Genetic Effects Research Articles

Influence of additive and maternal effects on growth traits in Deccani sheep through Bayesian inference

The aim of this study was to estimate genetic parameters for growth traits in Deccani sheep using Bayesian inference. A dataset comprising pedigree information and growth trait records from 2590 Deccani animals, born to 127 sires and 836 dams between 2011 and 2020, was analyzed. The growth traits considered included birth weight (BWT), weaning weight (WWT), and weights at six (SWT), nine (NWT), and twelve (YWT) months of age. Genetic evaluation employed six univariate animal models incorporating direct and maternal effects, implemented through THRGIBBS1F90 and POSTGIBBSF90 programs. Fixed factors adjusted in the analysis included year of birth, sex of lamb, season of lambing, and dam’s age and weight at lambing. Bayesian estimates of direct heritability, under the best model, revealed values of 0.14±0.05, 0.13±0.01, 0.20±0.04, 0.15±0.06, and 0.18±0.05 for BWT, WWT, SWT, NWT, and YWT traits, respectively. Corresponding maternal heritability estimates were 0.07±0.02, 0.08±0.01, 0.02±0.01, 0.10±0.02, and 0.17±0.06. Negative genetic correlations between additive and maternal genetic effects ranged from -0.14 to 0.07, while maternal permanent environmental influences were negligible. Positive and moderate to high genetic correlations among growth traits suggest potential relationships between them. These findings suggested selection based on WWT because it measured early in life, and has high correlation with other body weight especially SWT trait.

Stimulating Wnt signaling reveals context-dependent genetic effects on gene regulation in primary human neural progenitors.

Gene regulatory effects have been difficult to detect at many non-coding loci associated with brain-related traits, likely because some genetic variants have distinct functions in specific contexts. To explore context-dependent gene regulation, we measured chromatin accessibility and gene expression after activation of the canonical Wnt pathway in primary human neural progenitors (n = 82 donors). We found that TCF/LEF motifs and brain-structure-associated and neuropsychiatric-disorder-associated variants were enriched within Wnt-responsive regulatory elements. Genetically influenced regulatory elements were enriched in genomic regions under positive selection along the human lineage. Wnt pathway stimulation increased detection of genetically influenced regulatory elements/genes by 66%/53% and enabled identification of 397 regulatory elements primed to regulate gene expression. Stimulation also increased identification of shared genetic effects on molecular and complex brain traits by up to 70%, suggesting that genetic variant function during neurodevelopmental patterning can lead to differences in adult brain and behavioral traits.

Variation in Seedling Quality Attributes of Lentil (Lens <i>culinaris</i> Medik.) After Harvested Under Different Sowing Dates

Planting dates are the most important components for Lentil seedling growth and development. Seeds planted in diverse environmental situations lead to changes in various seedling development and the final output of the yield. The present study, aimed to assess the effect of different planting (sowing) dates harvested seeds on the physiological properties of nineteen (19) lentil genotypes. Lentil was growing during two consecutive planting days on average the normal (main) 17/08/2021 and lately 16/09/2021, then harvested seed extract after five months of storage, planted CRD with two factorial designs and three replications under laboratory condition within a white plastic container filled with autoclaved 3.0kg sand applying 100ml distilled water. The lentil was grown per standard, and the lentil's postharvest physiological quality parameters were evaluated. Results indicated that the lowest speed of germination index was obtained during late planting time or stress situations on the genotype Adda (8.78) and ILL-1760 (8.94). Seedling length is the seedling vigor criterion and is statistically significantly different between planting dates at 1%. The effect of planting day on seedling fresh weight was significantly different (p < 0.01) on genotype Beredu (0.0022) on the main planting day. The seed viability on vigor index (II) as shown in was affected by planting date (p < 0.01) and genotypes (p < 0.01). The heritability value of shoot length, root length, and seedling fresh weight equal zero was the variation due to environmental it had no genetic effect after five months of storage both main and late planting. The seedling vigor index, fresh and dry weight traits maximum on the main planting day on the genotype Beredu indicates 0.0445, 0.00218, and 0.00045 respectively. Seeds from the main planting day had the best seedling traits performance and recorded minimal germination time compared to late planting seeds. Lentil seeds planted lately finally recorded lower seedling fresh and dry weight, vigor, and speed of germination index as compared to those harvested lentil seeds in main planting dates. As a result, when there is adequate rainfall necessary for lentil growth, development, and final output.

Evaluating animal models comprising additive genetic and maternal effects on growth traits in German Angora rabbit

This investigation was carried out to estimate the genetic parameters for German Angora rabbits using most appropriate animal model comprising additive and maternal effects. The pedigree information and data on growth traits were collected from 5221 rabbits, which descended from 699 does and 294 bucks over a period of 21 yr (2001–2021) kept at Northern Temperate Regional Station, Garsa, Kullu (Himachal Pradesh), India. The average daily gain (ADG) and Kleiber ratio (KR) were calculated at different age intervals. Analyses were carried out by restricted maximum likelihood procedure fitting six animal models with various combinations of direct and maternal effects. The best model was evaluated on the basis of a likelihood ratio test. Analysis revealed that the model including both direct and maternal genetic effects along with permanent effect of the dam was most suitable for all traits except for body weight (BW) at 6th wk of age, ADG from 6th to 12th wk of age and KR associated to ADG from 12th to 18th wk of age. The direct heritability estimates from the best model were ranged from, 0.34±0.05 to 0.49±0.05 for BW; 0.19±0.04, to 0.46±0.06 for ADG and 0.21±0.04 to 0.41±0.05 for KR, respectively. Direct heritability estimates were overestimated when maternal effects were ignored. Maternal effects on BW declined from 0.49±0.04 at weaning to 0.06±0.03 at 12th wk of age and 0.09±0.04 at 18th wk of age. Correlations between direct and maternal effects ranged from −0.44±0.15 to−0.52±0.14 for body weights, indicating biological antagonism between these effects. Genetic correlations among various growth traits were positive and high, indicating scope for correlated response in later expressed traits. Analysis revealed that maternal additive influences were only important until weaning, whereas permanent environmental maternal influences were present in all growth traits considered in this study. The moderate estimates of heritability for growth traits and Kleiber ratio of rabbit in this study indicate that rates of genetic progress may be possible for these traits by selection under the standard management system.

Estimating genetic parameters and trends in growth curve traits of Zandi sheep using the SAEM algorithm.

The objectives of this study were to evaluate the influence of environmental effects on growth curve traits of Zandi lambs and estimate their genetic parameters with the best-fit animal model. For this purpose, live body weight (BW) records (n = 10,607) of 2,519 individuals (which were progeny of 278 rams and 1,485 ewes) were used to estimate genetic effects on growth curve traits from birth to yearling age of Zandi lambs. Using the Stochastic Approximation Expectation Maximization (SAEM) algorithm the growth curve parameters of five different mixed functions (i.e., Brody, Richards, Von Bertalanffy, Gompertz and Logistic) were obtained, then for the most appropriate model the genetic parameters were estimated using a Bayesian approach fitted multivariate animal model and ignoring or including maternal genetic effect. Except Richards model, all other mixed functions used here closely fitted actual BW records (R2 > 0.96). However, the Logistic function provided the best fit in every type. So, studied growth curve traits were estimated asymptotic weight which considered as mature weight (a), rate parameter (b), rate of maturing (k), and age (Ai)/weight (Wi) at the point of inflection. Of the fixed effects studied (i.e., gender, birth type, dam age, season and year of birth), the only non-significant relationship was the effect dam age on b and Ai. Based on the best-fitted model, posterior means of heritability estimates for a, b, k, Wi and Ai were 0.142 ± 0.036, 0.094 ± 0.029, 0.143 ± 0.063, 0.149 ± 0.039 and 0.029 ± 0.013, respectively. Posterior means of genetic correlations between mentioned traits ranged from -0.018 ± 0.069 (b-k) to 0.959 ± 0.029 (a-b), whereas the phenotypic correlation varied from -0.047 ± 0.014 (b-k) to 0.836 ± 0.007 (a-b). It was concluded that the model including only direct additive effect was sufficient to explain the variation in all investigated growth traits of Zandi lambs, selection for these traits results in slow genetic gain (due to the lack of sufficient genetic variation), but it would not be difficult to improve their mature weight and rate of maturing jointly. The results indicate that although the rate of genetic change for mature weight has been small (0.008 ± 0.003 kgyear-1; P < 0.05) but in the favorable direction for this breed.

Transcripts with high distal heritability mediate genetic effects on complex metabolic traits.

Although many genes are subject to local regulation, recent evidence suggests that complex distal regulation may be more important in mediating phenotypic variability. To assess the role of distal gene regulation in complex traits, we combined multi-tissue transcriptomes with physiological outcomes to model diet-induced obesity and metabolic disease in a population of Diversity Outbred mice. Using a novel high-dimensional mediation analysis, we identified a composite transcriptome signature that summarized genetic effects on gene expression and explained 30% of the variation across all metabolic traits. The signature was heritable, interpretable in biological terms, and predicted obesity status from gene expression in an independently derived mouse cohort and multiple human studies. Transcripts contributing most strongly to this composite mediator frequently had complex, distal regulation distributed throughout the genome. These results suggest that trait-relevant variation in transcription is largely distally regulated, but is nonetheless identifiable, interpretable, and translatable across species.

Genetic variability and diversity analysis for some agronomic traits of a sweet potato (Ipomoea batatas L.) collection: Insights for breeding superior genotypes

A study was conducted during the 2017 cropping season to assess the genetic variability of selected agronomic traits in sweet potato. A total of 355 sweet potato genotypes (351 from different countries of the world and four check varieties) were evaluated at Tuber Crops Research Sub Centre, Bangladesh Agricultural Research Institution, Bogura, Bangladesh. The experiment was conducted using an augmented randomized complete block design. Findings revealed positive correlations among five agronomic traits, namely the weight of marketable storage root per plant (MRW), weight of storage root per plant (RW), fresh weight of foliage per plant (FW), number of marketable storage root per plant (MRN) and number of storage root per plant (RN) suggesting their suitability for phenotypic selection. There was a positive skewness in most studied traits (FW, MRW, MRW and RW), indicating the presence of a few genotypes with exceptionally high values for those traits. By employing principal component analysis, the most influential traits in sweet potato genotypes were identified for first two components (Dim) showing significant contributions to Dim-1 and Dim-2 collectively accounting for 84.7 % of the total variance. Through cluster analysis, two main clusters (Cluster I and Cluster II) were identified among the studied genotypes, each characterized by distinct trait patterns. Cluster I demonstrated superior performance in all traits, while Cluster II exhibited lower values for all traits. Further sub-clustering within Cluster II (sub-clusters IIA and IIB) revealed additional variations of the collection. The Euclidean distance for inter-cluster (ranging from 7.94 to 11.39) and intra-cluster (ranging from 3.17 to 10.38) were determined utilizing the complete distance method. In restricted maximum likelihood (REML) model, all the traits were significantly influenced by genetic effects, with broad-sense heritability ranging from 32 % to 70 %. The REML displayed high selection accuracy, with values exceeding 91 % for studied traits. The Multi-Trait Genotype-Ideotype Distance Index (MGIDI) identified 18 genotypes, with Moz1.15 emerging as the best-performing genotype under a 5 % selection pressure. Among the selected genotypes, trait uniqueness was highest for FW (54 %), followed by RN (50 %), MRN (18 %), MRW (14 %), and RW (5 %). The selection gain for these top-ranked genotypes varied between 23.2 % and 69.2 %. Findings established the existence of useful genetic variability for selected agronomic traits of sweet potato that could be exploited for breeding, genetics study and conservation.

Powerful mapping of cis-genetic effects on gene expression across diverse populations reveals novel disease-critical genes.

While disease-associated variants identified by genome-wide association studies (GWAS) most likely regulate gene expression levels, linking variants to target genes is critical to determining the functional mechanisms of these variants. Genetic effects on gene expression have been extensively characterized by expression quantitative trait loci (eQTL) studies, yet data from non-European populations is limited. This restricts our understanding of disease to genes whose regulatory variants are common in European populations. While previous work has leveraged data from multiple populations to improve GWAS power and polygenic risk score (PRS) accuracy, multi-ancestry data has not yet been used to better estimate cis-genetic effects on gene expression. Here, we present a new method, Multi-Ancestry Gene Expression Prediction Regularized Optimization (MAGEPRO), which constructs robust genetic models of gene expression in understudied populations or cell types by fitting a regularized linear combination of eQTL summary data across diverse cohorts. In simulations, our tool generates more accurate models of gene expression than widely-used LASSO and the state-of-the-art multi-ancestry PRS method, PRS-CSx, adapted to gene expression prediction. We attribute this improvement to MAGEPRO's ability to more accurately estimate causal eQTL effect sizes (p < 3.98 × 10-4, two-sided paired t-test). With real data, we applied MAGEPRO to 8 eQTL cohorts representing 3 ancestries (average n = 355) and consistently outperformed each of 6 competing methods in gene expression prediction tasks. Integration with GWAS summary statistics across 66 complex traits (representing 22 phenotypes and 3 ancestries) resulted in 2,331 new gene-trait associations, many of which replicate across multiple ancestries, including PHTF1 linked to white blood cell count, a gene which is overexpressed in leukemia patients. MAGEPRO also identified biologically plausible novel findings, such as PIGB, an essential component of GPI biosynthesis, associated with heart failure, which has been previously evidenced by clinical outcome data. Overall, MAGEPRO is a powerful tool to enhance inference of gene regulatory effects in underpowered datasets and has improved our understanding of population-specific and shared genetic effects on complex traits.

Seasonal and genetic effects on lipid profiles of juvenile Atlantic salmon

Seasonality can influence many physiological traits requiring optimal energetic capacity for life-history stage transitions. In Atlantic salmon, high-energy status is essential for the initiation of maturation. Earlier studies have linked a genomic region encoding vgll3 to maturation age, potentially mediated via body condition. Vgll3 has also been shown to act as an inhibitor of adipogenesis in mice. Here we investigate the influence of season and vgll3 genotypes associating with early (EE) and late (LL) maturation on lipid profiles in the muscle and liver of juvenile Atlantic salmon. We reared Atlantic salmon for two years from fertilization and sampled muscle and liver during the spring and autumn of the second year (at which time some males were sexually mature). We found no seasonal or genotype effect in the muscle lipid profiles of immature males or females. However, in the liver we detected a triacylglycerol enrichment and a genotype specific direction of change in membrane lipids, phosphatidylcholine and phosphatidylethanolamine, from spring to autumn. Specifically, from spring to autumn membrane lipid concentrations increased in vgll3*EE individuals but decreased in vgll3*LL individuals. This could be explained by 1) a seasonally more stable capacity of endoplasmic reticulum (ER) functions in vgll3*EE individuals compared to vgll3*LL individuals or 2) vgll3*LL individuals storing larger lipid droplets from spring to autumn in the liver compared to vgll3*EE individuals at the expense of ER capacity. This genotype specific seasonal direction of change in membrane lipid concentrations provides more indirect evidence of a potential mechanism linking vgll3 with lipid metabolism and storage.

Prenatal exposure to per- and polyfluoroalkyl substances, genetic factors, and autistic traits: Evidence from the Shanghai birth cohort

The epidemiological evidence regarding prenatal PFAS exposure and its interaction with genetic factors on the autistic traits risk is unclear. This study included 1610 mother-child pairs from the Shanghai Birth Cohort (SBC). Ten PFAS were quantified in blood serum collected in the first trimester. Child autistic traits were evaluated at age 4 using a Chinese version of the social responsiveness scale-short form (SRS-SF). We calculated the polygenic risk score (PRS) to evaluate the cumulative genetic effects of autism. Additive interaction models were established to explore whether genetic susceptibility modified the effects of prenatal PFAS exposure. After adjusting for confounders, we found prenatal PFOA exposure was associated with an increased risk of autistic traits in children (OR, 3.05; 95 % CI, 1.14–7.58), and the increased risk associated with PFOA was mitigated among women who reported pre-pregnancy folic acid supplementation. Additionally, an increased risk of autistic traits was observed in children with higher levels of prenatal PFHxS exposure and a high PRS (p for interaction = 0.021). Our findings suggest prenatal PFAS exposure may increase the risk of autistic traits in children, especially in those with a high genetic risk. Further research is warranted to confirm this association and explore the underlying mechanisms.

A million-cow genome-wide association study of productive life in U.S. Holstein cows

BackgroundProductive life (PL) of a cow is the time the cow remains in the milking herd from first calving to exit from the herd due to culling or death and is an important economic trait in U.S. Holstein cattle. The large samples of Holstein genomic evaluation data that have become available recently provided unprecedented statistical power to identify genetic factors affecting PL in Holstein cows using the approach of genome-wide association study (GWAS).MethodsThe GWAS analysis used 1,103,641 Holstein cows with phenotypic observations on PL and genotypes of 75,282 single nucleotide polymorphism (SNP) markers. The statistical tests and estimation of SNP additive and dominance effects used the approximate generalized least squares method implemented by the EPISNPmpi computer program.ResultsThe GWAS detected 5390 significant additive effects of PL distributed over all 29 autosomes and the X–Y nonrecombining region of the X chromosome (Chr31). Two chromosome regions had the most significant and largest cluster of additive effects, the SLC4A4-GC-NPFFR2 (SGN) region of Chr06 with pleiotropic effects for PL, fertility, somatic cell score and milk yield; and the 32–52 Mb region of Chr10 with peak effects for PL in or near RASGRP1 with many important immunity functions. The dominance tests detected 38 significant dominance effects including 12 dominance effects with sharply negative homozygous recessive genotypes on Chr18, Chr05, Chr23 and Chr24.ConclusionsThe GWAS results showed that highly significant genetic effects for PL were in chromosome regions known to have highly significant effects for fertility and health and a chromosome region with multiple genes with reproductive and immunity functions. SNPs with rare but sharply negative homozygous recessive genotypes for PL existed and should be used for eliminating heifers carrying those homozygous recessive genotypes.

Identifying genetic variants that influence the abundance of cell states in single-cell data.

Disease risk alleles influence the composition of cells present in the body, but modeling genetic effects on the cell states revealed by single-cell profiling is difficult because variant-associated states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce Genotype-Neighborhood Associations (GeNA), a statistical tool to identify cell-state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of single-cell RNA sequencing peripheral blood profiling from 969 individuals, GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (P = 1.96 × 10-11) associates with increased abundance of natural killer cells expressing tumor necrosis factor response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-tumor necrosis factor treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk.

Carbon Isotopic Composition Reflects Intrinsic Water Use Efficiency But Not its Component Traits in Sugarcane

Water is the most important resource in plant growth and is a major limiting factor in sugarcane productivity worldwide. Improving water use efficiency (WUE) can increase sugarcane productivity relative to available water resources by increasing photosynthetic capacity relative to transpiration and stomatal conductance instead of decreasing stomatal conductance. Leaf carbon stable isotopic composition (δ13Cleaf) can serve as a proxy for intrinsic WUE (WUEi) because WUEi and δ13Cleaf are theoretically related through the link between intracellular and ambient CO2 concentrations (Ci/Ca) and leaf CO2 discrimination (Δ13Cleaf). In this study we surveyed 55 sugarcane genotypes for WUEi, leaf WUE (WUEleaf), Ci/Ca, and δ13Cleaf by gas exchange measurements and stable isotope analysis. We hypothesized that significant genotypic variation was found in WUEi, WUEleaf, and δ13Cleaf within the sugarcane population in Louisiana. We also hypothesized that both WUEi and δ13Cleaf and Δ13Cleaf and Ci/Ca were correlated and that δ13Cleaf could be used as a proxy for WUEi in sugarcane. Here WUEi and WUEleaf had a genetic effect and were controlled mostly by water loss (stomatal conductance or transpiration). WUEi, WUEleaf, Ci/Ca, and δ13Cleaf were correlated, but δ13Cleaf was not correlated with the component traits of WUEi (photosynthetic rate and stomatal conductance). δ13Cleaf shows promise as a proxy for WUEi to at least be able to select the tails of the distribution, but the relationship between WUEi and δ13Cleaf may not be sufficiently strong to select WUE at a finer scale.

Large-scale genome-wide interaction analyses on multiple cardiometabolic risk factors to identify age-specific genetic risk factors.

The genetic landscape of cardiometabolic risk factors has been explored extensively. However, insight in the effects of genetic variation on these risk factors over the life course is sparse. Here, we performed genome-wide interaction studies (GWIS) on different cardiometabolic risk factors to identify age-specific genetic risks. This study included 270,276 unrelated European-ancestry participants from the UK Biobank (54.2% women, a median age of 58 [interquartile range (IQR): 50, 63] years). GWIS models with interaction terms between genetic variants and age were performed on apolipoprotein B (ApoB), low-density lipoprotein-cholesterol (LDL-C), log-transformed triglycerides (TG), body mass index (BMI) and systolic blood pressure (SBP). Replication was subsequently performed in the Copenhagen General Population Study (CGPS) and the Estonian Biobank (EstBB). Multiple lead variants were identified to have genome-wide significant interactions with age (Pinteraction < 1e - 08). In detail, rs429358 (tagging APOE4) was identified for ApoB (Pinteraction = 9.0e - 14) and TG (Pinteraction = 5.4e - 16). Three additional lead variants were identified for ApoB: rs11591147 (R46L in PCSK9, Pinteraction = 3.9e - 09), rs34601365 (near APOB, Pinteraction = 8.4e - 09) and rs17248720 (near LDLR, Pinteraction = 2.0e - 09). Effect sizes of the identified lead variants were generally closer to the null with increasing age. No variant-age interactions were identified for LDL-C, SBP and BMI. The significant interactions of rs429358 with age on ApoB and TG were replicated in both CGPS and EstBB. The majority of genetic effects on cardiometabolic risk factors remain relatively constant over age, with the noted exceptions of specific genetic effects on ApoB and TG.

Evolution of Al2Cu Secondary Phase and Precipitation Strengthening of 2219 Al Alloy with Ultrasonic Melt Treatment, Hot Rolling, and Solution Aging

This study aims to clarify the combined effect of ultrasonic melt treatment (UST), hot rolling and solution aging (HRSA) on the evolution of the Al2Cu secondary phase in 2219 Al alloy toward the optimization of its mechanical properties for high‐performance structural applications. The results show that UST is beneficial for modifying the morphology and reducing the size of the secondary phase (by an average of 17.9%), and conducive to increasing the Cu content in the Al matrix (by an average of 37.5%) during solidification. The genetic effects of UST on the microstructure generated during HRSA result in denser and finer θ′/θ″ Al2Cu particles precipitating in the α‐Al matrix, with a 62.1% increase in the precipitates volume in the HRSA sample. Theoretical calculations confirm that precipitation strengthening is the predominant mechanism for strength improvement of 2219 Al alloy after UST and HRSA. The ultimate tensile strength (UTS), yield strength (YS), and elongation (EL) of the HRSA alloy without UST are 329, 214 MPa, and 11.9%, respectively, while these parameters are increased by 12.5% (to 370 MPa), 9.8% (to 235 MPa), and 17.6% (to 14.0%) for the UST alloy, respectively.

Identification of Genetic Associations of IDH2, LDHA, and LDHB Genes with Milk Yield and Compositions in Dairy Cows.

Previous study revealed that isocitrate dehydrogenase (NADP (+)) 2, mitochondrial (IDH2), lactate dehydrogenase A (LDHA), and lactate dehydrogenase B (LDHB) genes were significantly differentially expressed in liver tissues of Holstein cows among different lactation periods and associated with lipid and protein metabolism; hence, they were considered as candidates for milk production traits. Herein, the genetic effects of the three genes on milk yield, fat, and protein traits were studied by association analysis using 926 Chinese Holstein cows from 45 sire families. As a result, five single nucleotide polymorphisms (SNPs) in IDH2, one in LDHA, and three in LDHB were identified by re-sequencing, and subsequently, they were genotyped in 926 Chinese Holstein cows by genotyping by target sequencing (GBTS). With the animal model, single-locus association analysis revealed that four SNPs in IDH2 and one SNP in LDHA were significantly associated with milk, fat, and protein yields (p ≤ 0.0491), and three SNPs in LDHB were associated with milk yield, milk fat yield, and fat percentage (p ≤ 0.0285). Further, four IDH2 SNPs were found to form a haplotype block significantly associated with milk yield, fat yield, protein yield, and protein percentage (p ≤ 0.0249). In addition, functional predictions indicated that one SNP in LDHA, g.26304153G>A, may affect transcription factor binding and two SNPs, g.88544541A>G and g.88556310T>C could alter LDHB mRNA secondary structure. In summary, this study profiled the significant genetic effects of IDH2, LDHA, and LDHB on milk yield and composition traits and provided referable genetic markers for genomic selection programs in dairy cattle.

Impact of multiple obesity metrics on hypertensive disorders of pregnancy: a meta-analysis and Mendelian randomisation study

BackgroundThe relationships between various obesity measures and hypertensive disorders of pregnancy (HDP) remain inadequately explored, and their causal links are not well understood. This study aims to clarify these associations...

Gene-variant specific effects of plasma amyloid-β levels in Swedish autosomal dominant Alzheimer disease

BackgroundSeveral blood-based biomarkers offer the opportunity of in vivo detection of brain pathology and neurodegeneration in Alzheimer disease with high specificity and sensitivity, but the performance of amyloid-β (Aβ) measurements remains under evaluation. Autosomal dominant Alzheimer disease (ADAD) with mutations in PSEN1, PSEN2 and APP can be studied as a model for sporadic Alzheimer disease. However, clarifying the genetic effects on the Aβ-levels in different matrices such as cerebrospinal fluid or plasma is crucial for generalizability and utility of data. We aimed to explore plasma Aβ concentrations over the Alzheimer disease continuum in a longitudinal cohort of genetic Alzheimer disease.Methods92 plasma samples were collected from at-risk individuals (n = 47) in a Swedish cohort of ADAD, including 18 mutation carriers (13 APPswe (p.KM670/671NL) MC), 5 PSEN1 (p.H163Y) MC) and 29 non-carriers (NC) as the reference group. Concentrations of Aβ1–38, Aβ1–40 and Aβ1–42 were analyzed in plasma using immunoprecipitation coupled to tandem liquid chromatography mass spectrometry (IP-LC-MS/MS). Cross-sectional and repeated-measures data analyses were investigated family-wise, applying non-parametric tests as well as mixed-effects models.ResultsCross-sectional analysis at baseline showed more than a 3-fold increase in all plasma Aβ peptides in APPswe MC, regardless of clinical status, compared to controls (p < 0.01). PSEN1 (p.H163Y) presymptomatic MC had a decrease of plasma Aβ1–38 compared to controls (p < 0.05). There was no difference in Aβ1–42/1–40 ratio between APPswe MC (PMC and SMC), PSEN1 MC (PMC) and controls at baseline. Notably, both cross-sectional data and repeated-measures analysis suggested that APPswe MC have a stable Aβ1–42/1–40 ratio with increasing age, in contrast to the decrease seen with aging in both controls and PSEN1 (p.H163Y) MC.ConclusionThese data show very strong mutation-specific effects on Aβ profiles in blood, most likely due to a ubiquitous production outside of the CNS. Hence, analyses in an unselected clinical setting might unintentionally disclose genetic status. Furthermore, our findings suggest that the Aβ ratio might be a poor indicator of brain Aβ pathology in selected genetic cases. The very small sample size is a limitation that needs to be considered but reflects the scarcity of longitudinal in vivo data from genetic cohorts.

Optimizing purebred selection to improve crossbred performance.

Crossbreeding is a widely adopted practice in the livestock industry, leveraging the advantages of heterosis and breed complementarity. The prediction of Crossbred Performance (CP) often relies on Purebred Performance (PB) due to limited crossbred data availability. However, the effective selection of purebred parents for enhancing CP depends on non-additive genetic effects and environmental factors. These factors are encapsulated in the genetic correlation between crossbred and purebred populations ( ). In this study, a two-way crossbreeding simulation was employed to investigate various strategies for integrating data from purebred and crossbred populations. The goal was to identify optimal models that maximize CP across different levels of . Different scenarios involving the selection of genotyped individuals from purebred and crossbred populations were explored using ssGBLUP (single-step Genomic Best Linear Unbiased Prediction) and ssGBLUP-MF (ssGBLUP with metafounders) models. The findings revealed an increase in prediction accuracy across all scenarios as values increased. Notably, in the scenario incorporating genotypes from both purebred parent breeds and their crossbreds, both ssGBLUP and ssGBLUP-MF models exhibited nearly identical predictive accuracy. This scenario achieved maximum accuracy when was less than 0.5. However, at = 0.8, ssGBLUP, which exclusively included sire breed genotypes in the training set, achieved the highest overall prediction accuracy at 73.2%. In comparison, the BLUP-UPG (BLUP with unknown parent group) model demonstrated lower accuracy than ssGBLUP and ssGBLUP-MF across all levels. Although ssGBLUP and ssGBLUP-MF did not demonstrate a definitive trend in their respective scenarios, the prediction ability for CP increased when incorporating both crossbred and purebred population genotypes at lower levels of . Furthermore, when was high, utilizing paternal genotype for CP predictions emerged as the most effective strategy. Predicted dispersion remained relatively similar in all scenarios, indicating a slight underestimation of breeding values. Overall, the value emerged as a critical factor in predicting CP based on purebred data. However, the optimal model to maximize CP depends on the factors influencing . Consequently, ongoing research aims to develop models that optimize purebred selection, further enhancing CP.

Genetic and environmental drivers of legume cover crop performance: Hairy vetch

AbstractAmong 50 environments in the United States, we screened 35 hairy vetch (Vicia villosa Roth.) lines for traits of interest to cover cropping. We analyzed the influence of genotype, environment, and the genotype‐by‐environment interaction (G × E) on biomass, vigor, winter survival, emergence, flowering time, and nitrogen fixation. To explore how environments and G × E impacted each trait, we associated environment predictions and G × E loadings with weather and soil parameters. Environment had the largest influence on all traits, representing more than half of the variance. Environment predictions were significantly associated with weather and/or soil parameters for each trait. Biomass was associated with growing degree days, winter survival with freezing degree days without snow cover, growth stage with shortwave radiation, and emergence with soil texture. The G × E interaction was larger than genotypic variance for all traits except for winter survival and flowering time. The G × E interaction loadings were associated with soil sand content for biomass, air temperature for fall vigor and emergence, and snow cover for winter survival. Although it represented the smallest proportion of total variance, genetic effects were significant for all traits except for emergence, Ndfa, %N, and C:N. New hairy vetch breeding lines were superior to all commercially available lines for biomass and winter survival. Biomass harvest timing did not significantly change line rank, indicating that top‐performing lines can be used in diverse management systems. To select for high nitrogen contribution to subsequent crops, breeding programs can indirectly select for biomass rather than expensively evaluating symbiotic nitrogen fixation.