Biology subjects

Rosengren, A.

Publications and source records attributed to Rosengren, A..

4 recordsLinked to original sources

bioRxiv2024

Modelling genetic risk of β-cell dysfunction in human induced pluripotent stem cells from patients carrying the MTNR1B risk variant.

Disruptions in circadian rhythm, partly controlled by the hormone melatonin, increase the risk of type 2 diabetes (T2D). Accordingly, a variant of the gene encoding the melatonin receptor 1B (MTNR1B) is robustly associated with increased risk of T2D. This single nucleotide polymorphism (SNP; rs10830963; G-allele) is an expression quantitative trait locus (eQTL) in human pancreatic islets, conferring increased expression of MTNR1B, which is thought to perturb pancreatic {beta}-cell function. To understand this pathogenic mechanism in detail, we utilized human induced pluripotent stem cells (hiPSC), derived from individuals with T2D carrying the MTNR1B G-allele. Patient-derived fibroblasts were reprogrammed to hiPSC and single-base genome editing by CRISPR/Cas9 was employed to create isogenic lines of either the C/C or G/G genotypes (non-risk and risk, respectively). In addition, the human embryonic stem cell (hESC) line (HUES4) was subjected to genome editing to create isogenic lines of either the C/C or G/G genotypes. hiPSC and hESC were differentiated into {beta}-cells, using a 50-day 2D protocol. Single-base genome editing generated cells with the desired genotype at a success rate of >90%. Expression of stage-specific markers confirmed differentiation of both hiPSC and hESC into {beta}-cells. MTNR1B mRNA levels were consistently low in differentiated {beta}-cells, precluding quantitative analysis of gene expression. However, Western blot analysis showed higher levels of MTNR1B in differentiated {beta}-cells carrying the risk allele, consistent with rs10830963 (G-allele) being an eQTL in {beta}-cells. Insulin secretion in response to glucose and IBMX was similar between the genotypes, whereas addition of melatonin reduced secretion in G-allele carriers. We conclude that the stem cell-derived {beta}-cells are not sufficiently mature to allow determination of eQTL status at the mRNA level. However, we did observe increased MTNR1B protein and increased sensitivity of {beta}-cells from risk allele carriers (G-allele) to melatonin with regard to insulin secretion, thus supporting a functional role for the rs10830963 SNP in {beta}-cell dysfunction.

cell biology↗

bioRxiv2024

Steppe Ancestry in western Eurasia and the spread of the Germanic Languages

Today, Germanic languages, including German, English, Frisian, Dutch and the Nordic languages, are widely spoken in northwest Europe. However, key aspects of the assumed arrival and diversification of this linguistic group remain contentious1-3. By adding 712 new ancient human genomes we find an archaeologically elusive population entering Sweden from the Baltic region by around 4000 BP. This population became widespread throughout Scandinavia by 3500 BP, matching the contemporaneous distribution of Palaeo-Germanic, the Bronze Age predecessor of Proto-Germanic4-6. These Baltic immigrants thus offer a new potential vector for the first Germanic speakers to arrive in Scandinavia, some 800 years later than traditionally assumed7-12. Following the disintegration of Proto-Germanic13-16, we find by 1650 BP a southward push from Southern Scandinavia into presumed Celtic-speaking areas, including Germany, Poland and the Netherlands. During the Migration Period (1575-1375 BP), we see this ancestry representing West Germanic Anglo-Saxons in Britain, and Langobards in southern Europe. We find a related large-scale northward migration into Denmark and South Sweden corresponding with historically attested Danes and the expansion of Old Norse. These movements have direct implications for multiple linguistic hypotheses. Our findings show the power of combining genomics with historical linguistics and archaeology in creating a unified, integrated model for the emergence, spread and diversification of a linguistic group.

genetics↗

bioRxiv2022

The Selection Landscape and Genetic Legacy of Ancient Eurasians

The Holocene (beginning [~]12,000 years ago) encompassed some of the most significant changes in human evolution, with far-reaching consequences for the dietary, physical, and mental health of present-day populations. Using a dataset of >1600 imputed ancient genomes 1, we modelled the selection landscape during the transition from hunting and gathering, to farming and pastoralism across West Eurasia. We identify major selection signals related to metabolism, including that selection at the FADS cluster began earlier than previously reported, and that selection near the LCT locus predates the emergence of the lactase persistence allele by thousands of years. We also find strong selection in the HLA region, possibly due to increased exposure to pathogens during the Bronze Age. Using ancient individuals to infer local ancestry tracts in >400,000 samples from the UK Biobank, we identify widespread differences in the distribution of Mesolithic, Neolithic, and Bronze Age ancestries across Eurasia. By calculating ancestry-specific polygenic risk scores, we show that height differences between Northern and Southern Europe are associated with differential Steppe ancestry, rather than selection, and that risk alleles for mood-related phenotypes are enriched for Neolithic farmer ancestry, while risk alleles for diabetes and Alzheimers disease are enriched for Western Hunter-gatherer ancestry. Our results suggest that ancient selection and migration were major contributors to the distribution of phenotypic diversity in present-day Europeans.

evolutionary biology↗

bioRxiv2022

Accuracy of haplotype estimation and whole genome imputation affects complex trait analyses in complex biobanks

Sample recruitment for research consortia, hospitals, biobanks, and personal genomics companies span years, necessitating genotyping in batches, using different technologies. As marker content on genotyping arrays varies systematically, integrating such datasets is non-trivial and its impact on haplotype estimation (phasing) and whole genome imputation, necessary steps for complex trait analysis, remains under-evaluated. Using the iPSYCH consortium dataset, comprising 130,438 individuals, genotyped in two stages, on different arrays, we evaluated phasing and imputation performance across multiple phasing methods and data integration protocols. While phasing accuracy varied both by choice of method and data integration protocol, imputation accuracy varied mostly between data integration protocols. We demonstrate an attenuation in imputation accuracy within samples of non-European origin, highlighting challenges to studying complex traits in diverse populations. Finally, imputation errors can modestly bias association tests and reduce predictive utility of polygenic scores. This is the largest, most comprehensive comparison of data integration approaches in the context of a large psychiatric biobank.

bioinformatics↗