Population and Statistical genetics at the Bioinformatic Center at UCPH

We are meta group of labs that work with various parts of population, medical and statistical genetics at the Biocenter at University of Copenhagen. The meta group consists of 5 labs that works with focus on different organisms and systems. We apply and develop methods for analyzing large scale next generation sequencing data.

Hans Siegismund

Associate Professor

We work on population genetics, phylogeography and speciation processes of large African mammals, mainly bovids and great apes. Another research area includes the study of evolutionary genetics of foot-and-mouth-disease (FMD) virus in East Africa.

my website

Ida Moltke

Associate professor

We develop and apply statistical methods to genomic data with the purpose of gaining insights into human disease, history and evolution. For instance, by studying DNA from the Greenlandic population we recently identified a genetic variant that explains 10-15% of all cases of type 2 diabetes in Greenland. We have also looked into the migration history of the Artic and are currently investigating how the Greenlanders have genetically adapted the Arctic cold and their very fat-rich diet consisting mainly of seal and fish.

my website

Rasmus Heller

Associate Professor

I am interested in applying population and evolutionary genetics to answer questions about animal biology, particularly in large mammals. Most of my work has revolved around large African mammals. My research tries to address a range of topics in these species, including the historical drivers of population dynamics, how variation emerges and is retained, speciation, adaptive evolution and the relationship between phenotypic and genomic variation. I am also interested in topics of a more immediate interest in species conservation such as landscape genetics, the effect of habitat fragmentation, population connectivity etc

my website

Anders Albrechtsen

Professor

Our group develops statistical and computational methods for analysis of genomic data including methods for performing multi-loci association studies, methods for detecting and correcting for population stratification, methods for detecting natural selection, loci dependent methods for modeling identity-by-descent and various methods for analysis of second generation sequencing data.

my website

Software

APOH

Estimating admixture pedigrees of recent hybrids without a contiguous reference genome

How to infer the ancestry of an admixed individuals pedigree.

Genís Garcia-Erill🖂, Kristian Hanghøj, Rasmus Heller, Carsten Wiuf, Anders Albrechtsen

Mol Ecol Resour 23(7) 2023

software website

evalPCA

Evaluation of population structure inferred by principal component analysis or the admixture model.

When do distances in a PCA reflex genetic ancestry? if two individuals cluster togeather does that mean they are some the same populations? Here we present a method that you can use to interpret you genetic PCA plot.

Jan van Waaij* 🖂, Song Li* , Genís Garcia-Erill* , Anders Albrechtsen, Carsten Wiuf🖂;

Genetics: 2023, 225(2);

software website

PCAone

Fast and accurate out-of-core PCA framework for large scale biobank data.

A method for accurate PCA for large scale genetic data. Analyse millions of sites for all 500000 individuals in UKbiobank on your labtop

Zilong Li🖂, Jonas Meisner, Anders Albrechtsen🖂

Genome Res: 2023, 33(9);1599-1608

software website

HaploNet

Haplotype and population structure inference using neural networks in whole-genome sequencing data.

Using sequencing data from simulations and closely related human populations, we show that our approach is better at distinguishing closely related populations than standard admixture and principal component analysis software. We further show that HaploNet is fast and highly scalable by applying it to genotype array data of the UK Biobank. U

Jonas Meisner🖂, Anders Albrechtsen

Genome Res: 2022, 32(8);1542-52

software website

SATC

Joint identification of sex and sex-linked scaffolds in non-model organisms using low depth sequencing data

Framework for joint determination of individual sex and sex-linked scaffolds for non-model organism based on depth of coverage

Nursyifa C.* ; Brüniche-Olsen A.* ; Garcia-Erill G.; Heller R.🖂; Albrechtsen A.🖂

Mol. Eco. res(2022)

software website

winSFS

Estimation of site frequency spectra from low-coverage sequencing data using stochastic EM reduces overfitting, runtime, and memory usage

Inference of the site frequency spectrum (SFS) from low-depth sequencing data.

Rasmussen M.S.; Garcia-Erill G.; Korneliussen T.S.; Wiuf C.; Albrechtsen A.

Genetics(2022)

software website

EMU

Large-scale inference of population structure in presence of missingness using PCA

PCA with missingness including having samples with non overlapping data

Meisner J.; Liu S.; Huang M.; Albrechtsen A.

Bioinformatics(2021)

software website

NGSremix

NGSremix: A software tool for estimating pairwise relatedness between admixed individuals from next-generation sequencing data

Estimating relatedness coefficients for admixture samples with low depth sequencing. Also works for F1 and other recently admixed indivudals.

Nøhr A.K.; Hanghøj K.; Garcia-Erill G.; Li Z.; Moltke I.; Albrechtsen A.

G3(2021)

software website

evalAdmix

Evaluation of model fit of inferred admixture proportions

Evaluaation your estimated admixture proportions.

Garcia-Erill G.; Albrechtsen A.

Mol. Eco. res(2020)

software website

ASAmap

Ancestry-specific association mapping in admixed populations

Genetic association of ancestry specific effects when you do not have information about local ancestry.

Skotte L.; Jørsboe E.; Korneliussen T.S.; Moltke I.; Albrechtsen A.

Genetic Epidemiology(2019)

software website

IBSrelate

Allele frequency-free inference of close familial relationships from genotypes or low-depth sequencing data

KING, RO, R1, statistics for relatedness based only two individuals. No reference panel or allele frequencies needed

Waples R.K.; Albrechtsen A.; Moltke I.

Mol. Eco.(2019)

software website

PCAngsd

Inferring population structure and admixture proportions in low-depth NGS data

PCA, admixture proportions, HWE or selection scans from low depth sequencings data while accomidating population structure

Meisner J.; Albrechtsen A.

Genetics(2018)

software website

fastNGSadmix

FastNGSadmix: Admixture proportions and principal component analysis of a single NGS sample

none

Jørsboe E.; Hanghøj K.; Albrechtsen A.

Bioinformatics(2017)

software website

ANGSD

ANGSD: Analysis of Next Generation Sequencing Data

Software for population genetic and medical genetic analysis of low depth sequencing data.

Korneliussen T.S.; Albrechtsen A.; Nielsen R.

BMC Bioinformatics(2014)

software website

NGSadmix

Estimating individual admixture proportions from next generation sequencing data

none

Skotte L.; Korneliussen T.S.; Albrechtsen A.

Genetics(2013)

software website

relate

Relatedness mapping and tracts of relatedness for genome-wide data in the presence of linkage disequilibrium

none

Albrechtsen A.; Korneliussen T.S.; Moltke I.; van Overseem Hansen T.; Nielsen F.C.; Nielsen R.

Genetic Epidemiology(2009)

software website