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Arrivare ad una diagnosi sulla base dei dati genetici di ciascun individuo sarà un po' come costruirsi le proprie liste personalizzate di film o analizzare e trovare degli argomenti in mezzo a un mare magnum di testi: l'algoritmo utilizzato è infatti lo stesso. Lo hanno sperimentato i ricercatori guidati da David Blei, della Columbia University, il cui studio è in via di pubblicazione sulla rivista Nature Genetics.
L'algoritmo si chiama TeraStructure ed è la versione 'moderna' dell'algoritmo Structure, elaborato nel 2000. Riesce a scansionare la enorme mole di dati genetici e risalire anche alle caratteristiche più antiche, quelle originarie degli antenati, punto chiave per identificare le mutazioni genetiche di cui si è portatori.
Sui dati simulati di 10'000 individui, TeraStructure è riuscito a stimare la struttura di una popolazione in modo più accurato e due-tre volte più veloce degli attuali algoritmi. Da solo si è mostrato capace di analizzare un milione di individui, un ordine di grandezza nettamente superiore a quello consentito dai moderni software. Potenzialmente potrebbe anche tracciare la struttura di tutta la popolazione umana su scala mondiale. Per identificare le varianti genetiche causa di una malattia, i ricercatori devono infatti sapere qualcosa sulle origini della persona per controllare le normali varianti genetiche all'interno di un sottogruppo.
ATS/Swing
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