Research Group Principal Investigator

    Markus Perola, Research Professor, Docent in Quantitative Genetics, Ph.D, M.D.

    Tel. +358 40 861 2557
    E-mail: markus.perola [at] helsinki.fi

    National Institute for Health and Welfare (THL)
    P.O. Box 30 (Mannerheimintie 166)
    FI-00271 Helsinki
    Finland
    Tel. +358 2952 48727 
    E-mail: markus.perola [at] thl.fi

Diabetes and Obesity Research Program contact information

Postal Address
Research Programs Unit
Diabetes and Obesity
Research Program
P.O.Box 63, Haartmaninkatu 8
FI-00014 University of Helsinki
Finland

Visiting Address
Biomedicum 1
Haartmaninkatu 8
00290 Helsinki

Tel. +358 2941 911 (tel. exchange)
Fax + 358 2941 26382
Email name.lastname [at] helsinki.fi

Quantitative Genetics Group - Research

Stock image - NumbersOur deepened knowledge of genomic variation and simultaneous technological development have brought forth a revolution for multifactorial trait genetics, such as obesity-related traits. Characteristic for this era has been very large meta-analyses analyzing tens of thousands of individuals with genome-wide association (GWA) data. This has produced numerous new insights into complex trait genetics (GWAS publications). With recent advances in technology the genomics field has added whole genome and exome sequencing to its tool package.

GIANT-consortium

In the obesity field, the GIANT-consortium was established for this purpose (GIANT: Genomewide Investigation of ANThropometric measures) as an international assembly of investigators and genetic epidemiologists. The motivation for GIANT was to pool GWA results on anthropometric traits. The current version of GIANT ended up with an enormous number of individuals with genomic data, more than 240.000 for BMI alone. In two of the largest metastudies,the GIANT Consortium discovered eighteen new regions of the human genome contributing to obesity and thirteen new regions influencing waist to hip ratio (WHR). Our group has been and still is keenly involved in the GIANT consortium, both in contributing data and planning and performing analyses.

The on-going analyses in GIANT utilizing the “cardiometabochip” which includes more than 200.000 SNPs associated with anthropometric and metabolic traits has found 11 novel loci, and a total of 75-90 loci expected (p<5*108) for BMI and 15 for WHR. The DILGOM cohort has been genotyped with the cardiometabochip and is one of the cohorts in the GIANT consortium. The studies bring together data on BMI, WHR, and detailed genotypic information, from more than a quarter of a million participants globally (mostly still European-origin populations living in Europe, North America and Australia). Perhaps the most noteworthy aspect of these findings is that it has indeed been possible to find so many loci for WHR that are independent of BMI. Most of the BMI loci appear to affect central and neuronal processes regulating satiety and appetite. By contrast, the WHR loci appear to be involved in the development and distribution of adipose tissue. Also intriguingly, many of the WHR loci show a significantly greater impact in women than in men. The distinctions drawn here between BMI and WHR are steps towards better understanding of the roles of these two traits as risk factors for a range of diseases.

The Cardio-metabochip has been genotyped on 4700 DILGOM participants and the data are currently under analyses. The best signals of the GIANT consortium are included in Cardio-metabochip SNP set, which has 200000+ SNPs for deep replication of signals arising from GWA meta-analyses. In addition, most of the other signals included on the chip are relevant for DILGOM study, e.g., those being associated with lipids, glucose metabolism, blood pressure and type 2 diabetes. A subsample of the DILGOM subjects participating in the Helsinki region ("the systems biology cohort") was sampled for peripheral blood leukocyte RNA by PAXgene Blood RNA Tubes. In all, 518 unrelated individuals have undergone genome-wide expression profiling (Illumina HumanHT-12 Expression) and have been phenotyped by a high-throughput serum nuclear magnetic resonance metabonomics platform with an optimized protocol providing absolute information on ~140 metabolites. Their telomerase lengths have also been determined. The sub-cohort has been genotyped with Illumina 610 GWAS chip, as well as with the Cardio-metabochip and Immunochip. Currently we are sequencing the exomes of this subcohort in the Sanger Institute and determining the mass-spectometry metabolomics in collaboration with the Helmholz Center in Munich (professor Thomas Illig as the collaborator). This data will be available to us during the winter of 2011-12.

Study opens new avenues of research

We have several avenues of moving forward planned to study the ‘omics’ of obesity and related traits. Utilizing the longitudinal data from DILGOM we have the opportunity to analyze body composition associated SNPs for their effects on long-term weight change, abdominal obesity and related traits. We hypothesize that  genetic variants associated with obesity and anthropometric traits in large meta-analyses, and new variants detected in ongoing sequencing efforts, predict weight gain and/or increasing abdominal obesity during the follow-up in this prospective population-based setting.  Additionally, we will utilize systems biology approach in the subsample of the cohort for better functional understanding of the signals. Repeated analyses of transcriptomic and metabolomic profiles five years apart can, together with genomic profiles, reveal novel metabolic pathways leading to weight gain and abdominal obesity. Also epigenetic factors behind this cascade via detection of changes in methylation pattern across the genome and in individual candidate genes will be studied since DNA methylation patterns might predict weight gain and development of obesity. Finally, next-generation sequencing will give us an unprecedented view of rare variation and its relationship to Finnish body composition.

Eventually we aim to form a risk score for development of obesity from above signals (first tested in an independent cohort). This genetic risk score helps to identify people with increased susceptibility to obesity. However, the novel findings of new genetic loci and undiscovered metabolic pathways predisposing to obesity and its complications will provide biological insight into these traits which will probably raise up several new hypotheses and ways to go. Continuing participation in consortia will give us first-hand look of the upcoming findings of others in the field.