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A spectral theory for Wright's inbreeding coefficients and related quantities.

ABSTRACT: Wright's inbreeding coefficient, FST, is a fundamental measure in population genetics. Assuming a predefined population subdivision, this statistic is classically used to evaluate population structure at a given genomic locus. With large numbers of loci, unsupervised approaches such as principal component analysis (PCA) have, however, become prominent in recent analyses of population structure. In this study, we describe the relationships between Wright's inbreeding coefficients and PCA for a model of K discrete populations. Our theory provides an equivalent definition of FST based on the decomposition of the genotype matrix into between and within-population matrices. The average value of Wright's FST over all loci included in the genotype matrix can be obtained from the PCA of the between-population matrix. Assuming that a separation condition is fulfilled and for reasonably large data sets, this value of FST approximates the proportion of genetic variation explained by the first (K - 1) principal components accurately. The new definition of FST is useful for computing inbreeding coefficients from surrogate genotypes, for example, obtained after correction of experimental artifacts or after removing adaptive genetic variation associated with environmental variables. The relationships between inbreeding coefficients and the spectrum of the genotype matrix not only allow interpretations of PCA results in terms of population genetic concepts but extend those concepts to population genetic analyses accounting for temporal, geographical and environmental contexts.

SUBMITTER: Francois O

PROVIDER: S-EPMC8320931 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe calculation of demographic measures is a useful tool for evaluating the genomic architecture of dog breeds and enables ranking dog breeds in terms of genetic diversity. To achieve this for the German Dalmatian dog population, 307 purebred animals of this breed were genotyped on the Illumina Canine high density BeadChip. The analysis of pedigree-based inbreeding was performed based on a pedigree with 25,761 dogs including the genotyped dogs.ResultsThe effective population size derived from squared correlation coefficients between SNP alleles (r 2) was 69. The maximum value of r 2 was 0.56, resulting in a 50% decay value of 0.28 at a marker distance of 37.5 kb. The effective population size calculated from pedigree data using individual increase in inbreeding over equivalent generations was 116. The pedigree inbreeding coefficient was 0.026. The genomic inbreeding coefficient based on the length of runs of homozygosity (ROH) was calculated for seven length categories of ROHs, and ranged from 0.08 to 0.28. The fixation coefficients FIS_PED and FIS_GENO were at 0.017 and 0.004. PANTHER statistical overrepresentation analysis of genes located in consensus ROHs revealed highly underrepresented biological processes in 50% of the investigated dogs. One of those is the 0.28 fold enriched "immune response", which might be associated to the high prevalence of allergic dermatitis in the breed. Candidate genes for congenital sensorineural deafness (CCSD, a highly prevalent disease in the Dalmatian) were discovered in consensus ROHs.ConclusionsThe fast decay of r 2 and the moderate inbreeding coefficients indicate that the German Dalmatian dog population is rather diverse. Pedigree- and genomic-based inbreeding measures were highly correlated and therefore prove good reliability for the given population. Analyses of consensus ROHs with genes coding for deafness and other breed-defining traits, such as hyperuricosuria, indicate that those ROH became fixed in the Dalmatian population about 500 years ago. In case of the Dalmatian dog, a ROH of 40 SNPs length is enough to investigate signatures of selection (e.g. the ROH with the fixed hyperuricosuria mutation) as far back as the breed formation point approximately 500 years ago.

Project description:There has been a growing interest in computational electroencephalogram (EEG) signal processing in a diverse set of domains, such as cortical excitability analysis, event-related synchronization, or desynchronization analysis. In recent years, several inconsistencies were found across different EEG studies, which authors often attributed to methodological differences. However, the assessment of such discrepancies is deeply underexplored. It is currently unknown if methodological differences can fully explain emerging differences and the nature of these differences. This study aims to contrast widely used methodological approaches in EEG processing and compare their effects on the outcome variables. To this end, two publicly available datasets were collected, each having unique traits so as to validate the results in two different EEG territories. The first dataset included signals with event-related potentials (visual stimulation) from 45 subjects. The second dataset included resting state EEG signals from 16 subjects. Five EEG processing steps, involved in the computation of power and phase quantities of EEG frequency bands, were explored in this study: artifact removal choices (with and without artifact removal), EEG signal transformation choices (raw EEG channels, Hjorth transformed channels, and averaged channels across primary motor cortex), filtering algorithms (Butterworth filter and Blackman-Harris window), EEG time window choices (-750 ms to 0 ms and -250 ms to 0 ms), and power spectral density (PSD) estimation algorithms (Welch's method, Fast Fourier Transform, and Burg's method). Powers and phases estimated by carrying out variations of these five methods were analyzed statistically for all subjects. The results indicated that the choices in EEG transformation and time-window can strongly affect the PSD quantities in a variety of ways. Additionally, EEG transformation and filter choices can influence phase quantities significantly. These results raise the need for a consistent and standard EEG processing pipeline for computational EEG studies. Consistency of signal processing methods cannot only help produce comparable results and reproducible research, but also pave the way for federated machine learning methods, e.g., where model parameters rather than data are shared.

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A spectral theory for Wright's inbreeding coefficients and related quantities.

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