Inheritance of traits in F1 hybrids of diploid einkorn wheat of the spring crop

Diploid einkorn wheat (2n = 14) is an ancient crop that people cultivate for 10 thousand years. The grain of this wheat is a valuable product for a healthy diet, which determines the increasing interest in einkorn wheat by scientists and agricultural producers. Meanwhile, the wide use of this crop is hindered by several shortcomings that complicate the usage of modern technologies: low yield, ear fragility, a tendency to lodging, and difficult grain threshing. Nevertheless, there are some preconditions for improving the agronomic properties of this crop. We carried out crosses in seven combinations with the use of three wheat species (T. boeoticum, T. monococcum, T. sinskajae) to improve the diploid einkorn wheat in terms of productivity and threshing. In total, the hybrid seed set in the crosses varies from 6.3 % to 79.7 %. In the combination of cultivated wheat T. sinskajae with wild T. boeoticum, differences in the results of reciprocal crosses are observed specifically in the hybrid seed set (in the forward cross it equals 6.3 %; in the reverse one, 48.9 %). Hybrids from reciprocal crosses of T. monococcum var. sofianum UA0300649 and T. sinskajae f. aristata were equivalent in seed set (72 and 82 %) and inheritance patterns and had similar quantitative traits. In other combinations, the seed set varied from 12.5 to 45.6 %. Hybrid depression was the most frequent (22 cases out of 49) inheritance type of the F1 quantitative traits in einkorns; dominance of the parent form with a large trait manifestation was registered in 11 cases, and heterosis in four cases. In hybrids, the inheritance of spike length is correlated with the inheritance type of the ear number (r = 0.92) and the grain number (r = 0.78) per spike. The dominance degrees after these two traits are also highly correlated (r = 0.89). The combination UA0300400 T. boeoticum var. thaoudar ARM / UA0300224 T. sinskajae var. sinskajae RUS, which manifested heterosis for kernel number per spike (Hp = 1.2), the weight of spike (Hp = 11.8) and weight of kernels per spike (Hp = 5.4) is of particular interest. The combination UA0300222 T. monococcum var. hohensteinii / UA0300224 T. sinskajae var. sinskajae is promising for creating easily threshed material

Molecular genetic data in terms of associative and population genetics

In studies on associative genetics of various multifactorial diseases, it is most often found that the minor allele’s frequency in the group of patients is higher than in the group of healthy people. Due to reduced adaptation, the minor allele manifests itself as a disease. In the group of patients, the number of homozygotes by major allele is reduced, the number of heterozygous carriers of the provocative allele is increased, and the frequency of homozygotes by the provocative allele is significantly increased. The aim of this article was to analyze the unusual result for SNP 1298A/C of the MTHFR gene in multiple sclerosis, previously obtained by one of the authors. The allele frequencies in the control group and in the group of multiple sclerosis do not differ, but the distribution of genotypes in the patients does not correspond to the Hardy–Weinberg ratio in compare to healthy people. Among patients, the number of heterozygotes is increased and the number of each homozygote is decreased. The increase in the proportion of heterozygotes can be explained by the presence of one provocative allele, but the large shortage of homozygotes for the minor allele is unclear. To explain this fact, the composition of the group of patients was analysed. The patients are of different ages, this group is heterogeneous in sex and form of multiple sclerosis, but none of these indicators  has not be taken into account in the analysis of the distribution of genotypes. The age of the disease is a diagnostic sign and may depend on the genotype. The manifestation of multifactorial diseases depends on gender as well. Clinical forms of the disease usually have a different genetic basis. Due to the neglect of these conditions, a genetically heterogeneous group is formed, and any result, difficult for explanation, can be obtained. The lack of СС genotypes may be due to increased mortality, which reduces the probability of patients to be investigated in the sample. These facts once again indicate the need to form homogeneous groups for research on associative genetics

The genetic history of admixture across inner Eurasia

This is the author accepted manuscript. The final version is available from Nature Research via the DOI in this record.Data Availability. Genome-wide sequence data of two Botai individuals (BAM format) are available at the European Nucleotide Archive under the accession number PRJEB31152 (ERP113669). Eigenstrat format array genotype data of 763 present-day individuals and 1240K pulldown genotype data of two ancient Botai individuals are available at the Edmond data repository of the Max Planck Society (https://edmond.mpdl.mpg.de/imeji/collection/Aoh9c69DscnxSNjm?q=).The indigenous populations of inner Eurasia, a huge geographic region covering the central Eurasian steppe and the northern Eurasian taiga and tundra, harbor tremendous diversity in their genes, cultures and languages. In this study, we report novel genome-wide data for 763 individuals from Armenia, Georgia, Kazakhstan, Moldova, Mongolia, Russia, Tajikistan, Ukraine, and Uzbekistan. We furthermore report additional damage-reduced genome-wide data of two previously published individuals from the Eneolithic Botai culture in Kazakhstan (~5,400 BP). We find that present-day inner Eurasian populations are structured into three distinct admixture clines stretching between various western and eastern Eurasian ancestries, mirroring geography. The Botai and more recent ancient genomes from Siberia show a decrease in contribution from so-called “ancient North Eurasian” ancestry over time, detectable only in the northern-most “forest-tundra” cline. The intermediate “steppe-forest” cline descends from the Late Bronze Age steppe ancestries, while the “southern steppe” cline further to the South shows a strong West/South Asian influence. Ancient genomes suggest a northward spread of the southern steppe cline in Central Asia during the first millennium BC. Finally, the genetic structure of Caucasus populations highlights a role of the Caucasus Mountains as a barrier to gene flow and suggests a post-Neolithic gene flow into North Caucasus populations from the steppe.Max Planck SocietyEuropean Research Council (ERC)Russian Foundation for Basic Research (RFBR)Russian Scientific FundNational Science FoundationU.S. National Institutes of HealthAllen Discovery CenterUniversity of OstravaCzech Ministry of EducationXiamen UniversityFundamental Research Funds for the Central UniversitiesMES R

Population distribution and ancestry of the cancer protective MDM2 SNP285 (rs117039649)

The MDM2 promoter SNP285C is located on the SNP309G allele. While SNP309G enhances Sp1 transcription factor binding and MDM2 transcription, SNP285C antagonizes Sp1 binding and reduces the risk of breast-, ovary- and endometrial cancer. Assessing SNP285 and 309 genotypes across 25 different ethnic populations (>10.000 individuals), the incidence of SNP285C was 6-8% across European populations except for Finns (1.2%) and Saami (0.3%). The incidence decreased towards the Middle-East and Eastern Russia, and SNP285C was absent among Han Chinese, Mongolians and African Americans. Interhaplotype variation analyses estimated SNP285C to have originated about 14,700 years ago (95% CI: 8,300 - 33,300). Both this estimate and the geographical distribution suggest SNP285C to have arisen after the separation between Caucasians and modern day East Asians (17,000 - 40,000 years ago). We observed a strong inverse correlation (r = -0.805; p < 0.001) between the percentage of SNP309G alleles harboring SNP285C and the MAF for SNP309G itself across different populations suggesting selection and environmental adaptation with respect to MDM2 expression in recent human evolution. In conclusion, we found SNP285C to be a pan-Caucasian variant. Ethnic variation regarding distribution of SNP285C needs to be taken into account when assessing the impact of MDM2 SNPs on cancer risk

Genomic analyses inform on migration events during the peopling of Eurasia.

High-coverage whole-genome sequence studies have so far focused on a limited number of geographically restricted populations, or been targeted at specific diseases, such as cancer. Nevertheless, the availability of high-resolution genomic data has led to the development of new methodologies for inferring population history and refuelled the debate on the mutation rate in humans. Here we present the Estonian Biocentre Human Genome Diversity Panel (EGDP), a dataset of 483 high-coverage human genomes from 148 populations worldwide, including 379 new genomes from 125 populations, which we group into diversity and selection sets. We analyse this dataset to refine estimates of continent-wide patterns of heterozygosity, long- and short-distance gene flow, archaic admixture, and changes in effective population size through time as well as for signals of positive or balancing selection. We find a genetic signature in present-day Papuans that suggests that at least 2% of their genome originates from an early and largely extinct expansion of anatomically modern humans (AMHs) out of Africa. Together with evidence from the western Asian fossil record, and admixture between AMHs and Neanderthals predating the main Eurasian expansion, our results contribute to the mounting evidence for the presence of AMHs out of Africa earlier than 75,000 years ago.Support was provided by: Estonian Research Infrastructure Roadmap grant no 3.2.0304.11-0312; Australian Research Council Discovery grants (DP110102635 and DP140101405) (D.M.L., M.W. and E.W.); Danish National Research Foundation; the Lundbeck Foundation and KU2016 (E.W.); ERC Starting Investigator grant (FP7 - 261213) (T.K.); Estonian Research Council grant PUT766 (G.C. and M.K.); EU European Regional Development Fund through the Centre of Excellence in Genomics to Estonian Biocentre (R.V.; M.Me. and A.Me.), and Centre of Excellence for Genomics and Translational Medicine Project No. 2014-2020.4.01.15-0012 to EGC of UT (A.Me.) and EBC (M.Me.); Estonian Institutional Research grant IUT24-1 (L.S., M.J., A.K., B.Y., K.T., C.B.M., Le.S., H.Sa., S.L., D.M.B., E.M., R.V., G.H., M.K., G.C., T.K. and M.Me.) and IUT20-60 (A.Me.); French Ministry of Foreign and European Affairs and French ANR grant number ANR-14-CE31-0013-01 (F.-X.R.); Gates Cambridge Trust Funding (E.J.); ICG SB RAS (No. VI.58.1.1) (D.V.L.); Leverhulme Programme grant no. RP2011-R-045 (A.B.M., P.G. and M.G.T.); Ministry of Education and Science of Russia; Project 6.656.2014/K (S.A.F.); NEFREX grant funded by the European Union (People Marie Curie Actions; International Research Staff Exchange Scheme; call FP7-PEOPLE-2012-IRSES-number 318979) (M.Me., G.H. and M.K.); NIH grants 5DP1ES022577 05, 1R01DK104339-01, and 1R01GM113657-01 (S.Tis.); Russian Foundation for Basic Research (grant N 14-06-00180a) (M.G.); Russian Foundation for Basic Research; grant 16-04-00890 (O.B. and E.B); Russian Science Foundation grant 14-14-00827 (O.B.); The Russian Foundation for Basic Research (14-04-00725-a), The Russian Humanitarian Scientific Foundation (13-11-02014) and the Program of the Basic Research of the RAS Presidium “Biological diversity” (E.K.K.); Wellcome Trust and Royal Society grant WT104125AIA & the Bristol Advanced Computing Research Centre (http://www.bris.ac.uk/acrc/) (D.J.L.); Wellcome Trust grant 098051 (Q.A.; C.T.-S. and Y.X.); Wellcome Trust Senior Research Fellowship grant 100719/Z/12/Z (M.G.T.); Young Explorers Grant from the National Geographic Society (8900-11) (C.A.E.); ERC Consolidator Grant 647787 ‘LocalAdaptatio’ (A.Ma.); Program of the RAS Presidium “Basic research for the development of the Russian Arctic” (B.M.); Russian Foundation for Basic Research grant 16-06-00303 (E.B.); a Rutherford Fellowship (RDF-10-MAU-001) from the Royal Society of New Zealand (M.P.C.)

Population genetic characteristics of the population of Ukraine obtained with the use of surnames

The article presents the results of the first study in Ukraine of the population structure at different hierarchical levels inferred using surnames as quasigenetic markers. The surnames frequencies, random isonymy index, random component of inbreeding, migration index, rate of names diversity, index of entropy and index of redundancy of surnames diversity were calculated in total for the population of Ukraine, as well as for major geographic regions: Eastern, Southern, Central, Northern, Western and also for all the 25 administrative regions of the country. Our results show that Ukrainian surnames are a reliable tool for the population genetic studies, as they have a rather high differentiating abilit

Y-chromosome STR variation in Ukrainian populations

The haplotype and allele frequencies for 17 STR loci of Y-chromosome were obtained for 1151 indigenous Ukrainians from 13 regional populations representing the major territorial subdivisions of Ukraine. There were no significant inter-population differences. The genetic subdivisions within Ukraine was revealed between Polesie, western and eastern forest-steppe populations. The highest microsatellite variability was observed along the edges of Ukrainian area – in the Carpathian region, Bukovina, Sloboda Ukraine; the lowest – in Polesie. The average haplotype diversity values are higher in the steppe and forest-steppe zones, than in Polesie and the Carpathians. Forensic parameters were calculated: total haplotype diversity HD = 0,998855, match probability MP = 0.00114508, the discrimination capacity DC = 0,89400521

The gene pool of Belgorod oblast population : Study of biochemical gene markers in populations of Ukraine and Belarus and the position of the Belgorod population in the Eastern Slavic gene pool system

The characteristics of the gene pools of indigenous populations of Ukraine and Belarus have been studied using 28 alleles of 10 loci of biochemical gene markers (HP, GC, TF, PI, C'3, ACP1, GLO1, PGM1, ESD, and 6-PGD). The gene pools of the Russian and Ukrainian indigenous populations of Belgorod oblast (Russia) and the indigenous populations of Ukraine and Belarus have been comparedyesBS

The gene pool of Belgorod oblast population : Study of biochemical gene markers in populations of Ukraine and Belarus and the position of the Belgorod population in the Eastern Slavic gene pool system

yesBSUThe characteristics of the gene pools of indigenous populations of Ukraine and Belarus have been studied using 28 alleles of 10 loci of biochemical gene markers (HP, GC, TF, PI, C'3, ACP1, GLO1, PGM1, ESD, and 6-PGD). The gene pools of the Russian and Ukrainian indigenous populations of Belgorod oblast (Russia) and the indigenous populations of Ukraine and Belarus have been compare