Associate professor Anders Albrechtsen is a researcher at the Department of Biology, University of Copenhagen. His work focuses on developing and applying methods for exploring high throughput genetic data. Both for demographic inference, finding signs of local adaptation in the genome and for identifying genetic loci that affects phenotypic traits and diseases. He is extensively involved in studies of the genetic basis of many complex diseases in Greenlandic Inuit.
How a 20,000 year long bottleneck have shaped the genetic architecture of complex traits in Greenlandic Inuit
Anders Albrechtsen1, Niels Grarup2, Ida Moltke1 ,Torben Hansen2
1 Bioinformatics center, Department of Biology, University of Copenhagen, Denmark
2Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
Based on SNP chip data of 5000 Greenlandic Inuit and sequencing data for a subset we have shown that the Inuit have undergone a severe ∼20,000-year-long bottleneck. This has led to a markedly more extreme distribution of allele frequencies than seen for any other human population tested to date, making the Inuit the perfect population for investigating the effect of a bottleneck on patterns of deleterious variation. When comparing proxies for genetic load that assume an additive effect of deleterious alleles, the Inuit show, at most, a slight increase in load compared to European, East Asian, and African populations. However, the Inuit population carries fewer deleterious variants than other human populations, and those that are present tend to be at higher frequency than in other populations leading to a large increase in recessive genetic load. Overall, our results show how recent demographic history has fundamentally changed the patterns of deleterious variants in this population. This unusual distribution of allele frequencies greatly affects the ability to detect disease causing variant. Using a combination of genome-wide association mapping and screening for novel candidate loss-of-function variants we identify a large amount of causal variants affecting many metabolic traits such as lipids, glucose levels, fatty acids and obesity. These variants are all common and have very large effect sizes. This is in sharp contrast to the variants found to affect complex diseases in large well-studied populations such as Europeans or East Asian, where the effects sizes of common variants are small. This genetic architecture is fairly unique for human populations and may be the key to developing precision medicine and personalized treatment.