Browsing by Subject "linkage disequilibrium"

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  • Partanen, Reeta-Maria (Helsingin yliopisto, 2020)
    There is a naturally reproducing Atlantic salmon population in the River Teno in northern Norway and Finland. The Teno population has a strong population structure and up to 28 subpopulations have been recognized. Estimation of effective population size is important in conservation of the subpopulations. Effective population size tells about genetic variation of a population and is among the most important concepts in conservation genetics. In this study, current and past effective population sizes of 28 subpopulations were estimated from high density SNP-data for 1137 individuals in total. The estimation was done with the linkage disequilibrium method and the effects of using different assumptions were studied. Current estimated effective population sizes in subpopulations were generally low and ranged from around nine to 272 individuals. Only four populations had a current effective population size bigger than 50 individuals. Past effective population sizes showed a clear declining trend from the most distant generations in all populations. The choice between physical and linkage map as well as female, male or average linkage map had an effect to estimates. Also, different sample size corrections resulted in different estimates. Furthermore, effective population size was estimated with temporal method in three populations. It was detected that the estimates from temporal and linkage disequilibrium method were different from each other. The results of this study suggest that Teno Atlantic salmon subpopulations have declined over the past 150 generations and are in risk of losing genetic variation due to current low effective population size. This should be taken into account in conservation plans.
  • Paakala, Elina (Helsingfors universitet, 2011)
    The aim of this master’s thesis was to study genetic diversity within and between ten dog breeds. The original data was produced by Finnzymes Oy and it contained raw data files that were used to genotype microsatellite markers. The final data contained information from 395 dogs belonging to ten fairly different dog breeds. The amount of dogs per breed was from 31 to 53. The data was genotyped with 18 microsatellite markers. A llelic richness varied between 2,0 – 9,9. The most variable microsatellite locus was AHT137 and the least variable AHTk211. Over all loci allelic richness was highest in Jack Russell Terrier and lowest in Cavalier King Charles Spaniel. S c hipperke had the largest amount of microsatellite loci that were not in Hardy- Weinberg equilibrium. In Coton de Tulear, German Shepherd Dog and Finnish Lapphund all microsatellite loci were in Hardy-Weinberg equilibrium. The lowest overall heterozygosity was in Cavalier King Charles Spaniel (0,50) and highest in Finnish Lapphund (0,73). The only statistically significant FIS-values were in Schipperke in locus INU030 (0,39) and over all loci (0,11). The most distant breeds according to FST-value were Cavalier King Charles Spaniel and Rough Collie (0,34) and the closest breeds were Chihuahua and Coton de Tulear (0,07). Overall between breeds diversity was 17,7 %. On the grounds of these results the ten breeds are distinct populations. Coton de Tulear had clearly the highest amount of allele pairs in linkage disequilibrium (94) and Tibetan Spaniel the lowest amount (15). Effective population size was lowest in Rough Collie (35) and highest in Chihuahua (86). Based on many population genetic measures Cavalier King Charles Spaniel, Rough Collie and Schipperke seem to have the lowest genetic diversity and Chihuahua, Jack Russel Terrier and Finnish Lapphound the highest. Reasons for different levels of genetic diversity can be found on histories of these dog breeds.
  • Puranen, Santeri; Pesonen, Maiju; Pensar, Johan; Xu, Ying Ying; Lees, John A.; Bentley, Stephen D.; Croucher, Nicholas J.; Corander, Jukka (2018)
    The potential for genome-wide modelling of epistasis has recently surfaced given the possibility of sequencing densely sampled populations and the emerging families of statistical interaction models. Direct coupling analysis (DCA) has previously been shown to yield valuable predictions for single protein structures, and has recently been extended to genome-wide analysis of bacteria, identifying novel interactions in the co-evolution between resistance, virulence and core genome elements. However, earlier computational DCA methods have not been scalable to enable model fitting simultaneously to 10(4)-10(5) polymorphisms, representing the amount of core genomic variation observed in analyses of many bacterial species. Here, we introduce a novel inference method (SuperDCA) that employs a new scoring principle, efficient parallelization, optimization and filtering on phylogenetic information to achieve scalability for up to 10(5) polymorphisms. Using two large population samples of Streptococcus pneumoniae, we demonstrate the ability of SuperDCA to make additional significant biological findings about this major human pathogen. We also show that our method can uncover signals of selection that are not detectable by genome-wide association analysis, even though our analysis does not require phenotypic measurements. SuperDCA, thus, holds considerable potential in building understanding about numerous organisms at a systems biological level.