Carl Nettelblad

Dimensionality Reduction of Genetic Data using Contrastive Learning

This paper introduces a contrastive learning framework for dimensionality reduction of genetic data, showing improved preservation of global structure and generalization over existing methods.

Using feedback in pooled experiments augmented with imputation for high genotyping accuracy at reduced cost

The paper proposes an iterative feedback method combining pooled genotyping and imputation to improve SNP genotype accuracy at reduced cost.

Achieving improved accuracy for imputation of ancient DNA

This paper introduces an imputation method for ancient DNA that improves accuracy at lower coverages compared to existing tools.

An empirical evaluation of genotype imputation of ancient DNA

This study evaluates genotype imputation for ancient DNA using Beagle 4.0 and 1000 Genomes reference data, showing high accuracy even at low coverage and highlighting the benefits of diverse reference sets and low-coverage samples.

A deep learning framework for characterization of genotype data

The paper proposes a deep learning model for dimensionality reduction of genotype data, showing improved population clustering and visualization compared to PCA, t-SNE, and UMAP.

Adjustment for SNPs instead of demographic and clinical covariates in GWAS

This study evaluates imputation methods for ancient DNA data using Beagle 4.0 and 1000 Genomes reference data, showing high accuracy at low coverage and benefits of phased references and low-coverage samples.

The presence and impact of reference bias on population genomic studies of prehistoric human populations

This paper shows reference bias affects ancient DNA studies and suggests filtering strategies to reduce its impact on population genomic analyses.