University roles and responsibilities
- Head of Bioinformatics Core Facility
- Genome wide association studies (GWAS)
- Expression quantitative trail loci studies (eQTL, GxE-eQTL)
- Gene expression association studies
- Statistical modeling of health data
- Regression and graphical models (stan)
- Variable selection, and Model averaging
- Applied machine learning to health and biological data
- DNA-Seq: Genotype calling, and quality control
- RNA-Seq: Alignment and quantification
- Genotype SNP arrays: Genotype calling and quality control
Software in the areas of my research specialism can be found at https://github.com/acoutoal
In the media
His research purpose is to extend the healthspan of human populations. He focuses on developing bioinformatics methods and strategies to identify the genetic variants and molecular mechanisms controlling the lifecourse trajectory of health and disease phenotypes. The goal is to identify the genes networks and developmental time windows involved in disease susceptibility, and in doing so to guide intervention strategies improving population health. He has extensive experience coordinating, designing, and running genome wide association studies, gene expression association studies, and mapping expression quantitative trait loci. He has developed software and visualization tools widely used in genomics and molecular epidemiological studies for multi-omics data integration, DNA-Seq variant calling, RNA-Seq quantification, gene expression analysis, metabolomics protocol optimization and biomarker discovery. He has developed predictive models of molecular and clinical markers applied to disease prognosis and diagnosis. He is a reviewer of the journal of the royal statistical society, and a member of the american society of human genetics.
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
Keynote and plenary addresses at conferences
2014 Systems biology and functional analysis of disease genes. European academy of allergy and clinical immunology congress.
- Journal of the Royal Statistical Society
- Genome Medicine
- Nature scientific reports
- Annals of Human Genetics
- Bioscience reports
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.
Completed postgraduate research projects I have supervised
2014, Ricardo Pinho. MSc Thesis: Machine learning methodologies for gene-gene interactions discovery in complex disease, University of Porto
2013, Nikman Nor Ashim. MSc Thesis: Identification of copy number variants associated with fasting plasma glucose, Imperial College London
2006, Nuno Castro. MEng Thesis (Lic.): Prediction of rare events in sequence data, Minho University
2005, Hugo Penedones. MEng Thesis (Lic.): Anomaly detection in time series, University of Porto
I teach human genetics, genomics and bioinformatics in the following modules:
- MOLECULAR BIOLOGY AND GENETICS
- SYSTEMS BIOLOGY
- MICROBIAL GENETICS AND MOLECULAR BIOLOGY
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.
genetic factors influencing infant, child, and adult BMI, Science Advances 5 (9) American Association for the Advancement of Science
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.
You may find an updated list of publications at my google scholar profile