Embryonic losses between the early diagnosis and the confirmation of gestation in dairy cows from different farms for one year

ABSTRACT Objective: To analyze embryonic losses between early pregnancy diagnosis and confirmation in dairy cows of different farms for one year. Design/methodology/approach: Total of 3,413 Holstein milking cows stabled from three different farms were studied. Cows were milked 3 times a day and they had an average daily milk production of 36.5 ± 1.5 L. The diagnosis of pregnancy was made by ultrasonography at 34 ± 7 days post-artificial insemination and confirmation at 60 ± 5 days. Mean embryonic losses were compared with respect to farm and month. Results: Average embryonic losses were 18.8%. No effect was observed in month factor, nor in month x farm interaction in the embryonic loss percentage (p < 0.05). Factor farm showed differences (p < 0.05), embryonic loss percentages for farms 1, 2 and 3 were: 4 ± 1.6%, 11.4 ± 1.6%, y 22.9 ± 1.6%, respectively. Limitations on study/implications: Cow management was similar therefore no detailed differences between farms were studied. It is important to keep records of embryonic losses as they can have a significant impact on the farm. Findings/conclusions: There is a high variability of embryonic losses between the diagnosis of early pregnancy and the confirmation between the stables and this may be due to differences in management since the breed and environmental conditions were the same in the three stables.Objective: To determine embryonic losses between the early diagnosis and the confirmation of gestation in dairy cows from different barns for one year. Design/Methodology/Approach: A total of 3,413 confined Holstein cows from three different dairy barns in the Mexican Altiplano (highlands) were studied. Cows were milked three times a day with an average daily production of 36.5 ± 1.5 L. Gestation diagnosis was performed by ultrasonography at 34 ± 7 d after the artificial insemination, while gestation was confirmed at 60 ± 5 d. Average pregnancy loss was determined and embryonic losses were compared taking into account barn and month. Results: The overall average of embryonic losses was 18.8%. Neither the month factor nor the month × barn interaction affected the percentage of embryonic losses (p<0.05). Differences per barn (p<0.05) were observed and barns 1, 2, and 3 recorded losses percentages of 4 ± 1.6 %, 11.4 ± 1.6 %, and 22.9 ± 1.6%, respectively. Study Limitations/Implications: Detailed differences between barns were not studied, since cow management was similar in all three of them. Embryonic losses must be recorded, given their significant impact on the barn. Findings/Conclusions: There is a high variability among barns regarding embryonic losses between the early diagnosis and the confirmation of gestation. This situation may be the result of management differences, since the breed and environmental conditions were the same in all three barns

Multi-ancestry genome-wide study in >2.5 million individuals reveals heterogeneity in mechanistic pathways of type 2 diabetes and complications

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes. To characterise the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study (GWAS) data from 2,535,601 individuals (39.7% non-European ancestry), including 428,452 T2D cases. We identify 1,289 independent association signals at genome-wide significance (P&lt;5×10 - 8 ) that map to 611 loci, of which 145 loci are previously unreported. We define eight non-overlapping clusters of T2D signals characterised by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial, and enteroendocrine cells. We build cluster-specific partitioned genetic risk scores (GRS) in an additional 137,559 individuals of diverse ancestry, including 10,159 T2D cases, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned GRS are more strongly associated with coronary artery disease and end-stage diabetic nephropathy than an overall T2D GRS across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings demonstrate the value of integrating multi-ancestry GWAS with single-cell epigenomics to disentangle the aetiological heterogeneity driving the development and progression of T2D, which may offer a route to optimise global access to genetically-informed diabetes care. </p

Association Study with 77 SNPs Confirms the Robust Role for the rs10830963/G of MTNR1B Variant and Identifies Two Novel Associations in Gestational Diabetes Mellitus Development

CONTEXT: Genetic variation in human maternal DNA contributes to the susceptibility for development of gestational diabetes mellitus (GDM). OBJECTIVE: We assessed 77 maternal single nucleotide gene polymorphisms (SNPs) for associations with GDM or plasma glucose levels at OGTT in pregnancy. METHODS: 960 pregnant women (after dropouts 820: case/control: m99'WHO: 303/517, IADPSG: 287/533) were enrolled in two countries into this case-control study. After genomic DNA isolation the 820 samples were collected in a GDM biobank and assessed using KASP (LGC Genomics) genotyping assay. Logistic regression risk models were used to calculate ORs according to IADPSG/m'99WHO criteria based on standard OGTT values. RESULTS: The most important risk alleles associated with GDM were rs10830963/G of MTNR1B (OR = 1.84/1.64 [IADPSG/m'99WHO], p = 0.0007/0.006), rs7754840/C (OR = 1.51/NS, p = 0.016) of CDKAL1 and rs1799884/T (OR = 1.4/1.56, p = 0.04/0.006) of GCK. The rs13266634/T (SLC30A8, OR = 0.74/0.71, p = 0.05/0.02) and rs7578326/G (LOC646736/IRS1, OR = 0.62/0.60, p = 0.001/0.006) variants were associated with lower risk to develop GDM. Carrying a minor allele of rs10830963 (MTNR1B); rs7903146 (TCF7L2); rs1799884 (GCK) SNPs were associated with increased plasma glucose levels at routine OGTT. CONCLUSIONS: We confirmed the robust association of MTNR1B rs10830963/G variant with GDM binary and glycemic traits in this Caucasian case-control study. As novel associations we report the minor, G allele of the rs7578326 SNP in the LOC646736/IRS1 region as a significant and the rs13266634/T SNP (SLC30A8) as a suggestive protective variant against GDM development. Genetic susceptibility appears to be more preponderant in individuals who meet both the modified 99'WHO and the IADPSG GDM diagnostic criteria

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p

Analysis of protein-coding genetic variation in 60,706 humans

Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. We describe the aggregation and analysis of high-quality exome (protein-coding region) sequence data for 60,706 individuals of diverse ethnicities generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of truncating variants with 72% having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human “knockout” variants in protein-coding genes

Type 2 Diabetes Variants Disrupt Function of SLC16A11 through Two Distinct Mechanisms

Type 2 diabetes (T2D) affects Latinos at twice the rate seen in populations of European descent. We recently identified a risk haplotype spanning SLC16A11 that explains ∼20% of the increased T2D prevalence in Mexico. Here, through genetic fine-mapping, we define a set of tightly linked variants likely to contain the causal allele(s). We show that variants on the T2D-associated haplotype have two distinct effects: (1) decreasing SLC16A11 expression in liver and (2) disrupting a key interaction with basigin, thereby reducing cell-surface localization. Both independent mechanisms reduce SLC16A11 function and suggest SLC16A11 is the causal gene at this locus. To gain insight into how SLC16A11 disruption impacts T2D risk, we demonstrate that SLC16A11 is a proton-coupled monocarboxylate transporter and that genetic perturbation of SLC16A11 induces changes in fatty acid and lipid metabolism that are associated with increased T2D risk. Our findings suggest that increasing SLC16A11 function could be therapeutically beneficial for T2D. Video Abstract [Figure presented] Keywords: type 2 diabetes (T2D); genetics; disease mechanism; SLC16A11; MCT11; solute carrier (SLC); monocarboxylates; fatty acid metabolism; lipid metabolism; precision medicin

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology.

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

The trans-ancestral genomic architecture of glycemic traits

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10−8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Associations of autozygosity with a broad range of human phenotypes

In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (F-ROH) for >1.4 million individuals, we show that F-ROH is significantly associated (p <0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common variant homozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: F-ROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44-66%] in the odds of having children. Finally, the effects of F-ROH are confirmed within full-sibling pairs, where the variation in F-ROH is independent of all environmental confounding.Peer reviewe