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The presence of horizontal pleiotropy in Mendelian randomization (MR) analysis has long been a concern due to its potential to induce substantial bias. In recent years, many robust MR methods have been proposed to address this by relaxing the "no horizontal pleiotropy" assumption. Here, we propose a novel two-stage framework called CMR, which integrates a conditional analysis of multiple genetic variants to remove pleiotropy induced by linkage disequilibrium, followed by the application of robust MR methods to model the conditional genetic effect estimates. We demonstrate how the conditional analysis can reduce horizontal pleiotropy and improve the performance of existing MR methods. Extensive simulation studies covering a wide range of scenarios of horizontal pleiotropy showcased the superior performance of the proposed CMR framework over the standard MR framework in which marginal genetic effects are modeled. Moreover, the application of CMR in a negative control outcome analysis and investigation into the causal role of body mass index across various diseases highlighted its potential to deliver more reliable results in real-world applications.

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Ginseng, an important medicinal plant, is characterized by its main active component, ginsenosides. Among more than 40 ginsenosides, Rg1 is one of the ginsenosides used for measuring the quality of ginseng. Therefore, the identification and characterization of genes for Rg1 biosynthesis are important to elucidate the molecular basis of Rg1 biosynthesis. In this study, we utilized 39,327 SNPs and the corresponding Rg1 content from 344 core ginseng cultivars from Jilin Province. We conducted a genome-wide association study (GWAS) combining weighted gene co-expression network analysis (WGCNA), SNP-Rg1 content association analysis, and gene co-expression network analysis; three candidate Rg1 genes (PgRg1-1, PgRg1-2, and PgRg1-3) and one crucial candidate gene (PgRg1-3) were identified. Functional validation of PgRg1-3 was performed using methyl jasmonate (MeJA) regulation and RNAi, confirming that this gene regulates Rg1 biosynthesis. The spatial-temporal expression patterns of the PgRg1-3 gene and known key enzyme genes involved in ginsenoside biosynthesis differ. Furthermore, variations in their networks have a significant impact on Rg1 biosynthesis. This study established an accurate and efficient method for identifying candidate genes, cloned a novel gene controlling Rg1 biosynthesis, and identified 73 SNPs significantly associated with Rg1 content. This provides genetic resources and effective tools for further exploring the molecular mechanisms of Rg1 biosynthesis and molecular breeding.

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Purpose: Diabetes and its complications cause a heavy burden of disease worldwide. In recent years, Mendelian randomization (MR) has been widely used to discover the pathogenesis and epidemiology of diseases, as well as to discover new therapeutic targets. Therefore, based on systematic "druggable" genomics, we aim to identify new therapeutic targets for diabetes and analyze its pathophysiological mechanisms to promote its new therapeutic strategies. Material and method: We used double sample MR to integrate the identified druggable genomics to evaluate the causal effect of quantitative trait loci (eQTLs) expressed by druggable genes in blood on type 1 and 2 diabetes (T1DM and T2DM). Repeat the study using different data sources on diabetes and its complications to verify the identified genes. Not only that, we also use Bayesian co-localization analysis to evaluate the posterior probabilities of different causal variations, shared causal variations, and co-localization probabilities to examine the possibility of genetic confounding. Finally, using diabetes markers with available genome-wide association studies data, we evaluated the causal relationship between established diabetes markers to explore possible mechanisms. Result: Overall, a total of 4,477 unique druggable genes have been gathered. After filtering using methods such as Bonferroni significance (P<1.90e-05), the MR Steiger directionality test, Bayesian co-localization analysis, and validation with different datasets, Finally, 7 potential druggable genes that may affect the results of T1DM and 7 potential druggable genes that may affect the results of T2DM were identified. Reverse MR suggests that C4B may play a bidirectional role in the pathogenesis of T1DM, and none of the other 13 target genes have a reverse causal relationship. And the 7 target genes in T2DM may each affect the biomarkers of T2DM to mediate the pathogenesis of T2DM. Conclusion: This study provides genetic evidence supporting the potential therapeutic benefits of targeting seven druggable genes, namely MAP3K13, KCNJ11, REG4, KIF11, CCNE2, PEAK1, and NRBP1, for T2DM treatment. Similarly, targeting seven druggable genes, namely ERBB3, C4B, CD69, PTPN22, IL27, ATP2A1, and LT-ß, has The potential therapeutic benefits of T1DM treatment. This will provide new ideas for the treatment of diabetes and also help to determine the priority of drug development for diabetes.

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Our preliminary research proposed the cytochrome P450 family 7 subfamily A member 1 (CYP7A1) and hydroxyacyl-coenzyme A dehydrogenase trifunctional multienzyme complex beta subunit (HADHB) genes as candidates for association with milk-production traits in dairy cattle because of their differential expression across different lactation stages in the liver tissues of Chinese Holstein cows and their potential roles in lipid metabolism. Hence, we identified single-nucleotide polymorphisms (SNPs) of the CYP7A1 and HADHB genes and validated their genetic effects on milk-production traits in a Chinese Holstein population with the goal of providing valuable genetic markers for genomic selection (GS) in dairy cattle, This study identified five SNPs, 14:g.24676921A>G, 14:g.24676224G>A, 14:g.24675708G>T, 14:g.24665961C>T, and 14:g.24664026A>G, in the CYP7A1 gene and three SNPs, 11:g.73256269T>C, 11:g.73256227A>C, and 11:g.73242290C>T, in HADHB. The single-SNP association analysis revealed significant associations (p value ≤ 0.0461) between the eight SNPs of CYP7A1 and HADHB genes and 305-day milk, fat and protein yields. Additionally, using Haploview 4.2, we found that the five SNPs of CYP7A1 formed two haplotype blocks and that the two SNPs of HADHB formed one haplotype block; notably, all three haplotype blocks were also significantly associated with milk, fat and protein yields (p value ≤ 0.0315). Further prediction of transcription factor binding sites (TFBSs) based on Jaspar software (version 2023) showed that the 14:g.24676921A>G, 14:g.24675708G>T, 11:g.73256269T>C, and 11:g.73256227A>C SNPs could alter the 5' terminal TFBS of the CYP7A1 and HADHB genes. The 14:g.24665961C>T SNP caused changes in the structural stability of the mRNA for the CYP7A1 gene. These alterations have the potential to influence gene expression and, consequently, the phenotype associated with milk-production traits. In summary, we have confirmed the genetic effects of CYP7A1 and HADHB genes on milk-production traits in dairy cattle and identified potential functional mutations that we suggest could be used for GS of dairy cattle and in-depth mechanistic studies of animals.

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Many genetic studies contain rich information on longitudinal phenotypes that require powerful analytical tools for optimal analysis. Genetic analysis of longitudinal data that incorporates temporal variation is important for understanding the genetic architecture and biological variation of complex diseases. Most of the existing methods assume that the contribution of genetic variants is constant over time and fail to capture the dynamic pattern of disease progression. However, the relative influence of genetic variants on complex traits fluctuates over time. In this study, we propose a retrospective varying coefficient mixed model association test, RVMMAT, to detect time-varying genetic effect on longitudinal binary traits. We model dynamic genetic effect using smoothing splines, estimate model parameters by maximizing a double penalized quasi-likelihood function, design a joint test using a Cauchy combination method, and evaluate statistical significance via a retrospective approach to achieve robustness to model misspecification. Through simulations we illustrated that the retrospective varying-coefficient test was robust to model misspecification under different ascertainment schemes and gained power over the association methods assuming constant genetic effect. We applied RVMMAT to a genome-wide association analysis of longitudinal measure of hypertension in the Multi-Ethnic Study of Atherosclerosis. Pathway analysis identified two important pathways related to G-protein signaling and DNA damage. Our results demonstrated that RVMMAT could detect biologically relevant loci and pathways in a genome scan and provided insight into the genetic architecture of hypertension.

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Spike length (SL) is one of the most important agronomic traits affecting yield potential and stability in wheat. In this study, a major stable quantitative trait locus (QTL) for SL, i.e., qSl-2B, was detected in multiple environments in a recombinant inbred line (RIL) mapping population, KJ-RILs, derived from a cross between Kenong 9204 (KN9204) and Jing 411 (J411). The qSl-2B QTL was mapped to the 60.06-73.06 Mb region on chromosome 2B and could be identified in multiple mapping populations. An InDel molecular marker in the target region was developed based on a sequence analysis of the two parents. To further clarify the breeding use potential of qSl-2B, we analyzed its genetic effects and breeding selection effect using both the KJ-RIL population and a natural mapping population, which consisted of 316 breeding varieties/advanced lines. The results showed that the qSl-2B alleles from KN9204 showed inconsistent genetic effects on SL in the two mapping populations. Moreover, in the KJ-RILs population, the additive effects analysis of qSl-2B showed that additive effect was higher when both qSl-2D and qSl-5A harbor negative alleles under LN and HN. In China, a moderate selection utilization rate for qSl-2B was found in the Huanghuai winter wheat area and the selective utilization rate for qSl-2B continues to increase. The above findings provided a foundation for the genetic improvement of wheat SL in the future via molecular breeding strategies.

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Changes in avian breeding phenology are among the most apparent responses to climate change in free-ranging populations. A key question is whether populations will be able to keep up with the expected rates of environmental change. There is a large body of research on the mechanisms by which avian lay-dates track temperature change and the consequences of (mal)adaptation on population persistence. Often overlooked is the role of males, which can influence the lay-date of their mate through their effect on the prelaying environment. We explore how social plasticity causing male indirect genetic effects can help or hinder population persistence when female genes underpinning lay-date and male genes influencing female's timing of reproduction both respond to climate-mediated selection. We extend quantitative genetic moving optimum models to predict the consequences of social plasticity on the maximum sustainable rate of temperature change, and evaluate our model using a combination of simulated data and empirical estimates from the literature. Our results suggest that predictions for population persistence may be biased if indirect genetic effects and cross-sex genetic correlations are not considered and that the extent of this bias depends on sex differences in how environmental change affects the optimal timing of reproduction. Our model highlights that more empirical work is needed to understand sex-specific effects of environmental change on phenology and the fitness consequences for population dynamics. While we discuss our results exclusively in the context of avian breeding phenology, the approach we take here can be generalized to many different contexts and types of social interaction.

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Maize is one of the major crops that has demonstrated success in the utilization of heterosis. Developing high-yield hybrids is a crucial part of plant breeding to secure global food demand. In this study, we conducted a genome-wide association study (GWAS) for 10 agronomic traits using a typical breeder population comprised 442 single-cross hybrids by evaluating additive, dominance, and epistatic effects. A total of 49 significant single nucleotide polymorphisms (SNPs) and 69 significant pairs of epistasis were identified, explaining 26.2% to 64.3% of the phenotypic variation across the 10 traits. The enrichment of favorable genotypes is significantly correlated to the corresponding phenotype. In the confident region of the associated site, 532 protein-coding genes were discovered. Among these genes, the Zm00001d044211 candidate gene was found to negatively regulate starch synthesis and potentially impact yield. This typical breeding population provided a valuable resource for dissecting the genetic architecture of yield-related traits. We proposed a novel mating strategy to increase the GWAS efficiency without utilizing more resources. Finally, we analyzed the enrichment of favorable alleles in the Shaan A and Shaan B groups, as well as in each inbred line. Our breeding practice led to consistent results. Not only does this study demonstrate the feasibility of GWAS in F1 hybrid populations, it also provides a valuable basis for further molecular biology and breeding research.

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Background: Although extensive discussions on the influence of maternal educational attainment on offspring birthweight, the conclusion remains controversial, and it is challenging to comprehensively assess the causal association between them. Methods: To estimate effect of maternal educational attainment on the birthweight of first child, we first conducted an individual-level analysis with UK Biobank participants of white ancestry (n = 208,162). We then implemented Mendelian randomization (MR) methods including inverse variance weighted (IVW) MR and multivariable MR to assess the causal relation between maternal education and maternal-specific birthweight. Finally, using the UK Biobank parent-offspring trio data (n = 618), we performed a polygenic score based MR to simultaneously adjust for confounding effects of fetal-specific birthweight and paternal educational attainment. We also conducted simulations for power evaluation and sensitivity analyses for horizontal pleiotropy of instruments. Results: We observed that birthweight of first child was positively influenced by maternal education, with 7 years of maternal education as the reference, adjusted effect = 44.8 (95%CIs 38.0-51.6, P = 6.15 × 10-38), 54.9 (95%CIs 47.6-62.2, P = 4.21 × 10-128), and 89.4 (95%CIs 82.1-96.7, P = 4.28 × 10-34) for 10, 15 and 20 years of maternal educational attainment, respectively. A causal relation between maternal education and offspring birthweight was revealed by IVW MR (estimated effect = 0.074 for one standard deviation increase in maternal education years, 95%CIs 0.054-0.093, P = 2.56 × 10-13) and by complementary MR methods. This connection was not substantially affected by paternal education or horizontal pleiotropy. Further, we found a positive but insignificant causal association (adjusted effect = 24.0, 95%CIs -150.1-198.1, P = 0.787) between maternal education and offspring birthweight after simultaneously controlling for fetal genome and paternal education; this null causality was largely due to limited power of small sample sizes of parent-offspring trios. Conclusion: This study offers supportive evidence for a causal association between maternal education and offspring birthweight, highlighting the significance of enhancing maternal education to prevent low birthweight.

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Body composition traits are complex traits controlled by minor genes and, in hybrid populations, are impacted by additive and nonadditive effects. We aimed to identify candidate genes and increase the accuracy of genomic prediction of body composition traits in crossbred pigs by including dominance genetic effects. Genomic selection (GS) and genome-wide association studies were performed on seven body composition traits in 807 Yunong-black pigs using additive genomic models (AM) and additive-dominance genomic models (ADM) with an imputed high-density single nucleotide polymorphism (SNP) array and the Illumina Porcine SNP50 BeadChip. The results revealed that the additive heritabilities estimated for AM and ADM using the 50 K SNP data ranged from 0.20 to 0.34 and 0.11 to 0.30, respectively. However, the ranges of additive heritability for AM and ADM in the imputed data ranged from 0.20 to 0.36 and 0.12 to 0.30, respectively. The dominance variance accounted for 23% and 27% of the total variance for the 50 K and imputed data, respectively. The accuracy of genomic prediction improved by 5% on average for 50 K and imputed data when dominance effect were considered. Without the dominance effect, the accuracies for 50 K and imputed data were 0.35 and 0.38, respectively, and 0.41 and 0.43, respectively, upon considering it. A total of 12 significant SNP and 16 genomic regions were identified in the AM, and 14 significant SNP and 21 genomic regions were identified in the ADM for both the 50 K and imputed data. There were five overlapping SNP in the 50 K and imputed data. In the AM, a significant SNP (CNC10041568) was found in both body length and backfat thickness traits, which was in the PLAG1 gene strongly and significantly associated with body length and backfat thickness in pigs. Moreover, a significant SNP (CNC10031356) with a heterozygous dominant genotype was present in the ADM. Furthermore, several functionally related genes were associated with body composition traits, including MOS, RPS20, LYN, TGS1, TMEM68, XKR4, SEMA4D and ARNT2. These findings provide insights into molecular markers and GS breeding for the Yunong-black pigs.

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Telomere shortening is an important sign and driving factor of aging, but its association mechanisms and causal effects with other aging-related biochemical hallmarks are largely unknown. This study first performed comprehensive genetic analyses (eg, shared genetic analysis, pleiotropic analysis, and gene enrichment analysis) to detect the underlying molecular mechanisms for the associations between telomere length (TL) and aging-related biochemical hallmarks. Then, further bidirectional Mendelian randomization (MR) analyses investigated the causal effects between TL and other biochemical hallmarks. The genetic correlations were negative between TL and growth differentiation factor-15 (GDF15) (p = .024), C-reactive protein (p = .007), hemoglobin A1c (p = .007), and red blood cell (RBC) (p = .022), but positive between TL and insulin-like growth factor 1 (IGF-1) (p = .002) and white blood cell counts (p = .007). The increased TL has causal effects on the low levels of GDF15 (p = 3.73E-06), sex hormone binding globulin (p = 6.30E-06), testosterone (p = 5.56E-07), fasting insulin (p = 2.67E-05), and RBC (p = 1.54E-05), but the higher levels of IGF-1 (p = 3.24E-07). In conclusion, the observed phenotypic correlations between TL and aging-related biochemical hallmarks may arise from a combination of shared genetic components and causal effects. Telomere length is regarded as a driving hallmark for aging-related biochemical hallmarks.

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BACKGROUND: Our previous research identified the Kruppel like factor 6 (KLF6) gene as a prospective candidate for milk production traits in dairy cattle. The expression of KLF6 in the livers of Holstein cows during the peak of lactation was significantly higher than that during the dry and early lactation periods. Notably, it plays an essential role in activating peroxisome proliferator-activated receptor α (PPARα) signaling pathways. The primary aim of this study was to further substantiate whether the KLF6 gene has significant genetic effects on milk traits in dairy cattle. RESULTS: Through direct sequencing of PCR products with pooled DNA, we totally identified 12 single nucleotide polymorphisms (SNPs) within the KLF6 gene. The set of SNPs encompasses 7 located in 5' flanking region, 2 located in exon 2 and 3 located in 3' untranslated region (UTR). Of these, the g.44601035G > A is a missense mutation that resulting in the replacement of arginine (CGG) with glutamine (CAG), consequently leading to alterations in the secondary structure of the KLF6 protein, as predicted by SOPMA. The remaining 7 regulatory SNPs significantly impacted the transcriptional activity of KLF6 following mutation (P < 0.005), manifesting as changes in transcription factor binding sites. Additionally, 4 SNPs located in both the UTR and exons were predicted to influence the secondary structure of KLF6 mRNA using the RNAfold web server. Furthermore, we performed the genotype-phenotype association analysis using SAS 9.2 which found all the 12 SNPs were significantly correlated to milk yield, fat yield, fat percentage, protein yield and protein percentage within both the first and second lactations (P < 0.0001 ~ 0.0441). Also, with Haploview 4.2 software, we found the 12 SNPs linked closely and formed a haplotype block, which was strongly associated with five milk traits (P < 0.0001 ~ 0.0203). CONCLUSIONS: In summary, our study represented the KLF6 gene has significant impacts on milk yield and composition traits in dairy cattle. Among the identified SNPs, 7 were implicated in modulating milk traits by impacting transcriptional activity, 4 by altering mRNA secondary structure, and 1 by affecting the protein secondary structure of KLF6. These findings provided valuable molecular insights for genomic selection program of dairy cattle.

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As one of the major staple crops, rice feeds more than one half of the world population. Due to increasing population and dramatic climate change, the rice varieties with higher yield performance and excellent overall agronomic performance should be developed. The raise of molecular design breeding concept provides opportunity to get new breakthrough for variety development, and it is important to clarify the efficient gene combination during actual breeding. In this review, we summarize the recent advances about rice variety improvement either by marker assisted selection (MAS) breeding or popular gene editing technique, which will be beneficial to understand different aspects of the molecular design breeding. We provide genetic views for the classical MAS application, including the genetic effect of key genes and their combinations, the recurrent genome recovery rate at different backcross generations, linkage drag and recombination selection. Moreover, we compare the breeding value of recently-developed molecular techniques, including the advantage of high-throughput genotyping and the way and effect of gene editing in creating useful traits. Considering the current status and actual demands of rice breeding, we raise the strategy to take advantages of both traditional breeding resources and popular molecular techniques, which might pave the way to optimize the process of molecular design breeding in future.

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There is currently little evidence that the genetic basis of human phenotype varies significantly across the lifespan. However, time-to-event phenotypes are understudied and can be thought of as reflecting an underlying hazard, which is unlikely to be constant through life when values take a broad range. Here, we find that 74% of 245 genome-wide significant genetic associations with age at natural menopause (ANM) in the UK Biobank show a form of age-specific effect. Nineteen of these replicated discoveries are identified only by our modeling framework, which determines the time dependency of DNA-variant age-at-onset associations without a significant multiple-testing burden. Across the range of early to late menopause, we find evidence for significantly different underlying biological pathways, changes in the signs of genetic correlations of ANM to health indicators and outcomes, and differences in inferred causal relationships. We find that DNA damage response processes only act to shape ovarian reserve and depletion for women of early ANM. Genetically mediated delays in ANM were associated with increased relative risk of breast cancer and leiomyoma at all ages and with high cholesterol and heart failure for late-ANM women. These findings suggest that a better understanding of the age dependency of genetic risk factor relationships among health indicators and outcomes is achievable through appropriate statistical modeling of large-scale biobank data.

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Rye (Secale cereale L.) is an important genetic resource for improving the disease resistance of wheat. An increasing number of rye chromosome segments have been transferred into modern wheat cultivars via chromatin insertions. In this study, 185 recombinant inbred lines (RILs) derived from a cross between a wheat accession containing rye chromosomes 1RS and 3R and a wheat-breeding founder parent Chuanmai 42 from southwestern China were used to decipher the cytological and genetic effects of 1RS and 3R via fluorescence/genomic in situ hybridization and quantitative trait locus (QTL) analyses. Chromosome centromere breakage and fusion were detected in the RIL population. Additionally, the recombination of chromosomes 1BS and 3D from Chuanmai 42 was completely suppressed by 1RS and 3R in the RIL population. In contrast to chromosome 3D of Chuanmai 42, rye chromosome 3R was significantly associated with white seed coats and decreased yield-related traits, as revealed by QTL and single marker analyses, whereas it had no effect on stripe rust resistance. Rye chromosome 1RS did not affect yield-related traits and it increased the susceptibility of plants to stripe rust. Most of the detected QTLs that positively affected yield-related traits were from Chuanmai 42. The findings of this study suggest that the negative effects of rye-wheat substitutions or translocations, including the suppression of the pyramiding of favorable QTLs on paired wheat chromosomes from different parents and the transfer of disadvantageous alleles to filial generations, should be considered when selecting alien germplasm to enhance wheat-breeding founder parents or to breed new varieties. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-023-01386-0.

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Type 2 diabetes mellitus (T2DM) is a severe chronic epidemic that results from the body's improper usage of the hormone insulin. Globally, 700 million people are expected to have received a diabetes diagnosis by 2045, according to the International Diabetes Federation (IDF). Cancer and macro- and microvascular illnesses are only a few immediate and long-term issues it could lead to. T2DM accelerates the effect of organ weights by triggering a hyperinflammatory response in the body's organs, inhibiting tissue repair and resolving inflammation. Understanding how genetic variation translates into different clinical presentations may highlight the mechanisms through which dietary elements may initiate or accelerate inflammatory disease processes and suggest potential disease-prevention techniques. To address the host genetic background effect on the organ weight by utilizing the newly developed mouse model, the Collaborative Cross mice (CC). The study was conducted on 207 genetically different CC mice from 8 CC lines of both sexes. The experiment started with 8-week-old mice for 12 weeks. During this period, one group maintained a standard chow diet (CHD), while the other group maintained a high-fat diet (HFD). In addition, body weight was recorded bi-weekly, and at the end of the study, a glucose tolerance test, as well as tissue collection (liver, spleen, heart), were conducted. Our study observed a strong effect of HFD on blood glucose clearance among different CC lines. The HFD decreased the blood glucose clearance displayed by the significant Area Under Curve (AUC) values in both populations. In addition, variation in body weight changes among the different CC lines in response to HFD. The female liver weight significantly increased compared to males in the overall population when exposed to HFD. Moreover, males showed higher heritability values than females on the same diet. Regardless of the dietary challenge, the liver weight in the overall male population correlated positively with the final body weight. The liver weight results revealed that three different CC lines perform well under classification models. The regression results also varied among organs. Accordingly, the differences among these lines correspond to the genetic variance, and we suspect that some genetic factors invoke different body responses to HFD. Further investigations, such as quantitative trait loci (QTL) analysis and genomic studies, could find these genetic elements. These findings would prove critical factors for developing personalized medicine, as they could indicate future body responses to numerous situations early, thus preventing the development of complex diseases.

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The aim of this study was to investigate the paternal genetic intergenerational and transgenerational genetic effects of cadmium (Cd) exposure during pregnancy on estradiol (E2) and progesterone (Pg) synthesis in the ovarian granulosa cells (GCs) of offspring. Pregnant SD rats were intragastrically exposed to CdCl2 (0, 0.5, 2.0, 8.0 mg/kg) from days 1 to 20 to produce the F1 generation, F1 males were mated with newly purchased females to produce the F2 generation, and the F3 generation was obtained in the same way. Using this model, Cd-induced hormone synthesis disorders in GCs of F1 have been observed [8]. In this study, altered serum E2 and Pg levels in both F2 and F3 generations showed a nonmonotonic dose‒response relationship. In addition, hormone synthesis-related genes (Star, Cyp11a1, Cyp17a1, Cyp19a1, Sf-1) and miRNAs were observed to be altered in both F2 and F3. No differential changes in DNA methylation modifications of hormone synthesis-related genes were observed, and only the Adcy7 was hypomethylated. In summary, paternal genetic intergenerational and transgenerational effects exist in ovarian GCs E2 and Pg synthesis disorders induced by Cd during pregnancy. In F2, the upregulation of StAR and CYP11A1, and changes in the miR-27a-3p, miR-27b-3p, and miR-146 families may be important, while changes in the miR-10b-5p and miR-146 families in F3 may be important.

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The goal of this study was to investigate whether the inclusion of genomic information and epistatic (additive by additive) genetic effects would increase the accuracy of predicting phenotypes adjusted for known environmental effects, reduce prediction bias and minimize the confounding between additive and additive by additive epistatic effects on fertility and calving traits in Holstein cattle. Phenotypic and genotypic records were available for 6090 cows. Eight cow traits were assessed including 56-day nonreturn rate (NRR), number of services (NS), calving to first insemination (CTFS), first insemination to conception (FSTC), gestation length (GL), calving ease (CE), stillbirth (SB) and calf size (CZ). Four scenarios were assessed for their ability to predict adjusted phenotypes, which included: (1) traditional pedigree-based Best Linear Unbiased Prediction (P-BLUP) for additive genetic effects (PA); (2) P-BLUP for additive and epistatic (additive by additive) genetic effects (PAE); (3) genomic BLUP (G-BLUP) for additive genetic effects (GA); and (4) G-BLUP for additive and epistatic genetic effects (GAEn, where n = 1-3 depending on the alternative ways to construct the epistatic genomic matrix used). Constructing epistatic relationship matrix as the Hadamard product of the additive genomic relationship matrix (GAE1), which is the usual method and implicitly assumes a model that fits all pairwise interactions between markers twice and includes the interactions of the markers with themselves (dominance). Two additional constructions of the epistatic genomic relationship matrix were compared to test whether removing the double counting of interactions and the interaction of the markers with themselves (GAE2), and removing double counting of interactions between markers, but including the interaction of the markers with themselves (GAE3) would had an impact on the prediction and estimation error correlation (i.e. confounding) between additive and epistatic genetic effects. Fitting epistatic genetic effects explained up to 5.7% of the variance for NRR (GAE3), 7.7% for NS (GAE1), 11.9% for CTFS (GAE3), 11.1% for FSTC (GAE2), 25.7% for GL (GAE1), 2.3% for CE (GAE1), 14.3% for SB (GAE3) and 15.2% for CZ (GAE1). Despite a substantial proportion of variance being explained by epistatic effects for some traits, the prediction accuracies were similar or lower for GAE models compared with pedigree models and genomic models without epistatic effects. Although the prediction accuracy of direct genomic values did not change significantly between the three variations of the epistatic genetic relationship matrix used, removing the interaction of the markers with themselves reduced the confounding between additive and additive by additive epistatic effects. These results suggest that epistatic genetic effects contribute to the variance of some fertility and calving traits in Holstein cattle. However, the inclusion of epistatic genetic effects in the genomic prediction of these traits is complex and warrant further investigation.

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Data on 4487 Turkish Saanen kids from 176 bucks and 1318 dam-goats, obtained from the Turkish Saanen goats in the Izmir region and collected between 2018 and 2019, were analyzed to examine the effect of genetic and non-genetic factors on growth traits. The average birth weight of the kids was determined as 3.33 ± 0.68 kg, the average W60 was 13.06 ± 2.94 kg, the average WW was 18.38 ± 4.14 kg, and the average PreWDG until weaning was 0.17 ± 0.04 g. Model 1, which does not account for the maternal effect, and Model 2, which includes the maternal effect, were used in the estimation of genetic parameters. The heritability estimates of BW, W60, WW, and PreWDG ranged from 0.05 to 0.59 in both models. It is recommended to consider the maternal effect as well as the environmental factors in the selection program for the best early breeder selection of kids growing alongside their mothers until the weaning period.

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Objectives　Since the Fukushima Daiichi Nuclear Power Plant accident caused by the Great East Japan Earthquake, many people in Fukushima Prefecture have been concerned about the effects of radiation on their offspring. Although this fear should be relieved since no epidemiological study has shown evidential cases, situations wherein some people misunderstood the existence of evidence have been independently reported. Therefore, in this study, we examined the relationship between radiation knowledge and anxiety about radiation-based genetic effects using a survey mailed to residents in Fukushima Prefecture.Methods　In August 2016, we attempted to contact 2,000 people aged 20 to 79 years in Fukushima Prefecture through a survey distributed by mail inquiring about residents' health. We received 861 responses. Respondents selected their level of concern about radiation effects on the next generation on a four-point scale and answered five knowledge questions about radiation effects and protection. Responses were "correct," "incorrect," or "not sure." After adjusting according to residential area, family configuration, age, sex, academic background, media used, and trusted information source, we conducted multiple regression analyses to investigate the relationship between the level of concern and answers to the knowledge questions.Results　People who answered the knowledge questions more correctly had lower levels of anxiety. The number of "not sure" responses was unrelated to anxiety level. The results indicated that correct answers to questions related to radiation residuals in the body and genetic effects were negatively associated with anxiety. Incorrect answers to the question about genetic effects and another related to cell repair systems were positively associated with anxiety. Moreover, while a correct answer to another question related to the linear threshold model had a positive association, the association was insignificant. Further, any response related to radiation reference values in foods showed no association.Conclusion　Based on these results, we observed an association between the number of respondents with a correct knowledge of radiation and the degree of anxiety about radiation effects on the next generation. However, this relationship and its strength varied depending on the specific knowledge content. As a limitation of this study, the study could not establish any causality. Further studies, such as prospective interventional studies, should be conducted.

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