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A joint analysis of dendrochronological and genomic data was performed to identify genetic mechanisms of adaptation and assess the adaptive genetic potential of Siberian stone pine (Pinus sibirica Du Tour) populations. The data obtained are necessary for predicting the effect of climate change and mitigating its negative consequences. Presented are the results of an association analysis of the variation of 84,853 genetic markers (single nucleotide polymorphisms—SNPs) obtained by double digest restriction-site associated DNA sequencing (ddRADseq) and 110 individual phenotypic traits, including dendrophenotypes based on the dynamics of tree-ring widths (TRWs) of 234 individual trees in six natural populations of Siberian stone pine, which have a history of extreme climatic stresses (e.g., droughts) and outbreaks of defoliators (e.g., pine sawfly [Neodiprion sertifer Geoff.]). The genetic structure of studied populations was relatively weak; samples are poorly differentiated and belong to genetically similar populations. Genotype–dendrophenotype associations were analyzed using three different approaches and corresponding models: General Linear Model (GLM), Bayesian Sparse Linear Mixed Model (BSLMM), and Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK), respectively. Thirty SNPs were detected by at least two different approaches, and two SNPs by all three. In addition, three SNPs associated with mean values of recovery dendrophenotype (Rc) averaged across multiple years of climatic stresses were also found by all three methods. The sequences containing these SNPs were annotated using genome annotation of a very closely related species, whitebark pine (P. albicaulis Engelm.). We found that most of the SNPs with supposedly adaptive variation were located in intergenic regions. Three dendrophenotype-associated SNPs were located within the 10 Kbp regions and one in the intron of the genes encoding proteins that play a crucial role in ensuring the integrity of the plant’s genetic information, particularly under environmental stress conditions that can induce DNA damage. In addition, we found a correlation of individual heterozygosity with some dendrophenotypes. Heterosis was observed in most of these statistically significant cases; signs of homeostasis were also detected. Although most of the identified SNPs were not assigned to a particular gene, their high polymorphism and association with adaptive traits likely indicate high adaptive potential that can facilitate adaptation of Siberian stone pine populations to the climatic stresses and climate change. Full article

Thyroglobulin (Tg) is an iodoglycoprotein produced by thyroid follicular cells which acts as an essential substrate for thyroid hormone synthesis. To date, only one genome-wide association study (GWAS) of plasma Tg levels has been performed by our research group. Utilizing recent advancements in computation and modeling, we apply a Bayesian approach to the probabilistic inference of the genetic architecture of Tg. We fitted a Bayesian sparse linear mixed model (BSLMM) and a frequentist linear mixed model (LMM) of 7,289,083 variants in 1096 healthy European-ancestry participants of the Croatian Biobank. Meta-analysis with two independent cohorts (total n = 2109) identified 83 genome-wide significant single nucleotide polymorphisms (SNPs) within the ST6GAL1 gene ($p<5\times {10}^{-8}$). BSLMM revealed additional association signals on chromosomes 1, 8, 10, and 14. For ST6GAL1 and the newly uncovered genes, we provide physiological and pathophysiological explanations of how their expression could be associated with variations in plasma Tg levels. We found that the SNP-heritability of Tg is 17% and that 52% of this variation is due to a small number of 16 variants that have a major effect on Tg levels. Our results suggest that the genetic architecture of plasma Tg is not polygenic, but influenced by a few genes with major effects. Full article