3D genomic maps. Better than Google maps for understanding the functional role of GWAS hits

Genome wide association scan (GWAS) is a popular method among genetic epidemiologists for identifying genetic variants associated to a trait of interest. GWAS power relies on analyzing the statistical association between a phenotype of interest (i.e. “autism” versus “non-autism” individuals) in a panel of genetic markers (usually single nucleotide polymorphisms (SNPs)) that densely covers the genome. The description of millions of genetic variants in the human genome, advances in genotyping technologies, the generation of large databases of individuals for the traits of interest and the invaluable international cooperation among different groups of investigators identified during the last decade thousands of genetic markers associated to hundreds of phenotypic traits (https://www.ebi.ac.uk/gwas/). However, understanding the biological meaning of these hits is a challenge for the scientific community, and translating such knowledge to patient treatment is at its infancy for most of the analyzed traits.

For scientists, one of the many intriguing issues of a GWAS outcome is that most of the reported genetic variants tend to be non-coding, falling far away from any described gene or any other known functional element of the genome. This empirical observation is theoretically explained by the fact that statistical hits do not necessarily represent a causal variant, but more likely are “spatially close” to the (not genotyped) causal genetic variant. Here, “spatially close” is interpreted at one dimension. As a consequence, genetic epidemiologists tend to report as putative functional candidates all the genes that are at an ad hoc genomic distance from a hit; further in-deep analyses for identifying the causal gene and the causal genetic variant are performed in silico by applying powerful bioinformatics tools that combine the expertise from different fields of knowledge. Nevertheless, even after performing all these analyses, in most of the cases the conclusion of the study is just a general overview of the physiology of the phenotype summarized in a sentence like “among all the genes close to the hits we observe gene enrichment for a pathway X” and a list of associated SNPs of unknown function. This (quite frustrating) view is rapidly changing with the incorporation of new technological advances that do not consider the one-dimensional nature of DNA, which do not capture many regulatory interactions, but the tridimensional conformation of DNA within the cellular nucleus.

In a recent paper published in Nature (http://www.nature.com/nature/journal/v538/n7626/full/nature19847.html), Won et al described the tridimensional map of chromosome conformation during human corticogenesis. The role of human brain development in regulating gene networks has been claimed as one of the factors explaining neurodevelopmental disorders, such as autism or schizophrenia. After describing the topology of the 3D map of chromatin conformation during human corticogenesis, the authors conducted several analyses for understanding the evolutionary impact of this 3D conformation. More interestingly for understanding the etiology of schizophrenia, Won et al analyzed how this topological conformation relates to previously reported 108 SNPs associated to that disease. Similar to other GWAS studies, nearly all of these loci reside in relatively uncharacterized non-coding regions of the genome. The authors show that most of these SNPs, which were previously described to be associated to the disease only from a statistical point of view, are close in the 3D space to genes with a likely role in postsynaptic density, acetylcholine receptors, neuronal differentiation, and chromatin remodellers. Amazingly, most of these genes are not in linkage disequilibrium with the GWAS hits, but proximity –and likely interaction- is only at tridimensional level.

Overall, this study shows that attacking the “functional hit problem” requires a multidimensional perspective from a multidisciplinary and genomic point of view. Exciting times are coming for understanding the etiology of complex phenotypes and -we hope- for developing new treatment strategies.

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s