Background
Background
Major Depressive Disorder (MDD)
MDD is a highly prevalent disorder that afflicts 20% of the population in their lifetimes, and ranks high in morbidity among all human diseases. It is more prevalent in women than men (roughly 2:1 ratio), and presents differently in different people (in terms of symptoms, onset, recurrence and comorbidity). Its biological nature remains unknown, despite advances in modern medicine, and its diagnoses and treatments remain empirical. On the one hand, misdiagnosis and overprescription of antidepressants are both commonplace; on the other, 50% of individuals with the disorder never make it into a clinic to get diagnosed, and 50% of diagnosed cases do not respond to their first prescribed medication. It is needless to say that we need to do better here.
The state of understanding of MDD now is rather like that of cancer 100 years ago. Back then, without an understanding of the genome's structure and its pathogenic nature (i.e. mutations leading to disregulated cellular proliferation), cancer was a mystery too. In particular, no one knew that leukemias and solid tumours were, in fact, of a similar nature. Fast forward 100 years, we now know something of nature of cancer, though it remains a difficult problem to solve. MDD, and psychiatric disorders in general, however, have not reached this state.
What is the nature of MDD? Is it some specific neurons not firing right in a massive way, or all neurons not firing right in a sutble way, or has it nothing to do with neurons firing? How would we ever know? These are the questions our research tries to answer, using quantitative genetics as a tool.
Recommended reading:
The genetics of major depression. Flint J, Kendler KS. Neuron (2014). doi: 10.1016/j.neuron.2014.02.033
The nature of psychiatric disorders. Kendler KS. World Psychiatry (2016). doi: 10.1002/wps.20292
What kind of things are psychiatric disorders? Kendler KS, Zachar P, Craver C. Psychol Med (2011). doi: 10.1017/S0033291710001844
Quantitative Genetics
It is hoped that an agnostic approach like genome-wide association study (GWAS) would be helpful when we are this clueless. GWAS tests for the association between a phenotype of interest (e.g. a trait like height, or a disease MDD or diabetes) and genotypes at millions of genetic variants across the genome. It's based on the premise that genetic variants would affect gene functions (through increasing or decreasing the amount of gene products, or affecting their effectiveness at performing their functions), and genetic variants that affect genes whose functions are compromised in diseases would be revealed through association studies.
Simply put, the significant findings of a GWAS should be informative of the genetic variants, and by extension their regulated genes, that are important to a disease. In 2015, Na and colleagues in the CONVERGE Consortium performed the first GWAS that identified genome-wide significant findings for MDD, using data collected by the CONVERGE Consortium (6000 severe recurrent cases of MDD obtained from detailed clinican interviews, 6000 screened controls).
Associations, however, are just the starting points of our investigations. The "truth" of the nature of MDD is likely orders of magnitude more complicated, so our work is focused on figuring out these complicated bits by deriving statistical approaches that are based on but go beyond association studies.
Cai N.*, Bigdeli T.B.*, Kretzschmar W.W.*, Li Y.*, et al, Sparse whole-genome sequencing identifies two loci for major depressive disorder, Nature 2015
Recommended reading:
Genetic mapping of human disease. Altshuler D, Daly MJ, Lander ES. Science (2008). doi: 10.1126/science.1156409
GCTA: A tool for genome-wide complex trait analysis. Yang J, Lee SH, Goddard ME, Visscher PM. AJHG (2010). doi: 10.1016/j.ajhg.2010.11.011
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Bulik-Sullivan BK., et al. Nat Genet (2015). doi: 10.1038/ng.3211
Reevaluation of SNP heritability in complex human traits. Speed D., et al. Nat Genet (2017). doi: 10.1038/ng.3865