Dr Alexessander Couto Alves

Lecturer in Bioinformatics and Statistical Genomics
PhD, MSc, MEng
+44 (0)1483 684658
14AX02, by appointment


Areas of specialism

Genome wide association studies (GWAS); Expression quantitative trait loci studies (eQTL, and GxE-eQTL); Gene expression association studies; Statistical modeling of continuous and discrete outcomes; Regression and applied graphical models to health data; Variable selection and model averaging; Applied machine learning to health and biological data

University roles and responsibilities

  • Head of Bioinformatics Core Facility


    Research interests

    Research collaborations

    Indicators of esteem

    • Personal research awards and fellowships

      2018 Research Fellow, School of Public Health, Imperial College London

      2015 Research Fellow, Dept of Twin Research and Genetic Epidemiology, King’s College London

      2007 PhD Fellowship of the Portuguese National Science and Technology Foundation

    • Editorial board member:

    • Keynote and plenary addresses at conferences

      2014 Systems biology and functional analysis of disease genes. European academy of allergy and clinical immunology congress.

      • Conference organisation

        2007 Co-chair Workshop on Computational Methods in Bioinformatics and Systems Biology. As part of the Portuguese Conference on Artificial

        2005 Co-chair Workshop on Computational Methods in Bioinformatics. As part of the Portuguese Conference on Artificial Intelligence.

      • Reviewer for:

        • Journal of the Royal Statistical Society
        • International Journal of Epidemiology
        • International Journal of Allergy and Clinical Immunology
        • Nature Scientific Reports
        • Nature Communications Biology
        • Genome Medicine
        • Annals of Human Genetics


      Completed postgraduate research projects I have supervised

      Postgraduate research supervision




      Couto Alves, A., De Silva, N. M. G.,  Karhunen, V. , Sovio, U., Das, S., et al. GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI. Science Advances (2019).

      Couto Alves, A.; Glastonbury, CA; Moustafa, JSES; Small, KS Fasting and time of day independently modulate circadian rhythm relevant gene expression in adipose and skin tissue. BMC genomics (2018)

      Demenais, F., Margaritte-Jeannin, P., Barnes, K. C., Cookson, W. O., Altmüller, J., Ang, W., Barr, R. G., Beaty, T. H., Becker, A. B., Beilby, J., et al. Multiancestry association study identifies new asthma risk loci that colocalize with immune-cell enhancer marks. Nature genetics 50, 1 (2018), 42.

      Liu, D. J., Peloso, G. M., Yu, H., Butterworth, A. S., Wang, X., Mahajan, A., Saleheen, D., Emdin, C., Alam, D., Couto Alves, A., et al. Exome-wide association study of plasma lipids in> 300,000 individuals. Nature genetics 49, 12 (2017), 1758.

      Ried, J. S., Chu, A. Y., Bragg-Gresham, J. L., Van Dongen, J.,Huffman, J. E., Ahluwalia, T. S., Cadby, G., Eklund, N., Eriksson, J., Esko, T., et al. A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape. Nature communications 7 (2016), 13357.

      Paternoster, L., Standl, M., Waage, J., Baurecht, H., Hotze, M., Strachan, D. P., Curtin, J. A., Bønnelykke, K., Tian, C., Takahashi, A., et al. Multi-ethnic genome-wide association study of 21,000 cases and 95,000 controls identifies new risk loci for atopic dermatitis. Nature genetics 47, 12 (2015), 1449

      Kato, N., Loh, M., Takeuchi, F., Verweij, N., Wang, X., Zhang, W., Kelly, T. N., Saleheen, D., Lehne, B., Leach, I. M., et al. Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for dna methylation. Nature genetics 47, 11 (2015), 1282–1293

      Loth, D. W., Artigas, M. S., Gharib, S. A., Wain, L. V., Franceschini, N., Koch, B., Pottinger, T. D., Smith, A. V., Duan, Q., Oldmeadow, C., et al. Genome-wide association analysis identifies six new loci associated with forced vital capacity. Nature genetics 46, 7 (2014), 669–677.

      Bønnelykke, K., Matheson, M. C., Pers, T. H., Granell, R., Strachan, D. P., Couto Alves, A., Linneberg, A., Curtin, J. A., Warrington, N. M., Standl, M., et al. Meta-analysis of genomewide association studies identifies ten loci influencing allergic sensitization. Nature genetics 45, 8 (2013), 902–906.

      Paternoster, L., Standl, M., Chen, C.-M., Ramasamy, A., Bønnelykke, K., Duijts, L., Ferreira, M. A., Couto Alves, A., Thyssen, J. P., Albrecht, E., et al. Meta-analysis of genome-wide association studies identifies three new risk loci for atopic dermatitis. Nature genetics 44, 2 (2012), 187–192.

      Chen Li, Svetlana Stoma, Luca A. Lotta, Sophie Warner, Eva Albrecht, Alessandra Allione, Pascal P. Arp, Linda Broer, Jessica L. Bruxton, Alexessander Da Silva Couto Alves, Joris Deelen, Iryna O. Fedko, Scott D. Gordon, Tao Jiang, Robert Karlsson, Nicola Kerrison, Taylor K. Loe, Massimo Mangino, Yuri Milaneschi, Benjamin Miraglio, Natalia Pervjakova, Alessia Russo, Ida Surakka, Ashley van der Spek, Josine E. Verhoeven, Najaf Amin, Marian Beekman, Alexandra I. Blakemore, Frederico Canzian, Stephen E. Hamby, Jouke-Jan Hottenga, Peter D. Jones, Pekka Jousilahti, Reedik Magi, Sarah E. Medland, Grant W. Montgomery, Dale R. Nyholt, Markus Perola, Kirsi H. Pietilainen, Veikko Salomaa, Elina Sillanpaa, H. Eka Suchiman, Diana van Heemst, Gonneke Willemsen, Antonio Agudo, Heiner Boeing, Dorret I. Boomsma, Maria-Dolores Chirlaque, Guy Fagherazzi, Pietro Ferrari, Paul Franks, Christian Gieger, Johan Gunnar Eriksson, Marc Gunter, Sara Hagg, Iiris Hovatta, Liher Imaz, Jaakko Kaprio, Rudolf Kaaks, Timothy Key (2020)Genome-wide Association Analysis in Humans Links Nucleotide Metabolism to Leukocyte Telomere Length, In: American Journal of Human Genetics106(3)pp. 389-404 Elsevier

      Leukocyte telomere length (LTL) is a heritable biomarker of genomic aging. In this study, we perform a genome-wide meta-analysis of LTL by pooling densely genotyped and imputed association results across large-scale European-descent studies including up to 78,592 individuals. We identify 49 genomic regions at a false dicovery rate (FDR) < 0.05 threshold and prioritize genes at 31, with five highlighting nucleotide metabolism as an important regulator of LTL. We report six genome-wide significant loci in or near SENP7, MOB1B, CARMIL1, PRRC2A, TERF2, and RFWD3, and our results support recently identified PARP1, POT1, ATM, and MPHOSPH6 loci. Phenome-wide analyses in >350,000 UK Biobank participants suggest that genetically shorter telomere length increases the risk of hypothyroidism and decreases the risk of thyroid cancer, lymphoma, and a range of proliferative conditions. Our results replicate previously reported associations with increased risk of coronary artery disease and lower risk for multiple cancer types. Our findings substantially expand current knowledge on genes that regulate LTL and their impact on human health and disease.

      Tom A. Bond, Rebecca C. Richmond, Ville Karhunen, Gabriel Cuellar-Partida, Maria Carolina Borges, Verena Zuber, Alexessander Couto Alves, Dan Mason, Tiffany C. Yang, Marc J. Gunter, Abbas Dehghan, Ioanna Tzoulaki, Sylvain Sebert, David M. Evans, Alex M. Lewin, Paul F. O'Reilly, Deborah A. Lawlor, Marjo-Riitta Jarvelin, Alexessander Couto Alves (2022)Exploring the causal effect of maternal pregnancy adiposity on offspring adiposity: Mendelian randomisation using polygenic risk scores, In: BMC medicine20(1)34pp. 34-34 Springer Nature

      Background Greater maternal adiposity before or during pregnancy is associated with greater offspring adiposity throughout childhood, but the extent to which this is due to causal intrauterine or periconceptional mechanisms remains unclear. Here, we use Mendelian randomisation (MR) with polygenic risk scores (PRS) to investigate whether associations between maternal pre-/early pregnancy body mass index (BMI) and offspring adiposity from birth to adolescence are causal. Methods We undertook confounder adjusted multivariable (MV) regression and MR using mother-offspring pairs from two UK cohorts: Avon Longitudinal Study of Parents and Children (ALSPAC) and Born in Bradford (BiB). In ALSPAC and BiB, the outcomes were birthweight (BW; N = 9339) and BMI at age 1 and 4 years (N = 8659 to 7575). In ALSPAC only we investigated BMI at 10 and 15 years (N = 4476 to 4112) and dual-energy X-ray absorptiometry (DXA) determined fat mass index (FMI) from age 10-18 years (N = 2659 to 3855). We compared MR results from several PRS, calculated from maternal non-transmitted alleles at between 29 and 80,939 single nucleotide polymorphisms (SNPs). Results MV and MR consistently showed a positive association between maternal BMI and BW, supporting a moderate causal effect. For adiposity at most older ages, although MV estimates indicated a strong positive association, MR estimates did not support a causal effect. For the PRS with few SNPs, MR estimates were statistically consistent with the null, but had wide confidence intervals so were often also statistically consistent with the MV estimates. In contrast, the largest PRS yielded MR estimates with narrower confidence intervals, providing strong evidence that the true causal effect on adolescent adiposity is smaller than the MV estimates (P-difference = 0.001 for 15-year BMI). This suggests that the MV estimates are affected by residual confounding, therefore do not provide an accurate indication of the causal effect size. Conclusions Our results suggest that higher maternal pre-/early-pregnancy BMI is not a key driver of higher adiposity in the next generation. Thus, they support interventions that target the whole population for reducing overweight and obesity, rather than a specific focus on women of reproductive age.

      Alexessander Da Silva Couto Alves, N. Maneka G. De Silva, Ville Karhunen, Ulla Sovio, Shikta Das, H. Rob Taal, Nicole M. Warrington, Alexandra M. Lewin, Marika Kaakinen, Diana L. Cousminer, Elisabeth Thiering, Nicholas J. Timpson, Tom A. Bond, Estelle Lowry, Christopher D. Brown, Xavier Estivill, Virpi Lindi, Jonathan P. Bradfield, Frank Geller, Doug Speed, Lachlan J. M. Coin, Marie Loh, Sheila J. Barton, Lawrence J. Beilin, Hans Bisgaard, Klaus Bonnelykke, Rohia Alili, Ida J. Hatoum, Katharina Schramm, Rufus Cartwright, Marie-Aline Charles, Vincenzo Salerno, Karine Clement, Annique A.J Claringbould, BIOS Consortium, Cornelia M. van Duijin, Elena Moltchanova, Johan G. Eriksson, Cathy Elks, Bjarke Feenstra, Claudia Flexeder, Stephen Franks, Timothy M. Frayling, Rachel M. Freathy, Paul Elliot, Elisabeth Widen, Hakon Hakonarson, Andrew T. Hattersley, Alina Rodriguez, Marco Banterle, Joachim Heinrich, Barbara Heude, John W. Holloway, Albert Hofman, Elina Hypponen, Hazel Inskip, Lee M. Kaplan, Asa K. Hedman, Esa Laara, Holger Prokisch, Harald Grallert, Timo A. Lakka, Debbie A. Lawlor, Mads Melbye, Tarunveer S. Ahluwalia, Marcella Marinelli, Iona Y. Millwood, Lyle J. Palmer, Craig E. Pennell, John R. Perry, Susan M. Ring, Markku J. Savolainen, Fernando Rivadeneira, Marie Standl, Jordi Sunyer, Carla M.T Tiesler, Andre G. Uitterlinden, William Schierding, Justin M. O'Sullivan, Inga Prokopenko, Karl-Heinz Herzig, George Davey Smith, Paul O'Reilly, Janine F. Felix, Jessica L. Buxton, Alexandra L. F Blakemore, Ken K. Ong, Vincent W.V Jaddoe, Struan F.A Grant, Sylvain Sebert, Mark L. McCarthy, Marjo-Riitta Jarvelin (2019)GWAS on longitudinal growth traits reveals different genetic factors influencing infant, child, and adult BMI, In: Science Advances5(9) American Association for the Advancement of Science

      Early childhood growth patterns are associated with adult health, yet the genetic factors and the developmental stages involved are not fully understood. Here, we combine genome-wide association studies with modeling of longitudinal growth traits to study the genetics of infant and child growth, followed by functional, pathway, genetic correlation, risk score, and colocalization analyses to determine how developmental timings, molecular pathways, and genetic determinants of these traits overlap with those of adult health. We found a robust overlap between the genetics of child and adult body mass index (BMI), with variants associated with adult BMI acting as early as 4 to 6 years old. However, we demonstrated a completely distinct genetic makeup for peak BMI during infancy, influenced by variation at the LEPR/LEPROT locus. These findings suggest that different genetic factors control infant and child BMI. In light of the obesity epidemic, these findings are important to inform the timing and targets of prevention strategies.

      Additional publications