When a young man was brought to my clinic after his first episode of psychosis, his parents wanted to know when he could return to college.
The central development
The engine of much of this progress is the genome-wide association study (GWAS). Instead of choosing a few candidate genes to study, researchers use GWAS to compare millions of common genetic variants across very large groups of people with and without a condition. The point is to find out whether some variants appear more often in one particular group. Both these GWAS studies showed where researchers could look for biological mechanisms underlying these conditions — and possible treatment options. Many ‘signals’ lie in stretches of DNA that regulate when and where genes are switched ‘on’ rather than directly encoding a protein. Several genes may sit near the same signal. The relevant effect may occur during a narrow period of brain development. A GWAS is more like a satellite map that highlights areas of interest; to drill down beyond that, scientists have to use other tools. GWAS studies have also exposed a mismatch between traditional diagnostics and biology. This does not mean diagnoses become meaningless. A diagnosis is still useful to guide treatment and communicate the prognosis to the patients and their families.
In 2022, a landmark schizophrenia study involving tens of thousands of people identified associations at 287 genomic regions and pointed to genes active in neurons and synapses. A similarly large bipolar disorder study in 2021 identified 64 associated regions. For example, in a December 2025 study in Nature , researchers reported that some inherited risk is shared across schizophrenia and bipolar disorder.
That said, a genomic region is not the same as a gene, and association is not causation. At the same time, the finding suggests that nature has not organised mental illness according to the chapter headings of the Diagnostic and Statistical Manual of Mental Disorders (DSM).
What Happens Next
Some groups have still tried to compress all these small effects into a combined polygenic risk score — a single number they intend to use to estimate a person’s inherited susceptibility. they cannot say whether a person will become ill, at what age, how severe the condition will be or which medicine will work While such scores can be useful in specific areas of research. A person with a higher polygenic risk score may also remain well while a person with a lower score may develop illness. Indian psychiatrists have one more reason to be cautious. India cannot simply import a score developed elsewhere, test it in a small urban sample, and assume it applies to all its peoples. All this is to say that psychiatric genomics has its value when exercised properly: by involving diverse populations in research and clinicians and communities when deciding how data will be used, and protecting privacy and refusing to allow the enthusiasm of commerce to outrun science. Psychiatric genetics will probably become clinically more useful as datasets become larger and more representative, and as scientists better combine genetic findings with developmental, clinical, and environmental information. It may even help divide broad syndromes into biologically meaningful subgroups or identify new drug targets. However, predicting what will happen in future will also remain probabilistic; that will not change. And just as well, we must work to keep probabilities from being misunderstood, stigmatised or commercialised.
Because the score captures only a part of genetic liability, and it does not ‘contain’ childhood adversity, sleep disruption, substance use, medical illness, social support, access to timely care, and so on, this is. Because the frequencies and correlations of genetic variants differ by ancestry, scores developed from those datasets are often less accurate in other populations. The GenomeIndia project , which generated whole-genome data from 10,000 healthy, unrelated Indians across 83 population groups, documented an extraordinary genetic diversity.
Even “Indian ancestry” is too blunt a label.
Genomic databases have historically drawn disproportionately from people of European ancestry .
