLE genetics, CALVES, CATTLE breeds, GENOMICS, RANGELANDS
Abstract

The delivery of genomic sequences for most livestock species over the past 10-15 years has generated the potential to revolutionize livestock production globally, by providing farmers with the ability to match individual animals to the requirements of rapidly changing climates, production systems and markets. Review Process All articles published in this Special Issue "Application of Genetics and Genomics in Livestock Production" underwent peer review by independent subject matter experts in the fields of livestock genetics and genomics. Application of Genetics and Genomics to Livestock Production: Summary of Articles 3.1. [Extracted from the article]

Published
2023
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38. Identification of Genomic Variants Causing Variation in Quantitative Traits: A Review.

Author

Meuwissen, Theo, Hayes, Ben, MacLeod, Iona, and Goddard, Michael

Subjects
LINKAGE disequilibrium, NUCLEOTIDE sequence, LOCUS (Genetics), GENOME editing, DNA sequencing
Abstract

Many of the important traits of livestock are complex or quantitative traits controlled by thousands of variants in the DNA sequence of individual animals and environmental factors. Identification of these causal variants would be advantageous for genomic prediction, to understand the physiology and evolution of important traits and for genome editing. However, it is difficult to identify these causal variants because their effects are small and they are in linkage disequilibrium with other DNA variants. Nevertheless, it should be possible to identify probable causal variants for complex traits just as we do for simple traits provided we compensate for the small effect size with larger sample size. In this review we consider eight types of evidence needed to identify causal variants. Large and diverse samples of animals, accurate genotypes, multiple phenotypes, annotation of genomic sites, comparisons across species, comparisons across the genome, the physiological role of candidate genes and experimental mutation of the candidate genomic site. [ABSTRACT FROM AUTHOR]

Published
2022
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39. Genetic studies of body mass index yield new insights for obesity biology

Author

Locke, Adam E., Kahali, Bratati, Berndt, Sonja I., Justice, Anne E., Pers, Tune H., Day, Felix R., Powell, Corey, Vedantam, Sailaja, Buchkovich, Martin L., Yang, Jian, Croteau-Chonka, Damien C., Esko, Tonu, Fall, Tove, Ferreira, Teresa, Gustafsson, Stefan, Kutalik, Zoltán, Luan, Jianʼan, Mägi, Reedik, Randall, Joshua C., Winkler, Thomas W., Wood, Andrew R., Workalemahu, Tsegaselassie, Faul, Jessica D., Smith, Jennifer A., Hua Zhao, Jing, Zhao, Wei, Chen, Jin, Fehrmann, Rudolf, Hedman, Åsa K., Karjalainen, Juha, Schmidt, Ellen M., Absher, Devin, Amin, Najaf, Anderson, Denise, Beekman, Marian, Bolton, Jennifer L., Bragg-Gresham, Jennifer L., Buyske, Steven, Demirkan, Ayse, Deng, Guohong, Ehret, Georg B., Feenstra, Bjarke, Feitosa, Mary F., Fischer, Krista, Goel, Anuj, Gong, Jian, Jackson, Anne U., Kanoni, Stavroula, Kleber, Marcus E., Kristiansson, Kati, Lim, Unhee, Lotay, Vaneet, Mangino, Massimo, Mateo Leach, Irene, Medina-Gomez, Carolina, Medland, Sarah E., Nalls, Michael A., Palmer, Cameron D., Pasko, Dorota, Pechlivanis, Sonali, Peters, Marjolein J., Prokopenko, Inga, Shungin, Dmitry, Stančáková, Alena, Strawbridge, Rona J., Ju Sung, Yun, Tanaka, Toshiko, Teumer, Alexander, Trompet, Stella, van der Laan, Sander W., van Setten, Jessica, Van Vliet-Ostaptchouk, Jana V., Wang, Zhaoming, Yengo, Loïc, Zhang, Weihua, Isaacs, Aaron, Albrecht, Eva, Ärnlöv, Johan, Arscott, Gillian M., Attwood, Antony P., Bandinelli, Stefania, Barrett, Amy, Bas, Isabelita N., Bellis, Claire, Bennett, Amanda J., Berne, Christian, Blagieva, Roza, Blüher, Matthias, Böhringer, Stefan, Bonnycastle, Lori L., Böttcher, Yvonne, Boyd, Heather A., Bruinenberg, Marcel, Caspersen, Ida H., Ida Chen, Yii-Der, Clarke, Robert, Warwick Daw, E., de Craen, Anton J. M., Delgado, Graciela, Dimitriou, Maria, Doney, Alex S. F., Eklund, Niina, Estrada, Karol, Eury, Elodie, Folkersen, Lasse, Fraser, Ross M., Garcia, Melissa E., Geller, Frank, Giedraitis, Vilmantas, Gigante, Bruna, Go, Alan S., Golay, Alain, Goodall, Alison H., Gordon, Scott D., Gorski, Mathias, Grabe, Hans-Jörgen, Grallert, Harald, Grammer, Tanja B., Gräler, Jürgen, Grönberg, Henrik, Groves, Christopher J., Gusto, Gaëlle, Haessler, Jeffrey, Hall, Per, Haller, Toomas, Hallmans, Goran, Hartman, Catharina A., Hassinen, Maija, Hayward, Caroline, Heard-Costa, Nancy L., Helmer, Quinta, Hengstenberg, Christian, Holmen, Oddgeir, Hottenga, Jouke-Jan, James, Alan L., Jeff, Janina M., Johansson, Åsa, Jolley, Jennifer, Juliusdottir, Thorhildur, Kinnunen, Leena, Koenig, Wolfgang, Koskenvuo, Markku, Kratzer, Wolfgang, Laitinen, Jaana, Lamina, Claudia, Leander, Karin, Lee, Nanette R., Lichtner, Peter, Lind, Lars, Lindström, Jaana, Sin Lo, Ken, Lobbens, Stéphane, Lorbeer, Roberto, Lu, Yingchang, Mach, François, Magnusson, Patrik K. E., Mahajan, Anubha, McArdle, Wendy L., McLachlan, Stela, Menni, Cristina, Merger, Sigrun, Mihailov, Evelin, Milani, Lili, Moayyeri, Alireza, Monda, Keri L., Morken, Mario A., Mulas, Antonella, Müller, Gabriele, Müller-Nurasyid, Martina, Musk, Arthur W., Nagaraja, Ramaiah, Nöthen, Markus M., Nolte, Ilja M., Pilz, Stefan, Rayner, Nigel W., Renstrom, Frida, Rettig, Rainer, Ried, Janina S., Ripke, Stephan, Robertson, Neil R., Rose, Lynda M., Sanna, Serena, Scharnagl, Hubert, Scholtens, Salome, Schumacher, Fredrick R., Scott, William R., Seufferlein, Thomas, Shi, Jianxin, Vernon Smith, Albert, Smolonska, Joanna, Stanton, Alice V., Steinthorsdottir, Valgerdur, Stirrups, Kathleen, Stringham, Heather M., Sundström, Johan, Swertz, Morris A., Swift, Amy J., Syvänen, Ann-Christine, Tan, Sian-Tsung, Tayo, Bamidele O., Thorand, Barbara, Thorleifsson, Gudmar, Tyrer, Jonathan P., Uh, Hae-Won, Vandenput, Liesbeth, Verhulst, Frank C., Vermeulen, Sita H., Verweij, Niek, Vonk, Judith M., Waite, Lindsay L., Warren, Helen R., Waterworth, Dawn, Weedon, Michael N., Wilkens, Lynne R., Willenborg, Christina, Wilsgaard, Tom, Wojczynski, Mary K., Wong, Andrew, Wright, Alan F., Zhang, Qunyuan, Brennan, Eoin P., Choi, Murim, Dastani, Zari, Drong, Alexander W., Eriksson, Per, Franco-Cereceda, Anders, Gådin, Jesper R., Gharavi, Ali G., Goddard, Michael E., Handsaker, Robert E., Huang, Jinyan, Karpe, Fredrik, Kathiresan, Sekar, Keildson, Sarah, Kiryluk, Krzysztof, Kubo, Michiaki, Lee, Jong-Young, Liang, Liming, Lifton, Richard P., Ma, Baoshan, McCarroll, Steven A., McKnight, Amy J., Min, Josine L., Moffatt, Miriam F., Montgomery, Grant W., Murabito, Joanne M., Nicholson, George, Nyholt, Dale R., Okada, Yukinori, Perry, John R. B., Dorajoo, Rajkumar, Reinmaa, Eva, Salem, Rany M., Sandholm, Niina, Scott, Robert A., Stolk, Lisette, Takahashi, Atsushi, Tanaka, Toshihiro, vanʼt Hooft, Ferdinand M., Vinkhuyzen, Anna A. E., Westra, Harm-Jan, Zheng, Wei, Zondervan, Krina T., Heath, Andrew C., Arveiler, Dominique, Bakker, Stephan J. L., Beilby, John, Bergman, Richard N., Blangero, John, Bovet, Pascal, Campbell, Harry, Caulfield, Mark J., Cesana, Giancarlo, Chakravarti, Aravinda, Chasman, Daniel I., Chines, Peter S., Collins, Francis S., Crawford, Dana C., Adrienne Cupples, L., Cusi, Daniele, Danesh, John, de Faire, Ulf, den Ruijter, Hester M., Dominiczak, Anna F., Erbel, Raimund, Erdmann, Jeanette, Eriksson, Johan G., Farrall, Martin, Felix, Stephan B., Ferrannini, Ele, Ferrières, Jean, Ford, Ian, Forouhi, Nita G., Forrester, Terrence, Franco, Oscar H., Gansevoort, Ron T., Gejman, Pablo V., Gieger, Christian, Gottesman, Omri, Gudnason, Vilmundur, Gyllensten, Ulf, Hall, Alistair S., Harris, Tamara B., Hattersley, Andrew T., Hicks, Andrew A., Hindorff, Lucia A., Hingorani, Aroon D., Hofman, Albert, Homuth, Georg, Kees Hovingh, G., Humphries, Steve E., Hunt, Steven C., Hyppönen, Elina, Illig, Thomas, Jacobs, Kevin B., Jarvelin, Marjo-Riitta, Jöckel, Karl-Heinz, Johansen, Berit, Jousilahti, Pekka, Wouter Jukema, J., Jula, Antti M., Kaprio, Jaakko, Kastelein, John J. P., Keinanen-Kiukaanniemi, Sirkka M., Kiemeney, Lambertus A., Knekt, Paul, Kooner, Jaspal S., Kooperberg, Charles, Kovacs, Peter, Kraja, Aldi T., Kumari, Meena, Kuusisto, Johanna, Lakka, Timo A., Langenberg, Claudia, Le Marchand, Loic, Lehtimäki, Terho, Lyssenko, Valeriya, Männistö, Satu, Marette, André, Matise, Tara C., McKenzie, Colin A., McKnight, Barbara, Moll, Frans L., Morris, Andrew D., Morris, Andrew P., Murray, Jeffrey C., Nelis, Mari, Ohlsson, Claes, Oldehinkel, Albertine J., Ong, Ken K., Madden, Pamela A. F., Pasterkamp, Gerard, Peden, John F., Peters, Annette, Postma, Dirkje S., Pramstaller, Peter P., Price, Jackie F., Qi, Lu, Raitakari, Olli T., Rankinen, Tuomo, Rao, D. C., Rice, Treva K., Ridker, Paul M., Rioux, John D., Ritchie, Marylyn D., Rudan, Igor, Salomaa, Veikko, Samani, Nilesh J., Saramies, Jouko, Sarzynski, Mark A., Schunkert, Heribert, Schwarz, Peter E. H., Sever, Peter, Shuldiner, Alan R., Sinisalo, Juha, Stolk, Ronald P., Strauch, Konstantin, Tönjes, Anke, Trégouët, David-Alexandre, Tremblay, Angelo, Tremoli, Elena, Virtamo, Jarmo, Vohl, Marie-Claude, Völker, Uwe, Waeber, Gérard, Willemsen, Gonneke, Witteman, Jacqueline C., Carola Zillikens, M., Adair, Linda S., Amouyel, Philippe, Asselbergs, Folkert W., Assimes, Themistocles L., Bochud, Murielle, Boehm, Bernhard O., Boerwinkle, Eric, Bornstein, Stefan R., Bottinger, Erwin P., Bouchard, Claude, Cauchi, Stéphane, Chambers, John C., Chanock, Stephen J., Cooper, Richard S., de Bakker, Paul I. W., Dedoussis, George, Ferrucci, Luigi, Franks, Paul W., Froguel, Philippe, Groop, Leif C., Haiman, Christopher A., Hamsten, Anders, Hui, Jennie, Hunter, David J., Hveem, Kristian, Kaplan, Robert C., Kivimaki, Mika, Kuh, Diana, Laakso, Markku, Liu, Yongmei, Martin, Nicholas G., März, Winfried, Melbye, Mads, Metspalu, Andres, Moebus, Susanne, Munroe, Patricia B., Njølstad, Inger, Oostra, Ben A., Palmer, Colin N. A., Pedersen, Nancy L., Perola, Markus, Pérusse, Louis, Peters, Ulrike, Power, Chris, Quertermous, Thomas, Rauramaa, Rainer, Rivadeneira, Fernando, Saaristo, Timo E., Saleheen, Danish, Sattar, Naveed, Schadt, Eric E., Schlessinger, David, Eline Slagboom, P., Snieder, Harold, Spector, Tim D., Thorsteinsdottir, Unnur, Stumvoll, Michael, Tuomilehto, Jaakko, Uitterlinden, André G., Uusitupa, Matti, van der Harst, Pim, Walker, Mark, Wallaschofski, Henri, Wareham, Nicholas J., Watkins, Hugh, Weir, David R., Wichmann, H-Erich, Wilson, James F., Zanen, Pieter, Borecki, Ingrid B., Deloukas, Panos, Fox, Caroline S., Heid, Iris M., OʼConnell, Jeffrey R., Strachan, David P., Stefansson, Kari, van Duijn, Cornelia M., Abecasis, Gonçalo R., Franke, Lude, Frayling, Timothy M., McCarthy, Mark I., Visscher, Peter M., Scherag, André, Willer, Cristen J., Boehnke, Michael, Mohlke, Karen L., Lindgren, Cecilia M., Beckmann, Jacques S., Barroso, Inês, North, Kari E., Ingelsson, Erik, Hirschhorn, Joel N., Loos, Ruth J. F., and Speliotes, Elizabeth K.

Published
2015
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40. Interferon regulatory factor-5-dependent CD11c+ macrophages contribute to the formation of rupture–prone atherosclerotic plaques.

Author

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

Subjects
ATHEROSCLEROTIC plaque, INTERFERONS, INTERFERON regulatory factors, MACROPHAGES, TRANSCRIPTION factors
Abstract

Aims Inflammation is a key factor in atherosclerosis. The transcription factor interferon regulatory factor-5 (IRF5) drives macrophages towards a pro-inflammatory state. We investigated the role of IRF5 in human atherosclerosis and plaque stability. Methods and results Bulk RNA sequencing from the Carotid Plaque Imaging Project biobank were used to mine associations between major macrophage associated genes and transcription factors and human symptomatic carotid disease. Immunohistochemistry, proximity extension assays, and Helios cytometry by time of flight (CyTOF) were used for validation. The effect of IRF5 deficiency on carotid plaque phenotype and rupture in ApoE−/− mice was studied in an inducible model of plaque rupture. Interferon regulatory factor-5 and ITGAX/CD11c were identified as the macrophage associated genes with the strongest associations with symptomatic carotid disease. Expression of IRF5 and ITGAX/CD11c correlated with the vulnerability index, pro-inflammatory plaque cytokine levels, necrotic core area, and with each other. Macrophages were the predominant CD11c-expressing immune cells in the plaque by CyTOF and immunohistochemistry. Interferon regulatory factor-5 immunopositive areas were predominantly found within CD11c+ areas with a predilection for the shoulder region, the area of the human plaque most prone to rupture. Accordingly, an inducible plaque rupture model of ApoE−/−Irf5−/− mice had significantly lower frequencies of carotid plaque ruptures, smaller necrotic cores, and less CD11c+ macrophages than their IRF5-competent counterparts. Conclusion Using complementary evidence from data from human carotid endarterectomies and a murine model of inducible rupture of carotid artery plaque in IRF5-deficient mice, we demonstrate a mechanistic link between the pro-inflammatory transcription factor IRF5, macrophage phenotype, plaque inflammation, and its vulnerability to rupture. [ABSTRACT FROM AUTHOR]

Published
2022
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41. William G. Hill (August 7, 1940 – December 17, 2021).

Author

Charlesworth, Brian and Goddard, Michael E.

Subjects
*QUANTITATIVE genetics, *GENETIC drift, *HERITABILITY, *POPULATION genetics, *GENETIC models, *LIFE sciences, *INBREEDING, *ANIMAL breeding
Abstract

William G. Hill, universally known as Bill Hill, died on December 17, 2021, aged 81. Bill also compared the observed and predicted long-term response to selection and concluded that these data had little power to distinguish among different models for quantitative genetic variation, including the "infinitesimal model" that postulates a very large number of loci with very small effects (Hill 2010). Appropriately, John Sved and Bill wrote an insightful I Perspective i in Genetics reviewing 100 years of work on LD (Sved and Hill 2018). In particular, he was the first to quantify the role of new mutations in producing the variability needed for long-term continued responses to selection, which are such a remarkable feature of many artificial selection experiments and animal and plant breeding programs (Hill 1982a,b; Keightley and Hill 1983). [Extracted from the article]

Published
2022
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42. CD200 Limits Monopoiesis and Monocyte Recruitment in Atherosclerosis.

Author

Kassiteridi, Christina, Cole, Jennifer E., Griseri, Thibault, Falck-Hansen, Mika, Goddard, Michael E., Seneviratne, Anusha N., Green, Patricia A., Park, Inhye, Shami, Annelie G., Pattarabanjird, Tanyaporn, Upadhye, Aditi, Taylor, Angela M., Handa, Ashok, Channon, Keith M., Lutgens, Esther, McNamara, Coleen A., Williams, Richard O., and Monaco, Claudia

Published
2021
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43. Putative Causal Variants Are Enriched in Annotated Functional Regions From Six Bovine Tissues.

Author

Prowse-Wilkins, Claire P., Wang, Jianghui, Xiang, Ruidong, Garner, Josie B., Goddard, Michael E., and Chamberlain, Amanda J.

Subjects
MAMMARY glands, GENETIC variation, IMMUNOPRECIPITATION, POST-translational modification, BOS, GENE expression, PHENOTYPES
Abstract

Genetic variants which affect complex traits (causal variants) are thought to be found in functional regions of the genome. Identifying causal variants would be useful for predicting complex trait phenotypes in dairy cows, however, functional regions are poorly annotated in the bovine genome. Functional regions can be identified on a genome-wide scale by assaying for post-translational modifications to histone proteins (histone modifications) and proteins interacting with the genome (e.g., transcription factors) using a method called Chromatin immunoprecipitation followed by sequencing (ChIP-seq). In this study ChIP-seq was performed to find functional regions in the bovine genome by assaying for four histone modifications (H3K4Me1, H3K4Me3, H3K27ac, and H3K27Me3) and one transcription factor (CTCF) in 6 tissues (heart, kidney, liver, lung, mammary and spleen) from 2 to 3 lactating dairy cows. Eighty-six ChIP-seq samples were generated in this study, identifying millions of functional regions in the bovine genome. Combinations of histone modifications and CTCF were found using ChromHMM and annotated by comparing with active and inactive genes across the genome. Functional marks differed between tissues highlighting areas which might be particularly important to tissue-specific regulation. Supporting the cis-regulatory role of functional regions, the read counts in some ChIP peaks correlated with nearby gene expression. The functional regions identified in this study were enriched for putative causal variants as seen in other species. Interestingly, regions which correlated with gene expression were particularly enriched for potential causal variants. This supports the hypothesis that complex traits are regulated by variants that alter gene expression. This study provides one of the largest ChIP-seq annotation resources in cattle including, for the first time, in the mammary gland of lactating cows. By linking regulatory regions to expression QTL and trait QTL we demonstrate a new strategy for identifying causal variants in cattle. [ABSTRACT FROM AUTHOR]

Published
2021
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44. Improving Genomic Prediction of Crossbred and Purebred Dairy Cattle.

Author

Khansefid, Majid, Goddard, Michael E., Haile-Mariam, Mekonnen, Konstantinov, Kon V., Schrooten, Chris, de Jong, Gerben, Jewell, Erica G., O'Connor, Erin, Pryce, Jennie E., Daetwyler, Hans D., and MacLeod, Iona M.

Subjects
CATTLE crossbreeding, FORECASTING, DAIRY cattle, PREDICTION models
Abstract

This study assessed the accuracy and bias of genomic prediction (GP) in purebred Holstein (H) and Jersey (J) as well as crossbred (H and J) validation cows using different reference sets and prediction strategies. The reference sets were made up of different combinations of 36,695 H and J purebreds and crossbreds. Additionally, the effect of using different sets of marker genotypes on GP was studied (conventional panel: 50k, custom panel enriched with, or close to, causal mutations: XT_50k, and conventional high-density with a limited custom set: pruned HDnGBS). We also compared the use of genomic best linear unbiased prediction (GBLUP) and Bayesian (emBayesR) models, and the traits tested were milk, fat, and protein yields. On average, by including crossbred cows in the reference population, the prediction accuracies increased by 0.01–0.08 and were less biased (regression coefficient closer to 1 by 0.02–0.16), and the benefit was greater for crossbreds compared to purebreds. The accuracy of prediction increased by 0.02 using XT_50k compared to 50k genotypes without affecting the bias. Although using pruned HDnGBS instead of 50k also increased the prediction accuracy by about 0.02, it increased the bias for purebred predictions in emBayesR models. Generally, emBayesR outperformed GBLUP for prediction accuracy when using 50k or pruned HDnGBS genotypes, but the benefits diminished with XT_50k genotypes. Crossbred predictions derived from a joint pure H and J reference were similar in accuracy to crossbred predictions derived from the two separate purebred reference sets and combined proportional to breed composition. However, the latter approach was less biased by 0.13. Most interestingly, using an equalized breed reference instead of an H-dominated reference, on average, reduced the bias of prediction by 0.16–0.19 and increased the accuracy by 0.04 for crossbred and J cows, with a little change in the H accuracy. In conclusion, we observed improved genomic predictions for both crossbreds and purebreds by equalizing breed contributions in a mixed breed reference that included crossbred cows. Furthermore, we demonstrate, that compared to the conventional 50k or high-density panels, our customized set of 50k sequence markers improved or matched the prediction accuracy and reduced bias with both GBLUP and Bayesian models. [ABSTRACT FROM AUTHOR]

Published
2020
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45. Quantifying the contribution of sequence variants with regulatory and evolutionary significance to 34 bovine complex traits.

Author

Ruidong Xiang, van den Berg, Irene, MacLeod, Iona M., Hayes, Benjamin J., Prowse-Wilkins, Claire P., Min Wang, Bolormaa, Sunduimijid, Zhiqian Liu, Rochfort, Simone J., Reich, Coralie M., Mason, Brett A., Jagt, Christy J. Vander, Daetwyler, Hans D., Lund, Mogens S., Chamberlain, Amanda J., and Goddard, Michael E.

Subjects
GENE expression, GENETIC regulation, HERITABILITY, CATTLE, COWS
Abstract

Many genome variants shaping mammalian phenotype are hypothesized to regulate gene transcription and/or to be under selection. However, most of the evidence to support this hypothesis comes from human studies. Systematic evidence for regulatory and evolutionary signals contributing to complex traits in a different mammalian model is needed. Sequence variants associated with gene expression (expression quantitative trait loci [eQTLs]) and concentration of metabolites (metabolic quantitative trait loci [mQTLs]) and under histone-modification marks in several tissues were discovered from multiomics data of over 400 cattle. Variants under selection and evolutionary constraint were identified using genome databases of multiple species. These analyses defined 30 sets of variants, and for each set, we estimated the genetic variance the set explained across 34 complex traits in 11,923 bulls and 32,347 cows with 17,669,372 imputed variants. The per-variant trait heritability of these sets across traits was highly consistent (r > 0.94) between bulls and cows. Based on the per-variant heritability, conserved sites across 100 vertebrate species and mQTLs ranked the highest, followed by eQTLs, young variants, those under histone-modification marks, and selection signatures. From these results,we defined a Functional-And-Evolutionary Trait Heritability (FAETH) score indicating the functionality and predicted heritability of each variant. In additional 7,551 cattle, the high FAETH-ranking variants had significantly increased genetic variances and genomic prediction accuracies in 3 production traits compared to the low FAETH-ranking variants. The FAETH framework combines the information of gene regulation, evolution, and trait heritability to rank variants, and the publicly available FAETH data provide a set of biological priors for cattle genomic selection worldwide. [ABSTRACT FROM AUTHOR]

Published
2019
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46. From R.A. Fisher's 1918 Paper to GWAS a Century Later.

Author

Visscher, Peter M. and Goddard, Michael E.

Subjects
*GENETICS -- History, *COMPARATIVE studies, *GENOMES, *HUMAN genome, *PERSONALITY
Abstract

The genetics and evolution of complex traits, including quantitative traits and disease, have been hotly debated ever since Darwin. A century ago, a paper from R.A. Fisher reconciled Mendelian and biometrical genetics in a landmark contribution that is now accepted as the main foundation stone of the field of quantitative genetics. Here, we give our perspective on Fisher's 1918 paper in the context of how and why it is relevant in today's genome era. We mostly focus on human trait variation, in part because Fisher did so too, but the conclusions are general and extend to other natural populations, and to populations undergoing artificial selection. [ABSTRACT FROM AUTHOR]

Published
2019
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47. Immune cell census in murine atherosclerosis: cytometry by time of flight illuminates vascular myeloid cell diversity.

Author

Cole, Jennifer E, Park, Inhye, Ahern, David J, Kassiteridi, Christina, Abeam, Dina Danso, Goddard, Michael E, Green, Patricia, Maffia, Pasquale, and Monaco, Claudia

Subjects
ATHEROSCLEROSIS, CYTOMETRY, MYELOID leukemia, TIME-of-flight spectrometry, LEUCOCYTES
Abstract

Aims Atherosclerosis is characterized by the abundant infiltration of myeloid cells starting at early stages of disease. Myeloid cells are key players in vascular immunity during atherogenesis. However, the subsets of vascular myeloid cells have eluded resolution due to shared marker expression and atypical heterogeneity in vascular tissues. We applied the high-dimensionality of mass cytometry to the study of myeloid cell subsets in atherosclerosis. Methods and results Apolipoprotein E-deficient (ApoE−/−) mice were fed a chow or a high fat (western) diet for 12 weeks. Single-cell aortic preparations were probed with a panel of 35 metal-conjugated antibodies using cytometry by time of flight (CyTOF). Clustering of marker expression on live CD45+ cells from the aortas of ApoE−/− mice identified 13 broad populations of leucocytes. Monocyte, macrophage, type 1 and type 2 conventional dendritic cell (cDC1 and cDC2), plasmacytoid dendritic cell (pDC), neutrophil, eosinophil, B cell, CD4+ and CD8+ T cell, γδ T cell, natural killer (NK) cell, and innate lymphoid cell (ILC) populations accounted for approximately 95% of the live CD45+ aortic cells. Automated clustering algorithms applied to the Lin-CD11blo-hi cells revealed 20 clusters of myeloid cells. Comparison between chow and high fat fed animals revealed increases in monocytes (both Ly6C+ and Ly6C−), pDC, and a CD11c+ macrophage subset with high fat feeding. Concomitantly, the proportions of CD206+ CD169+ subsets of macrophages were significantly reduced as were cDC2. Conclusions A CyTOF-based comprehensive mapping of the immune cell subsets within atherosclerotic aortas from ApoE−/− mice offers tools for myeloid cell discrimination within the vascular compartment and it reveals that high fat feeding skews the myeloid cell repertoire toward inflammatory monocyte-macrophage populations rather than resident macrophage phenotypes and cDC2 during atherogenesis. [ABSTRACT FROM AUTHOR]

Published
2018
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48. Meta-analysis of sequence-based association studies across three cattle breeds reveals 25 QTL for fat and protein percentages in milk at nucleotide resolution.

Author

Pausch, Hubert, Emmerling, Reiner, Gredler-Grandl, Birgit, Fries, Ruedi, Daetwyler, Hans D., and Goddard, Michael E.

Subjects
NUCLEOTIDES, NUCLEIC acids, DATA analysis, CATTLE breeds, LIVESTOCK breeds
Abstract

Background: Genotyping and whole-genome sequencing data have been generated for hundreds of thousands of cattle. International consortia used these data to compile imputation reference panels that facilitate the imputation of sequence variant genotypes for animals that have been genotyped using dense microarrays. Association studies with imputed sequence variant genotypes allow for the characterization of quantitative trait loci (QTL) at nucleotide resolution particularly when individuals from several breeds are included in the mapping populations. Results: We imputed genotypes for 28 million sequence variants in 17,229 cattle of the Braunvieh, Fleckvieh and Holstein breeds in order to compile large mapping populations that provide high power to identify QTL for milk production traits. Association tests between imputed sequence variant genotypes and fat and protein percentages in milk uncovered between six and thirteen QTL (P < 1e-8) per breed. Eight of the detected QTL were significant in more than one breed. We combined the results across breeds using meta-analysis and identified a total of 25 QTL including six that were not significant in the within-breed association studies. Two missense mutations in the ABCG2 (p.Y581S, rs43702337, P = 4.3e-34) and GHR (p.F279Y, rs385640152, P = 1.6e-74) genes were the top variants at QTL on chromosomes 6 and 20. Another known causal missense mutation in the DGAT1 gene (p.A232K, rs109326954, P = 8.4e-1436) was the second top variant at a QTL on chromosome 14 but its allelic substitution effects were inconsistent across breeds. It turned out that the conflicting allelic substitution effects resulted from flaws in the imputed genotypes due to the use of a multi-breed reference population for genotype imputation. Conclusions: Many QTL for milk production traits segregate across breeds and across-breed meta-analysis has greater power to detect such QTL than within-breed association testing. Association testing between imputed sequence variant genotypes and phenotypes of interest facilitates identifying causal mutations provided the accuracy of imputation is high. However, true causal mutations may remain undetected when the imputed sequence variant genotypes contain flaws. It is highly recommended to validate the effect of known causal variants in order to assess the ability to detect true causal mutations in association studies with imputed sequence variants. [ABSTRACT FROM AUTHOR]

Published
2017
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49. Application of a Bayesian non-linear model hybrid scheme to sequence data for genomic prediction and QTL mapping.

Author

Tingting Wang, Chen, Yi-Ping Phoebe, MacLeod, Iona M., Pryce, Jennie E., Goddard, Michael E., and Hayes, Ben J.

Subjects
NUCLEOTIDE sequencing, EXPECTATION-maximization algorithms, MARKOV chain Monte Carlo, CATTLE population genetics, MILK yield, CATTLE fertility
Abstract

Background: Using whole genome sequence data might improve genomic prediction accuracy, when compared with high-density SNP arrays, and could lead to identification of casual mutations affecting complex traits. For some traits, the most accurate genomic predictions are achieved with non-linear Bayesian methods. However, as the number of variants and the size of the reference population increase, the computational time required to implement these Bayesian methods (typically with Monte Carlo Markov Chain sampling) becomes unfeasibly long. Results: Here, we applied a new method, HyB_BR (for Hybrid BayesR), which implements a mixture model of normal distributions and hybridizes an Expectation-Maximization (EM) algorithm followed by Markov Chain Monte Carlo (MCMC) sampling, to genomic prediction in a large dairy cattle population with imputed whole genome sequence data. The imputed whole genome sequence data included 994,019 variant genotypes of 16,214 Holstein and Jersey bulls and cows. Traits included fat yield, milk volume, protein kg, fat% and protein% in milk, as well as fertility and heat tolerance. HyB_BR achieved genomic prediction accuracies as high as the full MCMC implementation of BayesR, both for predicting a validation set of Holstein and Jersey bulls (multi-breed prediction) and a validation set of Australian Red bulls (across-breed prediction). HyB_BR had a ten fold reduction in compute time, compared with the MCMC implementation of BayesR (48 hours versus 594 hours). We also demonstrate that in many cases HyB_BR identified sequence variants with a high posterior probability of affecting the milk production or fertility traits that were similar to those identified in BayesR. For heat tolerance, both HyB_BR and BayesR found variants in or close to promising candidate genes associated with this trait and not detected by previous studies. Conclusions: The results demonstrate that HyB_BR is a feasible method for simultaneous genomic prediction and QTL mapping with whole genome sequence in large reference populations. [ABSTRACT FROM AUTHOR]

Published
2017
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50. Multiple-trait QTL mapping and genomic prediction for wool traits in sheep.

Author

Bolormaa, Sunduimijid, Swan, Andrew A., Brown, Daniel J., Hatcher, Sue, Moghaddar, Nasir, van der Werf, Julius H., Goddard, Michael E., and Daetwyler, Hans D.

Subjects
SHEEP genetics, GENOMES, SHEEP breeding
Abstract

Background: The application of genomic selection to sheep breeding could lead to substantial increases in profitability of wool production due to the availability of accurate breeding values from single nucleotide polymorphism (SNP) data. Several key traits determine the value of wool and influence a sheep's susceptibility to fleece rot and fly strike. Our aim was to predict genomic estimated breeding values (GEBV) and to compare three methods of combining information across traits to map polymorphisms that affect these traits. Methods: GEBV for 5726 Merino and Merino crossbred sheep were calculated using BayesR and genomic best linear unbiased prediction (GBLUP) with real and imputed 510,174 SNPs for 22 traits (at yearling and adult ages) including wool production and quality, and breech conformation traits that are associated with susceptibility to fly strike. Accuracies of these GEBV were assessed using fivefold cross-validation. We also devised and compared three approximate multi-trait analyses to map pleiotropic quantitative trait loci (QTL): a multi-trait genome-wide association study and two multi-trait methods that use the output from BayesR analyses. One BayesR method used local GEBV for each trait, while the other used the posterior probabilities that a SNP had an effect on each trait. Results: BayesR and GBLUP resulted in similar average GEBV accuracies across traits (~0.22). BayesR accuracies were highest for wool yield and fibre diameter (>0.40) and lowest for skin quality and dag score (<0.10). Generally, accuracy was higher for traits with larger reference populations and higher heritability. In total, the three multi-trait analyses identified 206 putative QTL, of which 20 were common to the three analyses. The two BayesR multi-trait approaches mapped QTL in a more defined manner than the multi-trait GWAS. We identified genes with known effects on hair growth (i.e. FGF5, STAT3, KRT86, and ALX4) near SNPs with pleiotropic effects on wool traits. Conclusions: The mean accuracy of genomic prediction across wool traits was around 0.22. The three multi-trait analyses identified 206 putative QTL across the ovine genome. Detailed phenotypic information helped to identify likely candidate genes. [ABSTRACT FROM AUTHOR]

Published
2017
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