Alexandr Rakitko

🧬Machine learning prediction of hypertension integrating polygenic risk scores in 175,704 inner Eurasian individuals from Genotek biobank🧡

Today our paper in Frontiers in Cardiovascular Medicine was published. We analyzed three PRS for blood pressure traits. The strongest effect was observed for the diastolic blood pressure PRS, with an OR of 6.7 for the top decile compared with the bottom decile

We also showed significant associations across different ethnic groups, including Russians, Mordvins, Ashkenazi Jews, Buryats, Yakuts, Armenians and populations of the North Caucasus.

The best-performing model was a Dense Neural Network with ROC-AUC = 0.82. The model integrated questionnaire-based risk factors, including age, sex, BMI, smoking, alcohol consumption, education, work type, physical activity and daily steps, together with three polygenic risk scores.

Overall, we showed that a combined model based on questionnaire data and three PRS achieves good predictive performance and transfers across different ethnic groups of inner Eurasia.

This work grew out of the master’s thesis of our student Vera Tsvetkova.

frontiersin.org/journals/cardi…

#PRS #Hypertension #MachineLearning #AncestryComposition

Can someone find your genome using your electronic health record? 🔍 🧬

A new study in @NatureComms explores genome re-identification using polygenic scores.

Imagine someone has your medical records with your height, weight, BMI and blood test results. They also have a database of hundreds of thousands of anonymous genomes. Can they find yours?

The idea is simple. Calculate polygenic scores for each genome and compare predicted traits with real ones. The authors developed PGMatch, an algorithm that accounts for prediction accuracy and correlations between traits.

They tested it on UK Biobank data using 40 traits.
With equal numbers of matching and non-matching pairs, the algorithm achieved over 99% precision at 40% recall.

But with just one real match per 1,000 incorrect pairs, average precision dropped to 13.4%.

Interestingly, earlier research showed that GWAS participation could be detected with 98% sensitivity at a false positive rate of 0.1% using 50,000 SNPs in a study of 2,000 people. However, this required knowing the person's genotype.

The conclusion is that even with access to your medical records and a huge database of anonymous genomes, finding yours would be extremely difficult.

So for now, no one is likely to identify your genome and use it to poison you with whole milk after discovering that you are lactose intolerant.

nature.com/articles/s4146…

#PRS #GWAS #anonymization #UKBiobank

Svante Pääbo and colleagues used AlphaGenome as a time machine to reconstruct the evolution of human gene regulation. An impressive new bioRxiv preprint! 🧬⏩

Fifty years ago, scientists proposed that the differences between humans and chimpanzees might be driven less by changes in genes themselves and more by changes in how those genes are regulated.

The authors revisited this hypothesis using deep learning and identified more than 71,000 genomic regions predicted to have gained regulatory activity along the human lineage. But the most interesting finding is how these changes happened!

In 71% of cases, evolution repurposed existing regulatory elements rather than creating new ones. A single mutation could introduce a tissue-specific transcription factor binding site, activating an existing element in another tissue.

To investigate this, the researchers used AlphaGenome to predict the regulatory effects of 17 million nucleotide substitutions, linking them to gene expression differences between humans and other great apes.

Interestingly, the vast majority of these regions fell outside known Human Accelerated Regions, genomic regions that evolved unusually rapidly along the human lineage. This suggests that the rate of sequence evolution does not necessarily reflect its functional impact.

Comparisons with Neanderthal and Denisovan genomes revealed that 99.6% of the regulatory gains fixed in modern humans and successfully dated arose before our evolutionary split from these archaic lineages.

There are also fascinating examples involving individual genes. One is SLC47A1, which encodes a transporter involved in eliminating drugs and toxins. The researchers identified five regulatory gains associated with increased expression in the human liver. Reverting these changes to their ancestral states in silico reduced predicted gene expression by 60%.

Of course, many of these predictions still require experimental validation. But the broader idea is remarkable: ancient DNA has allowed us to discover which mutations occurred in the past. Now, deep learning is beginning to reveal how those mutations may have changed the way genes worked.

biorxiv.org/content/10.648…

#aDNA #evolution #regulation #AlphaGenome

When our ancestors left Africa and spread across the world, they carried not only their genes but also thousands of microorganisms living in their gut. 🧬🦠

A new @Nature study explored whether the evolutionary history of gut bacteria follows human migrations.

Researchers compared the gut microbiomes of the Hadza hunter-gatherers from Tanzania and the Tsimane farmers and hunter-gatherers from Bolivia.

Surprisingly, 87% of microbial species were shared between these populations. Many of these bacteria are now extremely rare in industrialized societies, suggesting that we may be losing part of our ancient microbial heritage. Whether this affects our health remains unknown.

But the most fascinating result came from comparing bacterial genomes. Despite sharing the same species, their bacterial strains were genetically distinct.

Using bacterial recombination patterns and population genetic modeling, researchers estimated that these bacterial populations became isolated around 17,000 years ago. Remarkably, this roughly matches the period when humans first settled the Americas.

A beautiful example of how ancient human migrations left traces not only in our DNA but also in the genomes of bacteria living inside us.

nature.com/articles/s4158…

#microbiome #gut #evolution

East Asians often develop type 2 diabetes at a substantially lower BMI than Europeans. Does this mean that the genetic architecture of the disease is different? 🧬

A recent @CommsBio study addressed this question using a GWAS of almost 600,000 people of East Asian ancestry and a European GWAS of comparable statistical power.

Surprisingly, the overall picture was very similar. The studies identified 196 loci in East Asians and 199 in Europeans. Fine-mapping identified 521 likely causal signals, and about 74% were shared between the two populations.

But differences emerged when the authors looked deeper into the biology. Type 2 diabetes develops mainly through two related mechanisms: insufficient insulin secretion by pancreatic β cells and insulin resistance, strongly influenced by adipose tissue, lipid metabolism and obesity.

In both populations, GWAS signals were strongly enriched in regulatory regions active in pancreatic β cells. Yet the regulatory architecture differed: HNF1B-related regions were more enriched in East Asians, while PDX1-related regions were more prominent in Europeans. Europeans also showed a stronger genetic component related to adipose tissue and lipid metabolism.

The authors then used previously developed polygenic scores representing insulin secretion and insulin resistance. Among controls, the scores were almost identical between Japanese and British participants. But among people with diabetes, both effects were stronger in Europeans: OR 1.40 vs 1.21 for insulin secretion and OR 1.35 vs 1.18 for insulin resistance.

This raises an interesting question. Do East Asians have a lower threshold for developing diabetes, requiring less polygenic burden? Or do these mechanistic PRS simply work better in Europeans?

Still, PRS performed quite well in an independent Taiwanese cohort: a model combining age, sex, genetic PCs and PRS reached AUC ~0.8.

Overall, this looks like the same polygenic system with somewhat different weights assigned to its components across populations.

nature.com/articles/s4200…

#T2D #GWAS #PRS