Faculty Profile

Goncalo Abecasis

Goncalo Abecasis, DPhil

  • Felix E. Moore Collegiate Professor of Biostatistics

Goncalo Abecasis received his DPhil in Human Genetics from the University of Oxford in 2001 and joined the faculty at the University of Michigan in the same year. Abecasis' research focuses on the development of statistical tools for the identification and study of genetic variants important in human disease. Software developed by Abecasis at the University of Michigan is used in several hundred gene-mapping projects around the world.

  • DPhil, Human Genetics, University of Oxford, 2001
  • BSc (Honours), Genetics, University of Leeds, 1997

Research Interests:
The focus of my research is the identification and characterization of genes determining human variation and disease. In particular, I have focused on developing analytical methods and statistical tools that will facilitate the mapping of complex traits and allow geneticists to realize the benefits of new high-throughput technologies in the lab. Much of my research has focused on the use of linkage disequilibrium in the mapping of complex disease susceptibility genes. Linkage disequilibrium based mapping strategies search for short segments of ancestral chromosomes shared among present day individuals. High-throughput technologies allow fine-scale characterization of genetic variation and have made these searches possible.

Research Projects:
Quantitative Trait Mapping: I have proposed a test of association in general pedigrees that uses all available information on each individual's ancestry to separate population substructure from linkage disequilibrium (Abecasis et al. 2000a; Abecasis et al. 2000b). Using this model, I have investigated the role of various angiotensin-converting enzyme (ACE) gene polymorphisms on circulating ACE levels in blood. The ACE gene, and an Alu insertion-deletion polymorphism it contains, has generated intense interest because of its possible role in hypertension and heart disease. I evaluated the effect of variants in the gene in Jamaican and British samples. The data show that the I/D polymorphism is not the major functional variant in this gene and suggest a number of nearby variants as candidates (McKenzie et al. 2001).

Characterizing Genetic Variation in Humans: The success of mapping strategies based on allelic association and linkage disequilibrium is going to depend, to a large part, on the extent of linkage disequilibrium in the populations being studied. The observation that linkage disequilibrium extends for long distances and that most chromosomes are mosaics of relatively common short haplotypes is a critical underpinning of the NIH haplotype map initiative. I have developed graphical tools that allow scientists to explore and summarize patterns of disequilibrium in regions of interest (Abecasis and Cookson 2000). In addition I have provided some of the first detailed, large-scale descriptions of linkage disequilibrium in the genome (Moffatt et al. 2000; Abecasis et al. 2001d). My observations demonstrated that linkage disequilibrium extends further than theoretical predictions in much of the genome and suggested that it is organized in cluster-like structures.

Computational Tools: A significant challenge in the fine-scale characterization of genomic variation is the sheer amount of data that must be considered. Most current analytical methods focus on the analysis of relatively small numbers of markers (10 to 50) and cannot handle the large numbers of linked variants (1000) that can be assayed using high-throughput technologies. I have described and implemented novel, very efficient algorithms that exploit the structure of genetic data in pedigrees (Abecasis et al. 2002) and populations (Abecasis et al. 2001c) to provide extremely fast solutions to traditional problems. Specifically, I showed that allowing for the small number of recombination events between consecutive markers could greatly simplify likelihood calculations in dense genetic maps. I also described a representation of gene flow within pedigrees that uses sparse binary trees to summarize redundancies in genetic data and further simplifies likelihood calculations (Abecasis et al. 2002). Separately, I showed how population and family data could be combined in haplotype estimation and proposed a divide-and-conquer algorithm for tackling longer haplotypes (Abecasis et al. 2001c). Together, these methods can also handle very large datasets and enable construction of the first chromosome wide linkage disequilibrium map (Dawson et al. 2002).

Practical Challenges: A final aspect of my research concerns the challenges of handling real, sometimes imperfect data. I have investigated the effects of genotyping error on quantitative trait analyses (Abecasis et al. 2001a). In addition, I have proposed strategies for genotype error detection (Abecasis et al. 2002) and identifying misspecified relationships (Abecasis et al. 2001b). The motivation for my research comes from the day-to-day challenges encountered in the gene-mapping projects where I am involved. This research has been motivated by collaboration with researchers seeking to identify genes predisposing to diabetes (Dr. Michael Boehnke, University of Michigan), glaucoma and age-related macular degeneration (Drs. Julia Richard and Anand Swaroop, University of Michigan), schizophrenia (Dr. Maria Karayiorgou, Rockefeller University) and aging related traits (David Schlessinger, National Institutes of Aging).

The Future: I believe that further successes in the area of complex disease gene identification will require even better computational tools and further imhproved statistical methods that are able to tackle the large single nucleotide polymorphism datasets (SNP) now being collected. Some of the challenges for these tools include haplotype estimation, evaluation of linkage evidence through simulation, and improved detection and modeling of possible genotype and pedigree errors.

Graham SE, Nielsen JB, Zawistowski M, Zhou W, Fritsche LG, Gabrielsen ME, Skogholt AH, Surakka I, Hornsby WE, Fermin D, Larach DB, Kheterpal S, Brummett CM, Lee S, Kang HM, Abecasis GR, Romundstad S, Hallan S, Sampson MG, Hveem K, Willer CJ. Sex-specific and pleiotropic effects underlying kidney function identified from GWAS meta-analysis. Nat Commun. 2019 Apr 23;10(1):1847. doi: 10.1038/s41467-019-09861-z.

Bien SA, Su YR, Conti DV, Harrison TA, Qu C, Guo X, Lu Y, Albanes D, Auer PL, Banbury BL, Berndt SI, Bzieau S, Brenner H, Buchanan DD, Caan BJ, Campbell PT, Carlson CS, Chan AT, Chang-Claude J, Chen S, Connolly CM, Easton DF, Feskens EJM, Gallinger S, Giles GG, Gunter MJ, Hampe J, Huyghe JR, Hoffmeister M, Hudson TJ, Jacobs EJ, Jenkins MA, Kampman E, Kang HM, Kuhn T, Kury S, Lejbkowicz F, Le Marchand L, Milne RL, Li L, Li CI, Lindblom A, Lindor NM, Martin V, McNeil CE, Melas M, Moreno V, Newcomb PA, Offit K, Pharaoh PDP, Potter JD, Qu C, Riboli E, Rennert G, Sala N, Schafmayer C, Scacheri PC, Schmit SL, Severi G, Slattery ML, Smith JD, Trichopoulou A, Tumino R, Ulrich CM, van Duijnhoven FJB, Van Guelpen B, Weinstein SJ, White E, Wolk A, Woods MO, Wu AH, Abecasis GR, Casey G, Nickerson DA, Gruber SB, Hsu L, Zheng W, Peters U. Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer. Hum Genet. 2019 Apr;138(4):307-326. doi: 10.1007/s00439-019-01989-8. Epub 2019 Feb 28.

Justice AE, Karaderi T, Highland HM, Young KL, Graff M, Lu Y, Turcot V, Auer PL, Fine RS, Guo X, Schurmann C, Lempradl A, Marouli E, Mahajan A, Winkler TW, Locke AE, Medina-Gomez C, sko T, Vedantam S, Giri A, Lo KS, Alfred T, Mudgal P, Ng MCY, Heard-Costa NL, Feitosa MF, Manning AK, Willems SM, Sivapalaratnam S, Abecasis G, Alam DS, Allison M, Amouyel P, Arzumanyan Z, Balkau B, Bastarache L, Bergmann S, Bielak LF, Bluher M, Boehnke M, Boeing H, Boerwinkle E, Boger CA, Bork-Jensen J, Bottinger EP, Bowden DW, Brandslund I, Broer L, Burt AA, Butterworth AS, Caulfield MJ, Cesana G, Chambers JC, Chasman DI, Chen YI, Chowdhury R, Christensen C, Chu AY, Collins FS, Cook JP, Cox AJ, Crosslin DS, Danesh J, de Bakker PIW, Denus S, Mutsert R, Dedoussis G, Demerath EW, Dennis JG, Denny JC, Angelantonio ED, Dorr M, Drenos F, Dub MP, Dunning AM, Easton DF, Elliott P, Evangelou E, Farmaki AE, Feng S, Ferrannini E, Ferrieres J, Florez JC, Fornage M, Fox CS, Franks PW, Friedrich N, Gan W, Gandin I, Gasparini P, Giedraitis V, Girotto G, Gorski M, Grallert H, Grarup N, Grove ML, Gustafsson S, Haessler J, Hansen T, Hattersley AT, Hayward C, Heid IM, Holmen OL, Hovingh GK, Howson JMM, Hu Y, Hung YJ, Hveem K, Ikram MA, Ingelsson E, Jackson AU, Jarvik GP, Jia Y, Jorgensen T, Jousilahti P, Justesen JM, Kahali B, Karaleftheri M, Kardia SLR, Karpe F, Kee F, Kitajima H, Komulainen P, Kooner JS, Kovacs P, Kramer BK, Kuulasmaa K, Kuusisto J, Laakso M, Lakka TA, Lamparter D, Lange LA, Langenberg C, Larson EB, Lee NR, Lee WJ, Lehtimaki T, Lewis CE, Li H, Li J, Li-Gao R, Lin LA, Lin X, Lind L, Lindstrom J, Linneberg A, Liu CT, Liu DJ, Luan J, Lyytikainen LP, MacGregor S, Magi R, Mannisto S, Marenne G, Marten J, Masca NGD, McCarthy MI, Meidtner K, Mihailov E, Moilanen L, Moitry M, Mook-Kanamori DO, Morgan A, Morris AP, Muller-Nurasyid M, Munroe PB, Narisu N, Nelson CP, Neville M, Ntalla I, O'Connell JR, Owen KR, Pedersen O, Peloso GM, Pennell CE, Perola M, Perry JA, Perry JRB, Pers TH, Ewing A, Polasek O, Raitakari OT, Rasheed A, Raulerson CK, Rauramaa R, Reilly DF, Reiner AP, Ridker PM, Rivas MA, Robertson NR, Robino A, Rudan I, Ruth KS, Saleheen D, Salomaa V, Samani NJ, Schreiner PJ, Schulze MB, Scott RA, Segura-Lepe M, Sim X, Slater AJ, Small KS, Smith BH, Smith JA, Southam L, Spector TD, Speliotes EK, Stefansson K, Steinthorsdottir V, Stirrups KE, Strauch K, Stringham HM, Stumvoll M, Sun L, Surendran P, Swart KMA, Tardif JC, Taylor KD, Teumer A, Thompson DJ, Thorleifsson G, Thorsteinsdottir U, Thuesen BH, Tonjes A, Torres M, Tsafantakis E, Tuomilehto J, Uitterlinden AG, Uusitupa M, van Duijn CM, Vanhala M, Varma R, Vermeulen SH, Vestergaard H, Vitart V, Vogt TF, Vuckovic D, Wagenknecht LE, Walker M, Wallentin L, Wang F, Wang CA, Wang S, Wareham NJ, Warren HR, Waterworth DM, Wessel J, White HD, Willer CJ, Wilson JG, Wood AR, Wu Y, Yaghootkar H, Yao J, Yerges-Armstrong LM, Young R, Zeggini E, Zhan X, Zhang W, Zhao JH, Zhao W, Zheng H, Zhou W, Zillikens MC; CHD Exome+ Consortium; Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium; EPIC-CVD Consortium; ExomeBP Consortium; Global Lipids Genetic Consortium; GoT2D Genes Consortium; InterAct; ReproGen Consortium; T2D-Genes Consortium; MAGIC Investigators, Rivadeneira F, Borecki IB, Pospisilik JA, Deloukas P, Frayling TM, Lettre G, Mohlke KL, Rotter JI, Kutalik Z, Hirschhorn JN, Cupples LA, Loos RJF, North KE, Lindgren CM. Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution. Nat Genet. 2019 Mar;51(3):452-469. doi: 10.1038/s41588-018-0334-2. Epub 2019 Feb 18.

Ratnapriya R, Sosina OA, Starostik MR, Kwicklis M, Kapphahn RJ, Fritsche LG, Walton A, Arvanitis M, Gieser L, Pietraszkiewicz A, Montezuma SR, Chew EY, Battle A, Abecasis GR, Ferrington DA, Chatterjee N, Swaroop A. Retinal transcriptome and eQTL analyses identify genes associated with age-related macular degeneration. Nat Genet. 2019 Apr;51(4):606-610. doi: 10.1038/s41588-019-0351-9. Epub 2019 Feb 11.

Brazel DM, Jiang Y, Hughey JM, Turcot V, Zhan X, Gong J, Batini C, Weissenkampen JD, Liu M; CHD Exome+ Consortium; Consortium for Genetics of Smoking Behaviour, Barnes DR, Bertelsen S, Chou YL, Erzurumluoglu AM, Faul JD, Haessler J, Hammerschlag AR, Hsu C, Kapoor M, Lai D, Le N, de Leeuw CA, Loukola A, Mangino M, Melbourne CA, Pistis G, Qaiser B, Rohde R, Shao Y, Stringham H, Wetherill L, Zhao W, Agrawal A, Bierut L, Chen C, Eaton CB, Goate A, Haiman C, Heath A, Iacono WG, Martin NG, Polderman TJ, Reiner A, Rice J, Schlessinger D, Scholte HS, Smith JA, Tardif JC, Tindle HA, van der Leij AR, Boehnke M, Chang-Claude J, Cucca F, David SP, Foroud T, Howson JMM, Kardia SLR, Kooperberg C, Laakso M, Lettre G, Madden P, McGue M, North K, Posthuma D, Spector T, Stram D, Tobin MD, Weir DR, Kaprio J, Abecasis GR, Liu DJ, Vrieze S. Exome Chip Meta-analysis Fine Maps Causal Variants and Elucidates the Genetic Architecture of Rare Coding Variants in Smoking and Alcohol Use. Biol Psychiatry. 2018 Dec 6. pii: S0006-3223(18)32056-0. doi: 10.1016/j.biopsych.2018.11.024. [Epub ahead of print]

Liu M, Jiang Y, Wedow R, Li Y, Brazel DM, Chen F, Datta G, Davila-Velderrain J, McGuire D, Tian C, Zhan X; 23andMe Research Team; HUNT All-In Psychiatry, Choquet H, Docherty AR, Faul JD, Foerster JR, Fritsche LG, Gabrielsen ME, Gordon SD, Haessler J, Hottenga JJ, Huang H, Jang SK, Jansen PR, Ling Y, Magi R, Matoba N, McMahon G, Mulas A, Orru V, Palviainen T, Pandit A, Reginsson GW, Skogholt AH, Smith JA, Taylor AE, Turman C, Willemsen G, Young H, Young KA, Zajac GJM, Zhao W, Zhou W, Bjornsdottir G, Boardman JD, Boehnke M, Boomsma DI, Chen C, Cucca F, Davies GE, Eaton CB, Ehringer MA, Esko T, Fiorillo E, Gillespie NA, Gudbjartsson DF, Haller T, Harris KM, Heath AC, Hewitt JK, Hickie IB, Hokanson JE, Hopfer CJ, Hunter DJ, Iacono WG, Johnson EO, Kamatani Y, Kardia SLR, Keller MC, Kellis M, Kooperberg C, Kraft P, Krauter KS, Laakso M, Lind PA, Loukola A, Lutz SM, Madden PAF, Martin NG, McGue M, McQueen MB, Medland SE, Metspalu A, Mohlke KL, Nielsen JB, Okada Y, Peters U, Polderman TJC, Posthuma D, Reiner AP, Rice JP, Rimm E, Rose RJ, Runarsdottir V, Stallings MC, Stancakova A, Stefansson H, Thai KK, Tindle HA, Tyrfingsson T, Wall TL, Weir DR, Weisner C, Whitfield JB, Winsvold BS, Yin J, Zuccolo L, Bierut LJ, Hveem K, Lee JJ, MunafoMR, Saccone NL, Willer CJ, Cornelis MC, David SP, Hinds DA, Jorgenson E, Kaprio J, Stitzel JA, Stefansson K, Thorgeirsson TE, Abecasis G, Liu DJ, Vrieze S. Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet. 2019 Feb;51(2):237-244. doi: 10.1038/s41588-018-0307-5. Epub 2019 Jan 14.

Erzurumluoglu AM, Liu M, Jackson VE, Barnes DR, Datta G, Melbourne CA, Young R, Batini C, Surendran P, Jiang T, Adnan SD, Afaq S, Agrawal A, Altmaier E, Antoniou AC, Asselbergs FW, Baumbach C, Bierut L, Bertelsen S, Boehnke M, Bots ML, Brazel DM, Chambers JC, Chang-Claude J, Chen C, Corley J, Chou YL, David SP, de Boer RA, de Leeuw CA, Dennis JG, Dominiczak AF, Dunning AM, Easton DF, Eaton C, Elliott P, Evangelou E, Faul JD, Foroud T, Goate A, Gong J, Grabe HJ, Haessler J, Haiman C, Hallmans G, Hammerschlag AR, Harris SE, Hattersley A, Heath A, Hsu C, Iacono WG, Kanoni S, Kapoor M, Kaprio J, Kardia SL, Karpe F, Kontto J, Kooner JS, Kooperberg C, Kuulasmaa K, Laakso M, Lai D, Langenberg C, Le N, Lettre G, Loukola A, Luan J, Madden PAF, Mangino M, Marioni RE, Marouli E, Marten J, Martin NG, McGue M, Michailidou K, Mihailov E, Moayyeri A, Moitry M, Muller-Nurasyid M, Naheed A, Nauck M, Neville MJ, Nielsen SF, North K, Perola M, Pharoah PDP, Pistis G, Polderman TJ, Posthuma D, Poulter N, Qaiser B, Rasheed A, Reiner A, Renstrom F, Rice J, Rohde R, Rolandsson O, Samani NJ, Samuel M, Schlessinger D, Scholte SH, Scott RA, Sever P, Shao Y, Shrine N, Smith JA, Starr JM, Stirrups K, Stram D, Stringham HM, Tachmazidou I, Tardif JC, Thompson DJ, Tindle HA, Tragante V, Trompet S, Turcot V, Tyrrell J, Vaartjes I, van der Leij AR, van der Meer P, Varga TV, Verweij N, Volzke H, Wareham NJ, Warren HR, Weir DR, Weiss S, Wetherill L, Yaghootkar H, Yavas E, Jiang Y, Chen F, Zhan X, Zhang W, Zhao W, Zhao W, Zhou K, Amouyel P, Blankenberg S, Caulfield MJ, Chowdhury R, Cucca F, Deary IJ, Deloukas P, Di Angelantonio E, Ferrario M, Ferrieres J, Franks PW, Frayling TM, Frossard P, Hall IP, Hayward C, Jansson JH, Jukema JW, Kee F, Mannisto S, Metspalu A, Munroe PB, Nordestgaard BG, Palmer CNA, Salomaa V, Sattar N, Spector T, Strachan DP; Understanding Society Scientific Group, EPIC-CVD, GSCAN, Consortium for Genetics of Smoking Behaviour, CHD Exome+ consortium, van der Harst P, Zeggini E, Saleheen D, Butterworth AS, Wain LV, Abecasis GR, Danesh J, Tobin MD, Vrieze S, Liu DJ, Howson JMM. Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci. Mol Psychiatry. 2019 Jan 7. doi: 10.1038/s41380-018-0313-0. [Epub ahead of print]

Gagliano SA, Sengupta S, Sidore C, Maschio A, Cucca F, Schlessinger D, Abecasis GR. Relative impact of indels versus SNPs on complex disease. Genet Epidemiol. 2019 Feb;43(1):112-117. doi: 10.1002/gepi.22175. Epub 2018 Nov 22.

Choi SH, Weng LC, Roselli C, Lin H, Haggerty CM, Shoemaker MB, Barnard J, Arking DE, Chasman DI, Albert CM, Chaffin M, Tucker NR, Smith JD, Gupta N, Gabriel S, Margolin L, Shea MA, Shaffer CM, Yoneda ZT, Boerwinkle E, Smith NL, Silverman EK, Redline S, Vasan RS, Burchard EG, Gogarten SM, Laurie C, Blackwell TW, Abecasis G, Carey DJ, Fornwalt BK, Smelser DT, Baras A, Dewey FE, Jaquish CE, Papanicolaou GJ, Sotoodehnia N, Van Wagoner DR, Psaty BM, Kathiresan S, Darbar D, Alonso A, Heckbert SR, Chung MK, Roden DM, Benjamin EJ, Murray MF, Lunetta KL, Lubitz SA, Ellinor PT; DiscovEHR study and the NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium. Association Between Titin Loss-of-Function Variants and Early-Onset Atrial Fibrillation. JAMA. 2018 Dec 11;320(22):2354-2364. doi: 10.1001/jama.2018.18179.

Patrick MT, Stuart PE, Raja K, Gudjonsson JE, Tejasvi T, Yang J, Chandran V, Das S, Callis-Duffin K, Ellinghaus E, Enerback C, Esko T, Franke A, Kang HM, Krueger GG, Lim HW, Rahman P, Rosen CF, Weidinger S, Weichenthal M, Wen X, Voorhees JJ, Abecasis GR, Gladman DD, Nair RP, Elder JT, Tsoi LC. Genetic signature to provide robust risk assessment of psoriatic arthritis development in psoriasis patients. Nat Commun. 2018 Oct 9;9(1):4178. doi: 10.1038/s41467-018-06672-6.

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