Genetic Effects Research Articles

Genome-Wide Methylation Analysis Reveals a KCNK3-Prominent Causal Cascade on Hypertension.

Despite advances in understanding hypertension's genetic structure, how noncoding genetic variants influence it remains unclear. Studying their interaction with DNA methylation is crucial to deciphering this complex disease's genetic mechanisms. We investigated the genetic and epigenetic interplay in hypertension using whole-genome bisulfite sequencing. Methylation profiling in 918 males revealed allele-specific methylation and methylation quantitative trait loci. We engineered rs1275988T/C mutant mice using CRISPR/Cas9, bred them for homozygosity, and subjected them to a high-salt diet. Telemetry captured their cardiovascular metrics. Protein-DNA interactions were elucidated using DNA pull-downs, mass spectrometry, and Western blots. A wire myograph assessed vascular function, and analysis of the Kcnk3 gene methylation highlighted the mutation's role in hypertension. We discovered that DNA methylation-associated genetic effects, especially in non-CpG island and noncoding distal regulatory regions, significantly contribute to hypertension predisposition. We identified distinct methylation quantitative trait locus patterns in the hypertensive population and observed that the onset of hypertension is influenced by the transmission of genetic effects through the demethylation process. By evidence-driven prioritization and in vivo experiments, we unearthed rs1275988 in a cell type-specific enhancer as a notable hypertension causal variant, intensifying hypertension through the modulation of local DNA methylation and consequential alterations in Kcnk3 gene expression and vascular remodeling. When exposed to a high-salt diet, mice with the rs1275988C/C genotype exhibited exacerbated hypertension and significant vascular remodeling, underscored by increased aortic wall thickness. The C allele of rs1275988 was associated with elevated DNA methylation levels, driving down the expression of the Kcnk3 gene by attenuating Nr2f2 binding at the enhancer locus. Our research reveals new insights into the complex interplay between genetic variations and DNA methylation in hypertension. We underscore hypomethylation's potential in hypertension onset and identify rs1275988 as a causal variant in vascular remodeling. This work advances our understanding of hypertension's molecular mechanisms and encourages personalized health care strategies.

Genetic risk for trait aggression and alcohol use predict unique facets of alcohol-related aggression.

A propensity for aggression or alcohol use may be associated with alcohol-related aggression. Previous research has shown genetic overlap between alcohol use and aggression but has not looked at how alcohol-related aggression may be uniquely influenced by genetic risk for aggression or alcohol use. The present study examined the associations of genetic risk for trait aggression, alcohol use, and alcohol use disorder (AUD) with alcohol-related aggression using a polygenic risk score (PRS) approach. Using genome-wide association study summary statistics, PRSs were created for trait aggression, alcohol consumption, and AUD. These PRSs were used to predict the phenotype of alcohol-related aggression among drinkers in two independent samples: the University of California at San Francisco (UCSF) Family Alcoholism Study (n = 1,162) and the National Longitudinal Study of Adolescent to Adult Health (Add Health; n = 4,291). There were significant associations between the AUD PRS and lifetime alcohol-related aggression in the UCSF study sample. Additionally, the trait aggression PRS was associated with three or more experiences of hitting anyone else and getting into physical fights while under the influence of alcohol, along with a composite score of three or more experiences of alcohol-related aggression, in the UCSF study sample. No significant associations were observed in the Add Health sample. Limited sex-specific genetic effects were observed. These results provide preliminary evidence that genetic influences underlying alcohol use and aggression are uniquely associated with alcohol-related aggression and suggest that these associations may differ by type and frequency of alcohol-related aggression incidents. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Testing Quantitative and Qualitative Sex Effects in a National Swedish Twin-Sibling Study of Posttraumatic Stress Disorder.

Twin studies have demonstrated that posttraumatic stress disorder (PTSD) is moderately heritable, and the pattern of findings across studies suggests higher heritability in females compared with males. Formal testing of sex differences has yet to be done in twin studies of PTSD. The authors sought to estimate the genetic and environmental contributions to PTSD, and to formally test for sex differences, in the largest sample to date of both sexes, among twins and siblings. Using the Swedish National Registries, the authors performed structural equation modeling to decompose genetic and environmental variance for PTSD and to formally test for quantitative and qualitative sex differences in twins (16,242 pairs) and in full siblings within 2 years of age of each other (376,093 pairs), using diagnostic codes from medical registries. The best-fit model suggested that additive genetic and unique environmental effects contributed to PTSD. Evidence for a quantitative sex effect was found, such that heritability was significantly greater in females (35.4%) than males (28.6%). Evidence of a qualitative sex effect was found, such that the genetic correlation was high but less than complete (rg=0.81, 95% CI=0.73-0.89). No evidence of shared environment or special twin environment was found. This is the first demonstration of quantitative and qualitative sex effects for PTSD. The results suggest that unique environmental effects, but not the shared environment, contributed to PTSD and that genetic influences for the disorder are stronger in females compared with males. Although the heritability is highly correlated, it is not at unity between the sexes.

Genetic Diversity, Mating System, and Seed Viability Reveal a Trade-Off between Outcrossing and Inbreeding in Pinus yunnanensis var. tenuifolia, an Ecologically Important Conifer Species Growing in a Hot-Dry River Basin Habitat in Southwest China

Revealing the relationship between the mating system (i.e., the outcrossing/inbreeding degree) and the fitness of seeds in tree species under wild conditions is essential for understanding the ecological adaptability and evolutionary stability of the species. This study collected open-pollinated seeds from seven wild populations of Pinus yunnanensis var. tenuifolia that exhibited fragmentation in the Nanpan–Hongshui River basin, an ecologically fragile area in China. The seeds and sprouts (germinated seeds) from 20 families were genotyped (24 seeds and 24 sprouts per family) using twelve microsatellite loci to reveal the genetic diversity, mating status, and effect of inbreeding on the three seed quality indicators (thousand-seed weight, germination rate, and germination potential). The three seed quality indicators differed significantly between families (p < 0.001). Higher values of genetic diversity (except the observed heterozygosity) were observed in the sprout group than those in the seed group. Families from different populations showed a notable genetic differentiation (Φst = 0.12), and a large part of families from the common populations had a high degree of coancestry, which signified that the current habitat fragmentation is limiting gene flow between populations. High levels of outcrossing rates (tm) were observed in both the seed group (tm = 0.974) and the sprout group (tm = 0.978), indicating that a low proportion of seeds were self-fertilized. Although there was a slightly higher single outcrossing rate (ts = 0.888) and a lower proportion of biparental inbreeding (tm − ts = 0.077) in sprouts compared to the seeds (ts = 0.871, tm − ts = 0.091), indicating that a part of inbred seeds were purged during the germination stage, curve fitting between the outcrossing rate and seed quality indicators showed that a certain degree of biparental inbreeding (ts between 0.89 and 0.91 and tm − ts between 0.09 and 0.11) did have a positive effect on seed germination ability. This highlights that excessive inbreeding or outbreeding seems to be unfavorable to seed viability. The peculiar relationship between seed viability and the mating system in P. yunnanensis var. tenuifolia was likely an evolutionary consequence of a trade-off between the nature of mixed mating and its specific ecological niche.

Genetic bias in repeated evolution of pigment loss in cave populations of the Asellus aquaticus species complex.

Similar phenotypes can evolve repeatedly under the same evolutionary pressures. A compelling example is the evolution of pigment loss and eye loss in cave-dwelling animals. While specific genomic regions or genes associated with these phenotypes have been identified in model species, it remains uncertain whether a bias towards particular genetic mechanisms exists. An isopod crustacean, Asellus aquaticus, is an ideal model organism to investigate this phenomenon. It inhabits surface freshwaters throughout Europe but has colonized groundwater on multiple independent occasions and evolved several cave populations with distinct ecomorphology. Previous studies have demonstrated that three different cave populations utilized common genetic regions, potentially the same genes, in the evolution of pigment and eye loss. Expanding on this, we conducted analysis on two additional cave populations, distinct either phylogenetically or biogeographically from those previously examined. We generated F2 hybrids from cave × surface crosses and tested phenotype-genotype associations, as well as conducted complementation tests by crossing individuals from different cave populations. Our findings revealed that pigment loss and orange eye pigment in additional cave populations were associated with the same genomic regions as observed in the three previously tested cave populations. Moreover, the lack of complementation across all cross combinations suggests that the same gene likely drives pigment loss. These results substantiate a genetic bias in the recurrent evolution of pigment loss in this model system. Future investigations should focus on the cause behind this bias, possibly arising from allele recruitment from ancestral surface populations' genetic variation or advantageous allele effects via pleiotropy.

De novo prediction of functional effects of genetic variants from DNA sequences based on context-specific molecular information

Deciphering the functional effects of noncoding genetic variants stands as a fundamental challenge in human genetics. Traditional approaches, such as Genome-Wide Association Studies (GWAS), Transcriptome-Wide Association Studies (TWAS), and Quantitative Trait Loci (QTL) studies, are constrained by obscured the underlying molecular-level mechanisms, making it challenging to unravel the genetic basis of complex traits. The advent of Next-Generation Sequencing (NGS) technologies has enabled context-specific genome-wide measurements, encompassing gene expression, chromatin accessibility, epigenetic marks, and transcription factor binding sites, to be obtained across diverse cell types and tissues, paving the way for decoding genetic variation effects directly from DNA sequences only. The de novo predictions of functional effects are pivotal for enhancing our comprehension of transcriptional regulation and its disruptions caused by the plethora of noncoding genetic variants linked to human diseases and traits. This review provides a systematic overview of the state-of-the-art models and algorithms for genetic variant effect predictions, including traditional sequence-based models, Deep Learning models, and the cutting-edge Foundation Models. It delves into the ongoing challenges and prospective directions, presenting an in-depth perspective on contemporary developments in this domain.

Estimates of Maternal Effect and (co)Variance Components for BodyWeight at Different Ages by Animal Model in Chokla Sheep

Background: For this study, information was gathered on 6785 Chokla sheep at the Central Sheep and Wool Research Institute in Bikaner, Rajasthan, India and documented between 1974 and 2020. Methods: (Co)variance components and genetic parameters of weight at birth (BW), weaning (WW), 6, 9 and 12 months of age (6W,9W and YW, respectively) of Chokla sheep, were estimated by average algorithm restricted maximum likelihood (AIREML), fitting six different animal models with various combinations of direct and maternal effects. Result: The direct heritability estimates increased from birth to twelve months of age and values for all the body weight traits except birth weight (0.170) were moderate (0.30-0.50). The maternal influence diminished as age increases and maternal genetic effect (m2) was found to be important and sizeable at weaning stage (0.181). Maternal permanent environmental variance was found to influence the early body weight traits. Negative and high estimate of covariance between direct and maternal effects, resulted in highly inflated values of additive heritability. In this condition, it is more useful to use the total heritability (h2t) for evaluation of the response for selection based on phenotypic values to prevent the use of biased estimates of additive heritability. Genetic and phenotypic correlations among body weights at different ages were positive and ranged from medium to high.

Prospective of Mitochondrial DNA Variations in Cancer on Genomic Medicine.

Mitochondrial DNA (mtDNA) has emerged as a pivotal component in understanding the etiology and susceptibility of cancer. A recent study by Chen and colleagues delineated the germline genetic effect of mtDNA single-nucleotide polymorphisms (SNP) and haplogroups across pan-cancer risk. They identified a subset of mtSNPs and the corresponding risk score, as well as haplogroups A and M7 alongside their genetic interactions, conferring a protective effect against various cancers. These findings underscored the value of mtDNA variations as biomarkers for cancer etiology and as tools for cancer risk stratification. Future investigations are encouraged to integrate comprehensive omics data of genomics, transcriptomics, proteomics, and metabolomics, etc., from nuclear DNA with mtDNA variations, alongside single-cell and spatial technologies, to unravel the tumor mechanism and identify the drug targets. Moreover, the incorporation of polygenic risk score, that included mtDNA variations with both rare and common frequencies, and liquid biopsy-based biomarkers would enhance the predictive performance of cancer risk assessment and refine the risk stratification of population-based cancer screening. This commentary advocates for the validation across diverse populations to harness the full potential of mitochondrial genomics, and ultimately paves the prospective way for advancements in personalized cancer therapeutics and prevention strategies. See related article by Chen and colleagues, Cancer Epidemiol Biomarkers Prev 2024;33:381-8.

SARA captures disparate progression and responsiveness in spinocerebellar ataxias.

The Scale for Assessment and Rating of Ataxia (SARA) is a widely used clinical scale to assess cerebellar ataxia but faces some criticisms about the relevancy of all its items. To prepare for future clinical trials, we analyzed the progression of SARA and its items in several polyQ spinocerebellar ataxias (SCA) from various cohorts. We included data from patients with SCA1, SCA2, SCA3, and SCA6 from four cohorts (EUROSCA, RISCA, CRC-SCA, and SPATAX) for a total of 850 carriers and 3431 observations. Longitudinal progression of the SARA and its items was measured. Cohort, stage and genetic effects were tested. We looked at the respective contribution of each item to the total scale. Sensitivity to change of the scale and the impact of item removal was evaluated by calculating sample sizes needed in various scenarios. Longitudinal progression was significantly different between cohorts in SCA1, SCA2 and SCA3, the EUROSCA cohort having the fastest progression. Advanced-stage patients were progressing slower in SCA2 and SCA6. Items were not contributing equally to the full scale through ataxia severity: gait, stance, hand movement, and heel-shin contributed the most in the early stage, and finger-chase, nose-finger, and sitting in later stages. Few items drove the sensitivity to the change of SARA, but changes in the scale structure could not improve its sensitivity in all populations. SARA and its item's progression pace showed high heterogeneity across cohorts and SCAs. However, no combinations of items improved the responsiveness in all SCAs or populations taken separately.

Bayesian genome-wide TWAS with reference transcriptomic data of brain and blood tissues identified 141 risk genes for Alzheimer’s disease dementia

BackgroundTranscriptome-wide association study (TWAS) is an influential tool for identifying genes associated with complex diseases whose genetic effects are likely mediated through transcriptome. TWAS utilizes reference genetic and transcriptomic data to estimate effect sizes of genetic variants on gene expression (i.e., effect sizes of a broad sense of expression quantitative trait loci, eQTL). These estimated effect sizes are employed as variant weights in gene-based association tests, facilitating the mapping of risk genes with genome-wide association study (GWAS) data. However, most existing TWAS of Alzheimer's disease (AD) dementia are limited to studying only cis-eQTL proximal to the test gene. To overcome this limitation, we applied the Bayesian Genome-wide TWAS (BGW-TWAS) method to leveraging both cis- and trans- eQTL of brain and blood tissues, in order to enhance mapping risk genes for AD dementia.MethodsWe first applied BGW-TWAS to the Genotype-Tissue Expression (GTEx) V8 dataset to estimate cis- and trans- eQTL effect sizes of the prefrontal cortex, cortex, and whole blood tissues. Estimated eQTL effect sizes were integrated with the summary data of the most recent GWAS of AD dementia to obtain BGW-TWAS (i.e., gene-based association test) p-values of AD dementia per gene per tissue type. Then we used the aggregated Cauchy association test to combine TWAS p-values across three tissues to obtain omnibus TWAS p-values per gene.ResultsWe identified 85 significant genes in prefrontal cortex, 82 in cortex, and 76 in whole blood that were significantly associated with AD dementia. By combining BGW-TWAS p-values across these three tissues, we obtained 141 significant risk genes including 34 genes primarily due to trans-eQTL and 35 mapped risk genes in GWAS Catalog. With these 141 significant risk genes, we detected functional clusters comprised of both known mapped GWAS risk genes of AD in GWAS Catalog and our identified TWAS risk genes by protein-protein interaction network analysis, as well as several enriched phenotypes related to AD.ConclusionWe applied BGW-TWAS and aggregated Cauchy test methods to integrate both cis- and trans- eQTL data of brain and blood tissues with GWAS summary data, identifying 141 TWAS risk genes of AD dementia. These identified risk genes provide novel insights into the underlying biological mechanisms of AD dementia and potential gene targets for therapeutics development.

Prospective Effects of Self-Rated Health on Dementia Risk in Two Twin Studies of Aging

Subjective health ratings are associated with dementia risk such that those who rate their health more poorly have increased risk for dementia. The genetic and environmental mechanisms underlying this association are unclear, as prior research cannot rule out whether the association is due to genetic confounds. The current study addresses this gap in two samples of twins, one from Sweden (N = 548) and one from Denmark (N = 4,373). Using genetically-informed, bivariate regression models, we assessed whether additive genetic effects explained the association between subjective health and dementia risk as indexed by a latent variable proxy measure. Age at intake, sex, education, depressive symptomatology, and follow-up time between subjective health and dementia risk assessments were included as covariates. Results indicate that genetic variance and other sources of confounding accounted for the majority of the effect of subjective health ratings on dementia risk. After adjusting for genetic confounding and other covariates, a small correlation was observed between subjective health and latent dementia risk in the Danish sample (rE = − .09, p < .05). The results provide further support for the genetic association between subjective health and dementia risk, and also suggest that subjective ratings of health measures may be useful for predicting dementia risk.

Genetic architecture and correlations between the gut microbiome and gut gene transcription in Chinook salmon (Oncorhynchus tshawytscha).

Population divergence through selection can drive local adaptation in natural populations which has implications for the effective restoration of declining and extirpated populations. However, adaptation to local environmental conditions is complicated when both the host and its associated microbiomes must respond via co-evolutionary change. Nevertheless, for adaptation to occur through selection, variation in both host and microbiome traits should include additive genetic effects. Here we focus on host immune function and quantify factors affecting variation in gut immune gene transcription and gut bacterial community composition in early life-stage Chinook salmon (Oncorhynchus tshawytscha). Specifically, we utilized a replicated factorial breeding design to determine the genetic architecture (sire, dam and sire-by-dam interaction) of gut immune gene transcription and microbiome composition. Furthermore, we explored correlations between host gut gene transcription and microbiota composition. Gene transcription was quantified using nanofluidic qPCR arrays (22 target genes) and microbiota composition using 16 S rRNA gene (V5-V6) amplicon sequencing. We discovered limited but significant genetic architecture in gut microbiota composition and transcriptional profiles. We also identified significant correlations between gut gene transcription and microbiota composition, highlighting potential mechanisms for functional interactions between the two. Overall, this study provides support for the co-evolution of host immune function and their gut microbiota in Chinook salmon, a species recognized as locally adapted. Thus, the inclusion of immune gene transcription profile and gut microbiome composition as factors in the development of conservation and commercial rearing practices may provide new and more effective approaches to captive rearing.

Dual-trait genomic analysis in highly stratified Arabidopsis thaliana populations using genome-wide association summary statistics.

Genome-wide association study (GWAS) is a powerful tool to identify genomic loci underlying complex traits. However, the application in natural populations comes with challenges, especially power loss due to population stratification. Here, we introduce a bivariate analysis approach to a GWAS dataset of Arabidopsis thaliana. We demonstrate the efficiency of dual-phenotype analysis to uncover hidden genetic loci masked by population structure via a series of simulations. In real data analysis, a common allele, strongly confounded with population structure, is discovered to be associated with late flowering and slow maturation of the plant. The discovered genetic effect on flowering time is further replicated in independent datasets. Using Mendelian randomization analysis based on summary statistics from our GWAS and expression QTL scans, we predicted and replicated a candidate gene AT1G11560 that potentially causes this association. Further analysis indicates that this locus is co-selected with flowering-time-related genes. The discovered pleiotropic genotype-phenotype map provides new insights into understanding the genetic correlation of complex traits.

Simulation of functional additive and non-additive genetic effects using statistical estimates from quantitative genetic models.

Stochastic simulation software is commonly used to aid breeders designing cost-effective breeding programs and to validate statistical models used in genetic evaluation. An essential feature of the software is the ability to simulate populations with desired genetic and non-genetic parameters. However, this feature often fails when non-additive effects due to dominance or epistasis are modeled, as the desired properties of simulated populations are estimated from classical quantitative genetic statistical models formulated at the population level. The software simulates underlying functional effects for genotypic values at the individual level, which are not necessarily the same as effects from statistical models in which dominance and epistasis are included. This paper provides the theoretical basis and mathematical formulas for the transformation between functional and statistical effects in such simulations. The transformation is demonstrated with two statistical models analyzing individual phenotypes in a single population (common in animal breeding) and plot phenotypes of three-way hybrids involving two inbred populations (observed in some crop breeding programs). We also describe different methods for the simulation of functional effects for additive genetics, dominance, and epistasis to achieve the desired levels of variance components in classical statistical models used in quantitative genetics.

Differential etiologic associations of heroin use and prescription opioid misuse with psychopathology.

Patterns of association with externalizing and internalizing features differ across heroin use and prescription opioid misuse (POM). The present study examined whether heroin use and POM display differential etiologic overlap with symptoms of conduct disorder (CD), adult antisocial behavior (AAB), and major depressive episodes (MDEs), how aggregating heroin use and POM into a single phenotype may bias results, and explored potential sex differences. Seven thousand one hundred and sixty-four individual twins from the Australian Twin Registry (ATR; 59.81% female; Mage = 30.58 years) reported lifetime heroin use, POM, CD symptoms, AABs, and MDE symptoms within a semi-structured interview. Biometric models decomposed phenotypic variance and covariance into additive genetic, common environmental, and unique environmental effects. The proportion of variance in heroin use attributable to factors shared with CD, AAB, and MDE, respectively, was 41%, 41%, and 0% for men and 26%, 19%, and 42% for women; for POM, the proportions were 33%, 35%, and 20% for men and 15%, 9%, and 13% for women. CD and AAB were more strongly genetically correlated with heroin use among women and with POM among men. MDE was more strongly genetically correlated with POM than with heroin use among men, but more strongly genetically correlated with heroin use than with POM among women. Analyses using an aggregate opioid (mis)use variable were biased toward POM, which was the more prevalent phenotype. Magnitude and source of etiologic influence may differ across forms of opioid (mis)use and sex. Disaggregating heroin use and POM in future opioid research may be warranted. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Circulating anti-Müllerian hormone levels in pre-menopausal women: novel genetic insights from a genome-wide association meta-analysis.

Can a genome-wide association study (GWAS) meta-analysis, including a large sample of young premenopausal women from a founder population from Northern Finland, identify novel genetic variants for circulating anti-Müllerian hormone (AMH) levels and provide insights into single-nucleotide polymorphism enrichment in different biological pathways and tissues involved in AMH regulation? The meta-analysis identified a total of six loci associated with AMH levels at P < 5 × 10-8, three of which were novel in or near CHEK2, BMP4, and EIF4EBP1, as well as highlighted significant enrichment in renal system vasculature morphogenesis, and the pituitary gland as the top associated tissue in tissue enrichment analysis. AMH is expressed by preantral and small antral stage ovarian follicles in women, and variation in age-specific circulating AMH levels has been associated with several health conditions. However, the biological mechanisms underlying the association between health conditions and AMH levels are not yet fully understood. Previous GWAS have identified loci associated with AMH levels in pre-menopausal women, in or near MCM8, AMH, TEX41, and CDCA7. We performed a GWAS meta-analysis for circulating AMH level measurements in 9668 pre-menopausal women. We performed a GWAS meta-analysis in which we combined 2619 AMH measurements (at age 31 years) from a prospective founder population cohort (Northern Finland Birth Cohort 1966, NFBC1966) with a previous GWAS meta-analysis that included 7049 pre-menopausal women (age range 15-48 years) (N = 9668). NFBC1966 AMH measurements were quantified using an automated assay. We annotated the genetic variants, combined different data layers to prioritize potential candidate genes, described significant pathways and tissues enriched by the GWAS signals, identified plausible regulatory roles using colocalization analysis, and leveraged publicly available summary statistics to assess genetic and phenotypic correlations with multiple traits. Three novel genome-wide significant loci were identified. One of these is in complete linkage disequilibrium with c.1100delC in CHEK2, which is found to be 4-fold enriched in the Finnish population compared to other European populations. We propose a plausible regulatory effect of some of the GWAS variants linked to AMH, as they colocalize with GWAS signals associated with gene expression levels of BMP4, TEX41, and EIFBP41. Gene set analysis highlighted significant enrichment in renal system vasculature morphogenesis, and tissue enrichment analysis ranked the pituitary gland as the top association. The GWAS meta-analysis summary statistics are available for download from the GWAS Catalogue with accession number GCST90428625. This study only included women of European ancestry and the lack of sufficiently sized relevant tissue data in gene expression datasets hinders the assessment of potential regulatory effects in reproductive tissues. Our results highlight the increased power of founder populations and larger sample sizes to boost the discovery of novel trait-associated variants underlying variation in AMH levels, which aided the characterization of GWAS signals enrichment in different biological pathways and plausible genetic regulatory effects linked with AMH level variation for the first time. This work has received funding from the European Union's Horizon 2020 Research and Innovation Programme under the MATER Marie Sklodowska-Curie Grant Agreement No. 813707 and Oulu University Scholarship Foundation and Paulon Säätiö Foundation. (N.P.-G.), Academy of Finland, Sigrid Jusélius Foundation, Novo Nordisk, University of Oulu, Roche Diagnostics (T.T.P.). This work was supported by the Estonian Research Council Grant 1911 (R.M.). J.R. was supported by the European Union's Horizon 2020 Research and Innovation Program under Grant Agreements No. 874739 (LongITools), 824989 (EUCAN-Connect), 848158 (EarlyCause), and 733206 (LifeCycle). U.V. was supported by the Estonian Research Council grant PRG (PRG1291). The NFBC1966 received financial support from University of Oulu Grant No. 24000692, Oulu University Hospital Grant No. 24301140, and ERDF European Regional Development Fund Grant No. 539/2010 A31592. T.T.P. has received grants from Roche, Perkin Elmer, and honoraria for scientific presentations from Gedeon Richter, Exeltis, Astellas, Roche, Stragen, Astra Zeneca, Merck, MSD, Ferring, Duodecim, and Ajaton Terveys. For all other authors, there are no competing interests.

Genetic Association of APOA5 and AKT3 Genes with Milk Production Traits in Chinese Holstein Cows

Genome selection (GS) technology is an important means to improve the genetic improvement of dairy cows, and the mining and application of functional genes and loci for important traits is one of the important bases for accelerating genetic improvement. Our previous study found that the apolipoprotein A5 (APOA5) and AKT serine/threonine kinase 3 (AKT3) genes were differentially expressed in the liver tissue of Chinese Holstein cows at different lactation stages and influenced milk component synthesis and metabolism, so we considered these two genes as the candidates affecting milk production traits. In this study, we found in total six single nucleotide polymorphisms (SNPs), three in APOA5 and three in AKT3. Subsequent association analysis showed that the six SNPs were significantly associated with milk yield, fat yield, protein yield, or fat percentage (p ≤ 0.05). Three SNPs in APOA5 formed a haplotype block, which was found to be significantly associated with milk yield, fat yield, and protein yield (p ≤ 0.05). In addition, four SNPs were proposed to be functional mutations affecting the milk production phenotype, of which three, 15:g.27446527C&gt;T and 15:g.27447741A&gt;G in APOA5 and 16:g.33367767T&gt;C in AKT3, might change the transcription factor binding sites (TFBSs), and one is a missense mutation, 15:g.27445825T&gt;C in APOA5, which could alter the secondary structure and stability of mRNA and protein. In summary, we demonstrated the genetic effects of APOA5 and AKT3 on milk production traits, and the valuable SNPs could be used as available genetic markers for dairy cattle’s GS.

A polygenic score method boosted by non-additive models

Dominance heritability in complex traits has received increasing recognition. However, most polygenic score (PGS) approaches do not incorporate non-additive effects. Here, we present GenoBoost, a flexible PGS modeling framework capable of considering both additive and non-additive effects, specifically focusing on genetic dominance. Building on statistical boosting theory, we derive provably optimal GenoBoost scores and provide its efficient implementation for analyzing large-scale cohorts. We benchmark it against seven commonly used PGS methods and demonstrate its competitive predictive performance. GenoBoost is ranked the best for four traits and second-best for three traits among twelve tested disease outcomes in UK Biobank. We reveal that GenoBoost improves prediction for autoimmune diseases by incorporating non-additive effects localized in the MHC locus and, more broadly, works best in less polygenic traits. We further demonstrate that GenoBoost can infer the mode of genetic inheritance without requiring prior knowledge. For example, GenoBoost finds non-zero genetic dominance effects for 602 of 900 selected genetic variants, resulting in 2.5% improvements in predicting psoriasis cases. Lastly, we show that GenoBoost can prioritize genetic loci with genetic dominance not previously reported in the GWAS catalog. Our results highlight the increased accuracy and biological insights from incorporating non-additive effects in PGS models.

Connecting the dots: molecular pathways in bicuspid aortopathy explored

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Junta de Andalucía, Ministerio de Ciencia e Innovación Background Bicuspid aortic valve (BAV) is the most common congenital cardiac malformation. It predisposes to thoracic aortic dilatation (TAD), aneurysm and dissection. The current consensus is that the genetic defect that leads to the aortic valve malformation also causes structural anomalies of the ascending aorta. Altered hemodynamics caused by the abnormal valve morphology would accelerate aortic media degeneration. Molecular markers and pathways involved in the aetiology and pathophysiology of bicuspid aortopathy are poorly understood. Objective To elucidate the molecular and cellular mechanisms of the disease and to identify potential predictive molecular markers using a well-established isogenic hamster model (T strain) with a high (∼40%) incidence of BAV TAD. Methods Comparative quantitative proteomics combined with Western blot and morpho-molecular analyses in the ascending aorta of tricuspid aortic valve (TAV) and BAV animals from the T strain and TAV animals from a control strain. This strategy allows exploring independently the genetic and hemodynamic effects on the aorta in genetically homogeneous populations. Results The major molecular defect in the aorta of genetically homogeneous BAV individuals is PI3K/AKT overactivation caused by alterations in the EGF, ANGII and TGF-β signalling pathways. PI3K/AKT affects the downstream eNOS, MAP2K1/2, NF-κB, mTOR and WNT pathways. Most of these alterations are seen in independent patient studies with different clinical presentations, but not in TAV hamsters from the T strain, which mainly show downregulation of the WNT pathway. Conclusions We identify a combination of defective interconnected molecular pathways, directly linked to the central PI3K/AKT pathway, common to both BAV-associated TAD patients and hamsters. The defects indicate a shift of smooth muscle cells towards the synthetic phenotype induced by endothelial-to-mesenchymal transition, oxidative stress and inflammation. WNT signalling represents a genetic factor that may cause aortic structural abnormalities and aneurysm predisposition, whereas hemodynamics is the main trigger of molecular changes, likely determining the progression of aortopathy. We identify twenty-seven novel potential biomarkers with high predictive value.

PC Gene Affects Milk Production Traits in Dairy Cattle.

In previous work, we found that PC was differentially expressed in cows at different lactation stages. Thus, we deemed that PC may be a candidate gene affecting milk production traits in dairy cattle. In this study, we found the polymorphisms of PC by resequencing and verified their genetic associations with milk production traits by using an animal model in a cattle population. In total, we detected six single-nucleotide polymorphisms (SNPs) in PC. The single marker association analysis showed that all SNPs were significantly associated with the five milk production traits (p < 0.05). Additionally, we predicted that allele G of 29:g.44965658 in the 5' regulatory region created binding sites for TF GATA1 and verified that this allele inhibited the transcriptional activity of PC by the dual-luciferase reporter assay. In conclusion, we proved that PC had a prominent genetic effect on milk production traits, and six SNPs with prominent genetic effects could be used as markers for genomic selection (GS) in dairy cattle, which is beneficial for accelerating the improvement in milk yield and quality in Chinese Holstein cows.