Phenotype prediction accuracy – A Swedish perspective

• Methods for SNP-based phenotype prediction have recently been developed, but prediction accuracy data for several populations and regions are missing. We analysed the accuracy of hair and eye colour predictions for 111 individuals residing in Sweden, using the ForenSeq system and the MiSeq FGx instrument (Verogen). Observed colours were compared to predicted colours, using the colour with the highest probability value for each prediction. Overall, 80% of eye colour predictions were correct, but the system failed to predict intermediate/green eye colour in our cohort. For hair colour, 58% of predictions were correct, and the majority of incorrect predictions were related to brown hair. To assess if prediction accuracy could be improved by the exclusion of predictions with low probabilities, we applied a threshold of ≥0.7. The threshold improved eye colour prediction, from 80% to 85% correct predictions, whereas hair colour prediction accuracy was virtually unaffected (58% versus 57% correct predictions). In summary, the phenotype prediction accuracy was acceptable in our cohort and the use of a threshold was only useful for eye colour predictions.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Bioinformatics (hsv//eng)

NATURVETENSKAP  -- Kemi -- Analytisk kemi (hsv//swe)

NATURAL SCIENCES  -- Chemical Sciences -- Analytical Chemistry (hsv//eng)

Nyckelord

EVC

Massively parallel sequencing

Phenotype

SNP

EVC; Massively parallel sequencing; Phenotype; SNP

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