Analytical “Bake-Off” of Whole Genome Sequencing Quality for the Genome Russia Project Using a Small Cohort for Autoimmune Hepatitis

A comparative analysis of whole genome sequencing (WGS) and genotype calling was initiated for ten human genome samples sequenced by St. Petersburg State University Peterhof Sequencing Center and by three commercial sequencing centers outside of Russia. The sequence quality, efficiency of DNA variant and genotype calling were compared with each other and with DNA microarrays for each of ten study subjects. We assessed calling of SNPs, indels, copy number variation, and the speed of WGS throughput promised. Twenty separate QC analyses showed high similarities among the sequence quality and called genotypes. The ten genomes tested by the centers included eight American patients afflicted with autoimmune hepatitis (AIH), plus one case’s unaffected parents, in a prelude to discovering genetic influences in this rare disease of unknown etiology. The detailed internal replication and parallel analyses allowed the observation of two of eight AIH cases carrying a rare allele genotype for a previously described AIH-associated gene (FTCD), plus multiple occurrences of known HLA-DRB1 alleles associated with AIH (HLA-DRB1-03:01:01, 13:01:01 and 7:01:01). We also list putative SNVs in other genes as suggestive in AIH influence

Genome-wide sequence analyses of ethnic populations across Russia

The Russian Federation is the largest and one of the most ethnically diverse countries in the world, however no centralized reference database of genetic variation exists to date. Such data are crucial for medical genetics and essential for studying population history. The Genome Russia Project aims at filling this gap by performing whole genome sequencing and analysis of peoples of the Russian Federation. Here we report the characterization of genome-wide variation of 264 healthy adults, including 60 newly sequenced samples. People of Russia carry known and novel genetic variants of adaptive, clinical and functional consequence that in many cases show allele frequency divergence from neighboring populations. Population genetics analyses revealed six phylogeographic partitions among indigenous ethnicities corresponding to their geographic locales. This study presents a characterization of population-specific genomic variation in Russia with results important for medical genetics and for understanding the dynamic population history of the world's largest country

Statistical analysis of spatial distribution in populations of microspecies of Alchemilla L

In this paper, we consider Alchemilla vulgaris L. (or common lady's mantle), which is an herbaceous perennial plant. It is known that within this species it is possible to distinguish microspecies, that is, fairly homogeneous groups having minor morphological differences. We study spatial distributions of the microspecies found in various localities as well as possible interaction between different microspecies.4 page(s

Analysis of correlation structure in bilateral traits

When studying asymmetry of bilateral traits, it is very important to take into account their correlation structure. The assumption for quadrivariate normal distribution of traits considers possible models of equality of four correlation coefficients between traits using different criteria and approaches on the basis of two large lamina samples (N₁=500, N₂=521) of drooping birch (Betula pendula Roth.). This study has shown that different traits give evidence of different models of correlation structure.17 page(s

The Analysis of ontogenetic spectrum of heterogeneous population

The distribution of discrete ontogenetic states of individuals is usually spatially and temporally different within a population. If a sample from the population sample consists of several subsamples, the comparison of their ontogenetic spectra reveals heterogeneity of samples, i.e. different subsamples cannot be described by the same polynomial distribution. Therefore, the comparison of the samples using the aggregate data is not correct and tends to result in false inferences of biological importance. The paper proposes three methods for comparison of ontogenetic spectra of heterogeneous samples: a randomized variant of ANOVA, principal components analysis and ordinal regression analysis. The following approaches are exemplified in natural populations of cowberry Vaccinium vitis-idaea L. and epiphytic lichens Hypogymnia physodes (L.) Nyl. and Pseudevernia furfuracea (L.) Zopf.19 page(s

Analytical “bake-off” of whole genome sequencing quality for the Genome Russia project using a small cohort for autoimmune hepatitis

<div><p>A comparative analysis of whole genome sequencing (WGS) and genotype calling was initiated for ten human genome samples sequenced by St. Petersburg State University Peterhof Sequencing Center and by three commercial sequencing centers outside of Russia. The sequence quality, efficiency of DNA variant and genotype calling were compared with each other and with DNA microarrays for each of ten study subjects. We assessed calling of SNPs, indels, copy number variation, and the speed of WGS throughput promised. Twenty separate QC analyses showed high similarities among the sequence quality and called genotypes. The ten genomes tested by the centers included eight American patients afflicted with autoimmune hepatitis (AIH), plus one case’s unaffected parents, in a prelude to discovering genetic influences in this rare disease of unknown etiology. The detailed internal replication and parallel analyses allowed the observation of two of eight AIH cases carrying a rare allele genotype for a previously described AIH-associated gene (<i>FTCD</i>), plus multiple occurrences of known <i>HLA-DRB1</i> alleles associated with AIH <i>(HLA-DRB1-03</i>:<i>01</i>:<i>01</i>, <i>13</i>:<i>01</i>:<i>01 and 7</i>:<i>01</i>:<i>01</i>). We also list putative SNVs in other genes as suggestive in AIH influence.</p></div

Sample description.

<p>Sample description.</p

Genotype comparison.

<p>(A) Concordance of WGS genotypes with microarray genotypes. The concordance was estimated based on the trio data as the ratio of microarray SNPs with identical genotypes in WGS results. (B) Comparison of the three WGS datasets between each other in terms of precision, sensitivity and F-measure for pairwise comparisons. Color legend is given on the top right. (C) Concordance of genotypes in the three WGS datasets for all variants, SNPs and indels. Color legend is given on the top right.</p

<i>HLA-DRB1</i> and <i>FTCD</i> genotypes.

<p><i>HLA-DRB1</i> and <i>FTCD</i> genotypes.</p