In the last decade, one of the biggest challenges in genomics research has been to distinguish definitive pathogenic variants from all likely pathogenic variants identified by next-generation sequencing. This task is particularly complex because of our lack of knowledge regarding overall genome variation and pathogenicity of the variants. Therefore, obtaining sufficient information about genome variants in the general population is necessary as such data could be used for the interpretation of de novo mutations (DNMs) in the context of patient’s phenotype in cases of sporadic genetic disease. In this study, data from whole-exome sequencing of the general population in Lithuania were directly examined. In total, 84 (VarScan) and 95 (VarSeqTM) DNMs were identified and validated using different algorithms. Thirty-nine of these mutations were considered likely to be pathogenic based on gene function, evolutionary conservation, and mutation impact. The mutation rate estimated per position pair per generation was 2.74 × 10-8 [95% CI: 2.24 × 10-8–3.35 × 10-8] (VarScan) and 2.4 × 10-8 [95% CI: 1.96 × 10-8–2.99 × 10-8] (VarSeqTM), with 1.77 × 10-8 [95% CI: 6.03 × 10-9–5.2 × 10-8] de novo indels per position per generation. The rate of germline DNMs in the Lithuanian population and the effects of the genomic and epigenetic context on DNM formation were calculated for the first time in this study, providing a basis for further analysis of DNMs in individuals with genetic diseases. Considering these findings, additional studies in patient groups with genetic diseases with unclear etiology may facilitate our ability to distinguish certain pathogenic or adaptive DNMs from tolerated background DNMs and to reliably identify disease-causing DNMs by their properties through direct observation.

Next-generation sequencing (NGS) became an effective approach for finding novel causative genomic variants of genetic disorders and is increasingly used for diagnostic purposes. Public variant databases that gather data of pathogenic variants are being relied upon as a source for clinical diagnosis. However, research of pathogenic variants using public databases data could be carried out not only in patients, but also in healthy people. This could provide insights into the most common recessive disorders in populations. The study aim was to use NGS and data from the ClinVar database for the identification of pathogenic variants in the exomes of healthy individuals from the Lithuanian population. To achieve this, 96 exomes were sequenced. An average of 42 139 single-nucleotide variants (SNVs) and 2306 short INDELs were found in each individual exome. Pooled data of study exomes provided a total of 243 192 unique SNVs and 31 623 unique short INDELs. Three hundred and twenty-one unique SNVs were classified as pathogenic. Comparison of the European data from the 1000 Genomes Project with our data revealed five pathogenic genomic variants that are inherited in an autosomal recessive pattern and that statistically significantly differ from the European population data.

Taste has strong evolutionary basis in the sense of survival by influencing our behavior to obtain food/medicine or avoid poisoning. It is a complex trait and varies among individuals and distinct populations. We aimed to investigate the association between known genetic factors (673 SNPs) and taste preference in the Lithuanian population, as well as to determine a reasonable method for qualitative evaluation of a specific taste phenotype for further genetic analysis. Study group included individuals representing six ethnolinguistic regions of Lithuania. Case and control groups for each taste were determined according to the answers selected to the taste-specific and frequency of specific food consumption questions. Sample sizes (case/control) for each taste are as follows: sweetness (55/179), bitterness (82/208), sourness (32/259), saltiness (42/249), and umami (96/190). Genotypes were extracted from the Illumina HumanOmniExpress-12v1.1 arrays' genotyping data. Analysis was performed using PLINK v1.9. We found associations between the main known genetic factors and four taste preferences in the Lithuanian population: sweetness-genes TAS1R3, TAS1R2, and GNAT3 (three SNPs); bitterness-genes CA6 and TAS2R38 (six SNPs); sournessgenes PKD2L1, ACCN2, PKD1L3, and ACCN1 (48 SNPs); and saltiness-genes SCNN1B and TRPV1 (five SNPs). We found our questionnaire as a beneficial aid for qualitative evaluation of taste preference. This was the first initiative to analyze genetic factors related to taste preference in the Lithuanian population. Besides, this study reproduces, supports, and complements results of previous limited taste genetic studies or ones that lack comprehensive results concerning distinct (ethnic) human populations.

Background Preaxial polydactyly type IV, also referred as polysyndactyly, has been described in a few syndromes. We present three generations of a family with preaxial polydactyly type IV and other clinical features of Greig cephalopolysyndactyly syndrome (GCPS). Methods and results Sequencing analysis of the GLI3 coding region identified a novel donor splice site variant NC_000007.14(NM_000168.6):c.473+3A>T in the proband and the same pathogenic variant was subsequently identified in other affected family members. Functional analysis based on Sanger sequencing of the proband's complementary DNA (cDNA) sample revealed that the splice site variant c.473+3A>T disrupts the original donor splice site, thus leading to exon 4 skipping. Based on further in silico analysis, this pathogenic splice site variant consequently results in a truncated protein NP_000159.3:p.(His123Argfs*57), which lacks almost all functionally important domains. Therefore, functional cDNA analysis confirmed that the haploinsufficiency of the GLI3 is the cause of GCPS in the affected family members. Conclusion Despite the evidence provided, pathogenic variants in the GLI3 do not always definitely correlate with syndromic or nonsyndromic clinical phenotypes associated with this gene. For this reason, further transcriptomic and proteomic evaluation could be suggested.

Background. In the scientific literature, a wide range of effect variants that protect against complex disease phenotypes has been identified. Analysis of these variants and overall genetic structure of isolated or, in our case, small populations is important in association analysis. When analysing admixture populations during GWAS, one could expect inaccuracies, which could be eliminated by choosing distinct populations as one of the interests of study. Population genetic structure determines similarities and differences between individuals or different groups of individuals and the factors that may lead to those differences. Results. In our study, we identified six missense effect variants in the Lithuanian population having frequencies that were significantly different compared to other European populations. Three of these effect variants may potentially protect against type 2 diabetes and coronary heart disease.Conclusions. Even though high rates of these diseases in the Lithuanian population and other populations indicates the presence of environmental factors and the lack of knowledge about the interactions between regulatory regions and other effect variants. Identification of these effect variants is important not only to provide a better understanding of the microevolutionary processes and etiopathogenetic mechanisms, but also to develop disease prevention programs and novel, personalised therapies using genome editing or other genetic tools.