Category Archives: Genetics

Genetics

AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes – Nature.com

This study comprised two primary analyses: a genetic-centric analysis (Analysis 1; detailed in Fig.1 and the Methods section) and a genetic-imaging integrative analysis (Analysis 2; detailed in Fig.2 and the Methods section). Data used in the two analyses are summarized (Supplementary TableS1). A total of 68,911 participants from the TWB were included in the analysis (Fig. S1).

The dataset containing information from 60,747 individuals after data quality control (QC) was divided into several subsets: (i) The genome-wide association study (GWAS) samples (Dataset 1, N=35,688), training samples (Dataset 2, N=12,236; Dataset 4, N=40,787), and validation samples (Dataset 3, N=3060; Dataset 5, N=10,197). For classification analysis, testing samples comprised Dataset 6 (N=8827) and Dataset 7 (N=936), while for prediction analysis, they were represented as Datasets 6 (N=8827) and Dataset 7 (N=936); B Sample size. Total sample size, along with the number of cases and the number of controls, are shown for each of the four phenotype definitions in Datasets 1 7; C Phenotype definition criteria. The definition and sample size for the four Type 2 Diabetes (T2D) phenotype definitions is shown. D Analysis flowchart. The analysis flow comprises three steps, starting with selecting T2D-associated single nucleotide polymorphisms (SNPs) and polygenic risk score (PRS), then selecting demographic and environmental covariates, and the best XGBoost model was established using the selected features. As to the first step, SNPs can be chosen from A our own GWAS with an adjustment for age, sex, and top ten principal components (PCs), B published studies based on single ethnic populations, and C published studies based on multiple ethnic populations. Source data are provided as a Source Data file.

Phenotype Definition IV was used as an example to illustrate the process. The data containing information from 7,786 individuals were divided into four subsets: a training dataset (N=4689), a validation dataset (N=1175), and two independent testing datasets (N=1469 for the first dataset and N=444 for the second independent dataset). Subsequently, the best XGBoost model was established. B Flowchart of PRS construction. The Polygenic Risk Score (PRS) was constructed using PRS-CSx, utilizing genome-wide association study (GWAS) summary statistics from the European (EUR), East Asian (EAS), and South Asian (SAS) populations obtained from the analysis of the DIAGRAM Project. Source data are provided as a Source Data file.

We evaluated the prediction performance under different scenarios hierarchically (the best scenario at a previous variable was given for a discussion of the next variable) in the following order: the sources and significance levels of T2D-associated SNPs (Fig.3A and Fig. S2), T2D phenotype definitions (Fig.3B), family history variable combinations (Fig.3C and Fig. S3), demographic variable combinations (Fig.3D), demographic and genetic variable combinations (Fig.3E), and SNP and PRS combinations (Figs.3F and 3G). The findings are summarized as follows: First, using T2D-associated SNPs from the previous large-sample-size GWAS11 as predictors had the highest AUC of 0.557, but its AUC was not significantly higher than that used the SNPs identified by our smaller-sample-size GWAS under different thresholds of statistical significance (Fig.3A), although our GWASs did identify some T2D-associated SNPs (Fig. S4). Second, the phenotype defined by self-reported T2D with HbA1C6.5% or fasting glucose 126mg/dL (i.e., T2D Definition IV) had the highest AUC of 0.640. Its AUC was significantly higher than the AUCs of the other three T2D definitions (Fig.3B). Third, sibs disease history had a significantly higher AUC of 0.732 than parents disease history with an AUC of 0.670 (p=0.009). Moreover, additive parent-and-sib disease history had the highest AUC of 0.758. Its AUC was significantly higher than parent-only (p<0.001) (Fig.3C). Fourth, a joint effect of age, sex, and additive parent-sib disease history had the highest AUC of 0.884. Its AUC was significantly higher than other demographic variable combinations, except for the combination of age and additive parent-sib disease history (Fig.3D). Fifth, whatever SNPs were included or not, demographic and PRS combinations outperformed the models without incorporation of PRS (Fig.3E), although genetic factors only improved up to 3% of AUC conditional on demographic characteristics (age, sex, and family history of T2D). Finally, given T2D-associated SNPs, AUC significantly increased if PRS was included (Fig.3F); T2D-associated SNPs provided a limited additional effect if PRS was already included (Fig.3G).

A bar chart displays AUC. The two-sided DeLong test examined the difference between AUCs. Bonferronis correction was applied to control for a family-wise error rate in multiple comparisons. Symbols *, **, and *** indicate p-values<0.05, 0.01, and 0.001, respectively. A SNP selection. Model predictors were SNPs selected from published studies or our GWAS under different p-value thresholds, where our GWAS association test is a two-sided Wald test for the slope coefficient in a logistic regression. The average AUCs of prediction models for four phenotype definitions were compared. B T2D Phenotype Definition. In addition to including the selected variables in Fig.3A, the AUCs of four phenotype definitions were compared. C Family history of T2D. In addition to including the selected variables in Fig.3A, B, the AUCs of the four types of T2D family history (i.e., (i): parents (binary factor), (ii) sibs (binary factor), (iii) either parents or sibs (binary factors), and (iv) both parents and sibs (ordinal factor)) were compared. D Demographic variables. In addition to including the selected variables in Fig.3AC, the AUCs of different combinations of demographic factors, including age, sex, and family history of T2D, are compared. E PRS and demographic variables. In addition to including the selected variables in Fig.3AD, the AUCs of different combinations of genetic variables, including SNPs, PRS-CS, and PRS-CSx, and demographic variables, including age, sex, and family history of T2D, are compared. F Impact of including PRS after SNPs. The AUCs of the models that consider SNPs, SNPs+PRS-CS, and SNPs+PRS-CSx as predictors are compared. G Impact of including additional SNPs after PRS. The additional 137 SNPs were collected from published studies (Supplemental Text2). The AUCs of the models that consider additional SNPs given PRS in the model are compared. Source data are provided as a Source Data file.

Among different prediction models, the model with predictors PRS-CSx, age, sex, and family history of T2D had the highest AUC 0.915 (Fig.4A) for Type VI definition of T2D based on the first testing dataset (i.e., Dataset 6 in Fig.1). The optimal threshold, determined by the Youden index, for the fitted value that used to predict T2D or non-T2D in the XGboost model was 0.16. The Accuracy, Sensitivity, Specificity, and F1 indices were 0.843, 0.844, 0.843, and 0.672, respectively. Furthermore, the model was tested in the second independent testing dataset (i.e., Dataset 7 in Fig.1), and a promising result similar to the first testing dataset was found: AUC=0.905, Accuracy = 0.843, Sensitivity = 0.846, Specificity = 0.842, and F1=0.644. AUCs are also provided for the other three T2D definitions (Fig. S5).

A AUCs of all models based on Phenotype Definition IV. A heatmap summarizes the AUCs of all models based on Phenotype Definition IV (i.e., T2D was defined by self-reported T2D, HbA1c, and fasting glucose). The genetic variables are shown on the X-axis, and the demographic variables are shown on the Y-axis. B Positive correlation between PRS and T2D odds ratio. In each decile of PRS based on PRS-CSx, the odds ratio of T2D risk and its 95% confidence interval were calculated based on an unadjusted model (blue line) and an adjusted model considering age, sex, and T2D family history (red line). The reference group was the PRS group in the 4060% decile. The horizontal bars are presented as the odds ratio estimates (square symbol) +/ its 95% confidence intervals (left and right ends) at a PRS decile. C High-risk group. In the chart, the figures from the inner to the outer represent (i) the case-to-control ratio, (ii) the number of cases, and (iii) the number of controls in the PRS decile subgroups. D Association of age, sex, T2D family history, and PRS with T2D. In the univariate analysis, the p-values for age, sex, family history, and PRS were 4.17 1020, 7.08 107, 9.41 1013, and 2.06 1013, respectively. In the multivariate analysis, the p-values for age, sex, family history, and PRS were 2.00 1016, 5.56 105, 1.43 1010, and 5.49 1013, respectively. E Risk factors for T2D. Kaplan-Meier curves reveal that Age (older individuals), sex (males), T2D family history (the larger number of parents and siblings who had T2D), and PRS (high decile PRS group) are risk factors (high-risk level) for T2D risk. F Median event time of T2D. Examples of the median event time for developing T2D are provided based on a multivariate Cox regression model, both without and with incorporating PRS. NA indicates not assessable. Source data are provided as a Source Data file.

The importance of each predictor was evaluated through a backward elimination procedure of variables. The optimal model incorporating age, sex, family history of T2D, and PRS achieved an AUC of 0.915. The AUC reductions upon removing individual variables are as follows: (a) Omitting the age variable resulted in an AUC of 0.839, representing a reduction of 0.076. (b) Excluding the sex variable resulted in an AUC of 0.905, with a decrease of 0.01. (c) Removing the family history of the T2D variable yielded an AUC of 0.881, with a reduction of 0.034. (d) Eliminating the PRS variable resulted in an AUC of 0.884, decreasing to 0.031. Based on the decrease in AUC, the impact size appears to be in the order of age > family history > PRS > sex. Additionally, we evaluated feature importance (see the Methods section), and the order of feature importance is family history > age > PRS > sex. Our findings consistently highlight age and family history as the most crucial risk factors for T2D.

Family history encompasses genetics and environment. We delved into the connection between the family history of T2D treated as a graded scale (0, 1, 2, 3, and 4) and the genetic component represented by the PRS. Through ordinal logistic regression, we observed a beta coefficient of 0.808 and an associated odds ratio (OR) of 2.24 (p=1.65 10296). The remarkably small p-value emphasizes the robust statistical significance, signaling a substantial association between the PRS and familial T2D status. For each incremental unit rise in an individuals PRS, their odds of belonging to a higher family history category for T2D increase by 2.24 times. This implies a tangible shift in the likelihood of different family history classifications as the PRS changes. The findings underscore a strong statistical link between genetic predisposition, as captured by the PRS, and the gradation of family history of T2D.

Furthermore, we calculated the Population Attributable Risk (PAR) by dichotomizing PRS into a high-risk group (PRS tercile >80%) and a non-high-risk group (PRS tercile <80%). Among the 59,811 participants, the breakdown was as follows: high PRS with family history (N=5473), high PRS without family history (N=6489), non-high PRS with family history (N=16,054), and non-high PRS without family history (N=31,795). The PAR estimate was 10.17%, indicating that 10.17% of the family history of T2D is attributed to genetic heritability. If considering a broader definition of the high-risk group (PRS tercile >60%) and non-high-risk group (PRS tercile <60%), the PAR estimate increased to 18.41%.

Further consideration of environmental factors, including education level, drinking experience, exercise habits, the number of exercise types, and SNP-SNP interactions with and without SNPs main effect, did not improve T2D prediction (Supplementary TableS2). Considering model parsimony, the final model did not include these environmental factors and SNP-SNP interactions. In addition to prediction models, classification models were also established. The AUCs in classification models (Fig. S6) were generally slightly higher than those in prediction models (Fig. S5).

The positive association between PRS and T2D risk is shown (Fig.4B). Compared to the participants in the 4060% PRS decile group, those in the top 10% decile group had a 4.738-fold risk of developing T2D (95% confidence interval: 3.1477.132, p<0.001) and a 4.660-fold risk (95% confidence interval: 2.6828.097, p<0.001) after adjusting for age, sex, and family history. In addition, we performed a stratified analysis across various combinations of age subgroups, sex subgroups, and family history subgroups to identify high-risk subgroups, where age was stratified into four subgroups based on quartiles: 025%, 2550%, 5075%, and 75100%, corresponding to age subgroups of 43, 4352, 5259, and >59 years of age, respectively (Fig. S7). We identified a high-risk subgroup of women who were older than 59 and had a family history of T2D. The ratio of case vs. control sample size was as high as 7.313.0-fold in the 80100% decile groups (Fig.4C). The ratio was much higher than a 1.6-fold that did not consider PRS (i.e., PRS at 0100%) (Fig.4C). Due to ambiguity or instability in the evidence for other combinations, we chose not to report them.

Among 8347 non-T2D participants at baseline in the first testing dataset of 8827 participants, 220 reported T2D in the follow-up. The Cox regression analyses considered two types of time scales and three types of sex variable treatment and obtained a consistent result (Supplementary TableS3). Using the analysis in which we considered time-on-study as the time-scale with age at baseline, sex, family history of T2D, and PRS as covariates for illustration, age, sex, family history of T2D, and PRS were all significantly associated with T2D (p<0.001) (Fig.4D). Increased age, higher PRS, and stronger T2D family history had a higher T2D risk. The elderly male, with a strong family history and high PRS, had a severe T2D risk (Fig.4E for multivariate Cox regression and Fig. S8 for univariate Cox regression). We also provided the predicted time-to-event (week) (Fig.4F). For example, a 50-year-old man with one of his family members had T2D will achieve median T2D-free time after 460 weeks (95% CI, 384NA). The median time to develop T2D was shortened to 419 weeks (95% CI, 384NA) after considering a standardized PRS of 0.66 (equivalent to a PRS risk subgroup in the top 25% of the population).

A linear regression analysis was performed to assess the impact of exercise on HbA1c. Multiple testing for 110 analyses was corrected using Bonferroni correction, and the significance level was set as 4.5 10-4. It was observed that individuals engaging in regular exercise experienced a significant reduction in HbA1c by an average of 0.09% mg/dL (p<0.001) compared to those who did not engage in regular exercise. Moreover, individuals with a high PRS who engaged in exercise demonstrated a greater reduction in HbA1c (0.13% mg/dL) than those with a low PRS (0.08% mg/dL). The results also suggested that the T2D patients who regularly engaged in exercise can have a noteworthy improvement of 0.32% mg/dL in HbA1c than those T2D patients who did not exercise regularly. In addition, among the various types of exercise, walking for fitness exhibited the most robust reduction in HbA1c for all samples, including high and low-risk subgroups and both T2D and non-T2D groups (Fig. S9). On average, participants engaged in walking for fitness 18.30 times a month (standard deviation = 8.64) for approximately 48.13minutes per session (standard deviation = 22.92).

To investigate the early detection capability of our model for T2D, we performed an analysis focusing on 550 women participants older than 59 years, all of whom had a family history of T2D. We identified them as at high risk if they possessed a high PRS, even though they were initially reported as non-T2D at baseline. Thirty-six were changed to T2D, and 514 were still non-T2D at follow-up. We predicted their T2D status. G1 G4 are the groups of participants in true positive, false negative, false positive, and true negative, respectively (Fig.5A). We evaluated that G3 was indeed misclassified by our prediction model or our prediction had corrected the problem in the self-reported T2D by further investigating: (1) their follow-up time and current risk in the Cox regression model; (2) HbA1c and fasting glucose; (3) the accuracy of self-reported disease status.

A Four subgroups (N=550). B Survival rate (N=550). C Median survival time (N=550). P-values of G1 vs. G3, G2 vs. G3, and G4 vs. G3 were 0.092, 0.0014 (**), and 2.22 1016 (***), respectively. D Follow-up time (N=550). P-values of G1 vs. G3, G2 vs. G3, and G4 vs. G3 were 0.056, 0.32, and 0.14, respectively. E T2D risk (N=550). P-values of G1 vs. G3, G2 vs. G3, and G4 vs. G3 were 0.018 (*), 0.073, and 0.0039 (**), respectively. F HbA1c (N=550). P-values of G1 vs. G3, G2 vs. G3, and G4 vs. G3 were 2.21 1014, 0.0039, and 3.00 105; respectively; in the follow-up, p-values of G1 vs. G3, G2 vs. G3, and G4 vs. G3 were 1.50 10-13, 6.01 104, and 4.55 10-6, respectively. G Fasting glucose (N=550). In the baseline, p-values of G1 vs. G3, G2 vs. G3, and G4 vs. G3 were 2.06 10-12, 6.66 104, and 1.63 102; respectively; in the follow-up, p-values of G1 vs. G3, G2 vs. G3, and G4 vs. G3 were 8.30 108, 1.38 103, and 1.84 102, respectively. H Phenotype definition in G3 (N=395). Many individuals in G3 cannot satisfy the T2D Phenotype Definition IV. In Fig.5CG, two-sided Wilcoxon rank-sum tests were applied to compare group differences. The box plots center lines indicate the medians, the lower and upper boundaries of the boxes represent the first and third quartiles, and the whiskers extend to cover a range of 1.5 interquartile distances from the edges. The violin plots upper and lower bounds depict the minimum and maximum values. Source data are provided as a Source Data file.

The Kaplan-Meier curve for each subgroup is depicted (Fig.5B). The distributions of median survival time for each subgroup are illustrated (Fig.5C). The distributions of the time period from baseline to follow-up for each subgroup are presented (Fig.5D). The distributions of Type 2 diabetes (T2D) risk at follow-up for each subgroup are shown (Fig.5E). The distributions of HbA1c levels at baseline and follow-up for each subgroup are displayed (Fig.5F). The distributions of fasting glucose levels at baseline and follow-up are demonstrated (Fig.5G).

First, compared to G4 (true negative), G3 had a significantly lower T2D-free probability (Fig.5B), shorter median survival time (Fig.5C), higher T2D-risk under similar follow-up time (Fig.5D and 5E), higher HbA1c (Fig.5F), and higher fasting glucose (Fig.5G). Second, compared to G1 (true positive), G3 had a comparable survival rate (Fig.5B), median survival time (Fig.5C), and T2D-risk under similar follow-up time (Fig.5D and5E) but lower HbA1c (Fig.5F) and fasting glucose (Fig.5G). We didnt compare G2 and G3 because of the small sample size in G2. Finally, among the 395 participants in G3, 80.76% of them were removed from our previous analysis because their baseline HbA1c and fasting glucose violated the criteria for the phenotype definition (Fig.1C); 339 participants were removed because of their follow-up HbA1c and fasting glucose violated the formal T2D criteria; only 34 self-reported non-T2D were really non-T2D participants who had HbA1C<6.5% and fasting glucose <126mg/dL (Fig.5H). Overall, the results consistently indicate that G3 represents individuals in a pre-T2D stage, which our model can detect early.

The model that combined four types of image features performed best. Moreover, the model based on BMD image features exhibited a higher AUC, accuracy, specificity, and F1 than the models based on any other three types of images (Fig.6A). The models based on image features had an AUC of 0.898, higher than the ones of genetic information (AUC=0.677) and demographic factors (AUC=0.843). Integrating image features, genetic information, and demographic factors increased AUC to 0.949 in the first testing data (Fig.6B); the results for each of the four images are also provided (Fig. S10). The accuracy, sensitivity, specificity, and F1 of the model in the first testing data were 0.871, 0.878, 0.870, and 0.663, respectively, based on a classification threshold of 0.03. The model also performed reasonably well in the second testing dataset with AUC=0.929, Accuracy = 0.854, Sensitivity = 0.789, Specificity = 0.862, and F1=0.558. The results of a prediction model using tuned parameters are also provided (Supplementary TableS4). As no significant improvement was observed, this paper discusses the default model. According to the estimated feature importance in the best XGBoost model, all genetic factors (PRS), four types of medical images, and demographic variables provided informative features for risk assessment, such as PRS (genetics), family history and age (demographic factors), fatty liver (ABD images), end-diastolic velocity in the right common carotid artery (CAU images), RR interval (ECG images), and spine thickness (BMD images). Of the 152 medical imaging features, 125 were selected in the final model. (Fig.6C).

A Performance comparison of medical imaging data analysis. The area under the receiver operating characteristic (ROC) curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPEC), and F1 score are compared for the integrative analysis of four types of medical images (All) and individual medical image analyses, including BMD, ECG, CAU, and ABD. B The model that combines four types of medical imaging, PRS, and demographic variables shows the highest AUC of 0.949. ROC plots and the corresponding AUC for the models considering medical image features (I), genetic PRS (G), and demographic variables, including age, sex, T2D family history (D), and their combinations. C An optimal model combining medical imaging, PRS, and demographic variables. The best models top 20 features with a high feature impact include the medical image, genetic, and demographic features. D Positive correlation between MRS and T2D odds ratio. In each decile of MRS based on four types of medical images, the odds ratio of T2D risk and its 95% confidence interval were calculated based on an unadjusted model (blue line) and an adjusted model considering age, sex, and T2D family history (red line), with the MRS group in the 4060% decile serving as the reference group. The horizontal bars are presented as the odds ratio estimates (square symbol) +/ its 95% confidence intervals (left and right ends). E High-risk group. The figures from the inner to the outer in the chart display (i) the case-to-control ratio, (ii) the number of cases, and (iii) the number of controls in the MRS decile subgroups. F Input page of the online T2D prediction website. Personal information, including age, sex, family history of T2D, PRS, and MRS, is input to calculate T2D risk. PRS and MRS are optional, and a reference distribution is provided. G Output page of the online T2D prediction website. Source data are provided as a Source Data file.

To address the challenges of practical clinical implementation in the best XGBoost model, we have proposed an alternative model that requires a limited number of features. We systematically calculated each features incremental area under the AUC by sequentially including those with the highest feature importance. We selected the top features showing a positive AUC increment. The analysis revealed that a sub-model incorporating only the following eight crucial variables: family history (from the questionnaire), age (from the questionnaire), fatty liver (from ABD images), spine thickness (from BMD images), PRS (from genetic data), end-diastolic velocity in the right common carotid artery (R_CCA_EDV) (from CAU images), RR interval (from ECG images), and end-diastolic velocity in the left common carotid artery (L_CCA_EDV) (from CAU images), maintains a commendable AUC of 0.939 (Fig. S11). This streamlined model significantly reduces the number of risk predictors while preserving high prediction accuracy, demonstrating promising potential for practical application in clinical settings. Moreover, the reduced number of risk predictors in the streamlined model alleviates concerns about model overfitting.

Each participants multi-image risk score (MRS) was calculated as the likelihood of being predicted as a T2D case using XGBoost, which analyzed the medical imaging features for T2D prediction. The odds ratio and its confidence interval for the association between MRS and T2D are shown by percentiles of MRS (Fig.6D). Compared to the participants in the 4060% MRS decile group, the risk of T2D increased with MRS. Of importance, we further identified that, for the men older than 54 years old with a family history of T2D, the case vs. control ratio of sample size was 9.3 in the 90100% MRS decile group, much higher than 1.3, which MRS was not considered (Fig.6E).

We have established a website where users can calculate their T2D risk online. To obtain the risk assessment, users are required to provide age, sex, and family history of T2D, and they can optionally provide PRS and MRS (Fig.6F). PRS and MRS can be entered manually or uploaded as a file (Supplemental Text1). Additionally, we have provided PRS and MRS risk percentages based on the study population as a reference. The online risk assessment offers information, including the risk of developing T2D over 3, 5, and 7 years, T2D-free probability, and T2D risk with and without considering PRS (Fig.6G). The assessment takes into account both PRS and MRS (Fig.6G). For example, consider a 50-year-old male with a family history of T2D and PRS 1.5 and MRS 1.5. Without considering PRS, the risk (probability) of developing T2D after a 7-year follow-up is 0.23. However, when PRS is considered, the risk increases to 0.37. Furthermore, considering MRS further increases the risk to 0.81. The online tool provides these valuable insights to users based on their input data.

Link:
AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes - Nature.com

Genetics

World-first AI algorithm developed at CHEO leads to rare disease diagnosis for families – CHEO

Harnessing the power of artificial intelligence (AI), CHEO researchers have developed a groundbreaking search algorithm that identifies children and youth who may have an undiagnosed rare genetic disease and refers them for genetic testing putting an end to their diagnostic odyssey.

The ThinkRare algorithm is incredibly exciting and promising because it means we can help families find answers and get the care and support they need sooner, said Dr. Kym Boycott, Senior Scientist at the CHEO Research Institute and Chief of Genetics at CHEO. This algorithm is a game changer. Using AI to scour CHEOs electronic health record based on set criteria, ThinkRare can accurately identify kids who may have an undiagnosed rare genetic disease and refer them to our clinic something that may have never happened without it.

Ten-year-old Antony Wistaff and hisfamily have spentcountlesshours at CHEO, callingit a second home. Antony wasbornprematurely in October 2013 and a few dayslaterunderwent emergency surgery at CHEO to place a shunt for hydrocephalus. But thatwasonly the beginning of whatwouldbecome a decade-long diagnostic journeyconsisting of more than 100 outpatientappointmentsacross six differentspecialtyclinics at CHEO, and 30 trips to the emergency department for variousreasons.

That was until recently, when the ThinkRare algorithm identified Antony as potentially having an undiagnosed rare genetic disease and flagged him for a referral to receive genome-wide sequencing testing a test that simultaneously analyzes the more than 5,000 genes that have been associated with rare disease and is now available clinically in Ontario.

The results of the genetictestingdiscoveredthat Antony has Chung-Jansen Syndrome a rare disorderresultingfrom a pathogenic variant in the PHIP gene. At present, the syndrome has been diagnosed in only about 400 people worldwide and itexplainedmany of Antonyshealth and behavioural challenges, includinghisdevelopmentaldelays, learningdifficulties, and large head size.

When we found out that Antony was diagnosed with Chung-Jansen Syndrome, it answered so many questions for our family, said Georges Wistaff, Antonys dad. This research brought a kind of peace to our house. Hadweknownthissooner, itwould have meantlessquestioning as parents, less stress, and more support becausewewould have had a cleardiagnosis for Antony. A little bit of blood and a simple test, answeredsomany questions.

To date, Think Rare, whichiscurrently operating as a researchprojectapproved by the CHEO ResearchEthicsBoard, isthree for three meaning the first three patients identified by ThinkRare and referred to genetics have received test results and been diagnosedwith a rare disease. Genetictestingisunderway for manyotherfamiliesidentified by ThinkRare.

Our goal is to flip the diagnostic care journey on itshead and start withgenetictestingearlier on the care pathway. By incorporating the ThinkRarealgorithmintoclinical care, wewillbe able to support CHEO clinicians and frontlineworkerswith the power of machine learning to find the needle in the haystack, added Dr. Boycott, whois a Tier 1 Canada Research Chair in Rare DiseasePrecisionHealth and Professor of Pediatrics at the University of Ottawa.

Work iscurrentlyunderway at CHEO to transition the ThinkRareprojectfrom researchintoclinical practice, with all the necessary patient privacymechanisms in place.

CHEO isuniquelypositioned to develop an impactfulalgorithmsuch as ThinkRarebecause of CHEOsinvestment in a robustelectronichealth record system, ourcommitment to innovation, our close collaboration betweenclinical and researchteams, and becausewe are the only pediatric healthcare centre in Eastern Ontario serving a widegeographic area. At CHEO, we have broughttogether all the necessaryelementswhenitcomes to making AI advancements in healthcare, said Dr. Jason Berman, CEO and Scientific Director, CHEO Research Institute, and Vice-PresidentResearch, CHEO.

The ThinkRareprojectwas made possible withfundingfrom the CHEO Foundation, the CHAMO Innovation Fund, and Ontario Genomics.

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Media contact:

Jennifer Ruff Director of Communications CHEO Research Institute (613) 261-3979 jruff@cheo.on.ca

About the CHEO Research Institute

The CHEO Research Institute is a global centre of excellence in pediatric research that connects talent and technology in pursuit of life-changing research for every child, youth and family in the CHEO community and beyond. The CHEO Research Institute coordinates the researchactivities of CHEO and isaffiliatedwith the University of Ottawa. At the CHEO Research Institute, discoveries inspire the best life for everychild and youth. For more information, visitcheoresearch.ca.

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World-first AI algorithm developed at CHEO leads to rare disease diagnosis for families - CHEO

Genetics

Enhancing Chickpea Crop Improvement With Wild Chickpea Genes – Technology Networks

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The studyCicer super-pangenome provides insights into species evolution and agronomic trait loci for crop improvement in chickpea,published in Nature Genetics, provides insights into the evolutionary history and divergence time of the Cicer genus, sequencing the genomes of eight wild Cicer species and comparing them with two cultivated chickpea varieties.

The study also constructs a graph-based super-pangenome that can help identify and transfer valuable genes from wild species to cultivated ones.

Director of Murdoch UniversitysCentre for Crop and Food InnovationProfessor Rajeev Varshney, who coined the term super-pangenome in 2019 inTrends in Plant Science, said the findings in the new study could accelerate crop improvement globally.

Subscribe to Technology Networks daily newsletter, delivering breaking science news straight to your inbox every day.

The Cicer super-pangenome offers a powerful way to study chickpea genes to perform association analyses and determine the most important traits for our farming industry.

Our study found that the wild species have more genetic diversity and variations that could be useful for improving chickpea traits such as disease resistance, flowering time, and stress tolerance.

Traditional and modern breeding efforts have improved chickpea productivity, but more exhaustive steps have been needed to meet the growing worldwide demand.

Chickpeas are highly nutritious, economically significant and important contributors to soil fertility, fixing atmospheric nitrogen but chickpea production currently faces several biotic and abiotic constraints.

They are widely grown, with an annual global production exceeding 17 million tonnes.

In the context of Australia, chickpea production reached more than 2 million tonnes in 2017, but at present it is only 500,000 tonnes, so there is huge scope for enhancing local production to contribute to both environmental sustainability and growers profitability.

Reference:Khan AW, Garg V, Sun S, et al. Cicer super-pangenome provides insights into species evolution and agronomic trait loci for crop improvement in chickpea. Nature Genetics. 2024. doi:10.1038/s41588-024-01760-4

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Enhancing Chickpea Crop Improvement With Wild Chickpea Genes - Technology Networks

Genetics

What links our Siberian ancestors to a heightened risk of developing multiple sclerosis? – The Conversation

The genetic predisposition to suffering from multiple sclerosis (MS) is closely linked to historical migration patterns, and to our ancestors lifestyles. Specifically, MS is linked to the genetic contributions made by nomadic populations who reached Western Europe 5000 years ago from the Siberian Steppe.

Human genomes vary by 0.1%, and this difference is often the result of responses to environmental pressures. When faced with epidemic diseases, for instance, natural selection means the genetic variants that provide individual resistance to pathogens are the ones that survive. These markers are, in terms of population genetics, positively selected.

However, variants that are beneficial in one situation can be counterproductive in another. Immune systems the first line of defence against harmful viruses and bacteria tend to be involved in such a mismatch between genetics and environment.

In some cases, immune systems are unable to distinguish between the bodys own cells and external ones, leading them to destroy tissues. This is what causes autoimmune diseases such as rheumatoid arthritis, lupus or MS.

In the case of MS, the immune system attacks the insulating covers of nerve fibres in the brain and spinal cord. Until recently, the cause of MS was unknown, as were the causes of different distributions among the worlds population. However, new hypotheses have been proposed that may shine a light on these mysteries.

Two sources of information can help us understand when, where and how MS originated. The first is the diseases prevalence across continental Europe: there are higher concentrations of MS in the north and lower ones in the south. The second comes from palaeogenomics, the study of DNA recovered from ancient remains. Research in this field suggests that the European gene pool is made up of three major lineages.

Read more: Early humans reached northwest Europe 45,000 years ago, new research shows

The base of the European genome is linked to the first European settlers: hunter gatherers who arrived in Western Europe around 45,000 years ago. Later, between 8000 and 6000 years ago, Neolithic populations from Anatolia migrated into the region, mixing with the hunter gatherer genome. These groups were linked to the domestication of plants and animals.

To these two previous genomes we can add the Yamnaya migration. The Yamnaya was made up of pastoralist groups from the Siberian Pontic Steppes who dispersed across Eurasia in the Bronze Age around 5000 years ago.

Europeans are therefore a complex mixture in varying proportions of these three gene pools.

A recent study based on analysis of ancient DNA has found a direct relationship between the genetic risk of developing MS and an individuals proportion of Yamnaya ancestry. Like the incidence of MS, the Yamnaya genome is more prevalent in northern than southern Europe.

The contribution of these nomadic pastoralists to the European cultural and genetic ancestry had been overlooked by archaeologists until palaeogeneticists detected traces of them in Bronze Age populations.

They were hierarchical, patrilineal and patriarchal groups who introduced, among other innovations, the domestication of horses, the use of carriages, and Indo-European languages to Europe.

Read more: Indo-European languages: new study reconciles two dominant hypotheses about their origin

Their arrival in Western Europe also brought about the contribution of new genetic variants that had been selected to suit lifestyles based on pastoralism and animal husbandry.

Coexistence with cattle meant access to milk, an exceptional dietary source of energy. This in turn led to the selection of genetic variants that allowed adults to properly digest lactose.

Another interesting finding is the presence of certain pathogens such as the bacteria Yersina pestis, which causes the plague among the remains recovered from Siberian pastoral groups.

We can therefore explain the relationship between Yamnaya ancestry and MS, as contact with the pathogens carried by livestock caused the Yamnayas immune systems to adapt. They became hypersensitive to infections, which sometimes led their immune systems to confuse their own cells with those of others, resulting in the development of autoimmune diseases.

It is perhaps surprising to learn that some characteristics of modern day humans such as the ability to digest lactose as adults, resistance to infectious diseases, or the development of autoimmune diseases are inherited from a remote past that developed in the Pontic steppes. This discovery also has potential benefits in the field of medicine, such as in allocating healthcare resources to regions with a higher genetic predisposition to developing MS. This practical application would, however, require further, more focused research.

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What links our Siberian ancestors to a heightened risk of developing multiple sclerosis? - The Conversation

Genetics

Scientists Unveil the Genetic Blueprint of Blood Pressure – SciTechDaily

In a comprehensive study published in Nature Genetics, researchers identified over 100 new genomic loci that influence blood pressure, using data from more than a million participants. The findings, which also relate to iron metabolism and adrenergic receptors, could lead to novel treatments for hypertension. Credit: SciTechDaily.com

Over 100 new genomic regions linked to blood pressure were discovered, offering insights into iron metabolism and potential new drug targets for treating hypertension.

NIH-led study finds genetic markers that explain up to 12% of the differences between two peoples blood pressure.

National Institutes of Health (NIH) researchers and collaborators have discovered over 100 new regions of the human genome, also known as genomic loci, that appear to influence a persons blood pressure. Results of the study also point to several specific genomic loci that may be relevant to iron metabolism and a type of cellular receptor known as adrenergic receptors.

The study, published recently in the journal Nature Genetics, is one of the largest such genomic studies of blood pressure to date, including data from over 1 million participants and laying the groundwork for researchers to better understand how blood pressure is regulated. Such insights could point to potential new drug targets.

NIH-led study finds genetic markers that explain up to 12% of the differences between two peoples blood pressure. Credit: Darryl Leja, National Human Genome Research Institute

Our study helps explain a much larger proportion of the differences between two peoples blood pressure than was previously known, said Jacob Keaton, Ph.D., staff scientist in the Precision Health Informatics Section within the National Human Genome Research Institutes (NHGRI) Intramural Research Program and first author of the study. Our study found additional genomic locations that together explain a much larger part of the genetic differences in peoples blood pressure. Knowing a persons risk for developing hypertension could lead to tailored treatments, which are more likely to be effective.

To understand the genetics of blood pressure, the researchers combined four large datasets from genome-wide association studies of blood pressure and hypertension. After analyzing the data, they found over 2,000 genomic loci linked to blood pressure, including 113 new regions. Among the newly discovered genomic loci, several reside in genes that play a role in iron metabolism, confirming previous reports that high levels of accumulated iron can contribute to cardiovascular disease.

The researchers also confirmed the association between variants in the ADRA1A gene and blood pressure. ADRA1A encodes a type of cell receptor, called an adrenergic receptor, that is currently a target for blood pressure medication, suggesting that other genomic variants discovered in the study may also have the potential to be drug targets to alter blood pressure.

This study shows that these big genome-wide association studies have clinical relevance for finding new drug targets and are needed to discover more drug targets as we go forward, said Dr. Keaton.

From these analyses, the researchers were able to calculate a polygenic risk score, which combines the effects of all genomic variants together to predict blood pressure and risk for hypertension. These risk scores consider which genomic variants confer risk for hypertension and reveal clinically meaningful differences between peoples blood pressure.

Polygenic risk scores have potential to serve as a useful tool in precision medicine, but more diverse genomic data is needed for them to be applicable broadly in routine health care. While the collected data was mostly from people of European ancestry (due to limited availability of diverse datasets when the study was started), the researchers found that the polygenic risk scores were also applicable to people of African ancestry, which was confirmed through analyzing data from NIHs All of Us Research Program, a nationwide effort to build one of the largest biomedical data resources and accelerate research to improve human health.

Nearly half of adults in the United States have high blood pressure, known as hypertension. High blood pressure often runs in families, meaning that there is a genetic component to developing the condition in addition to environmental contributions such as a high-salt diet, lack of exercise, smoking, and stress. When blood pressure is consistently too high, it can damage the heart and blood vessels throughout the body, increasing a persons risk for heart disease, kidney disease, stroke, and other conditions.

For more on this research, see 2,000 Genetic Signals Linked to Blood Pressure Discovered in Study of Over a Million People.

Reference: Genome-wide analysis in over 1 million individuals of European ancestry yields improved polygenic risk scores for blood pressure traits by Jacob M. Keaton, Zoha Kamali, Tian Xie, Ahmad Vaez, Ariel Williams, Slavina B. Goleva, Alireza Ani, Evangelos Evangelou, Jacklyn N. Hellwege, Loic Yengo, William J. Young, Matthew Traylor, Ayush Giri, Zhili Zheng, Jian Zeng, Daniel I. Chasman, Andrew P. Morris, Mark J. Caulfield, Shih-Jen Hwang, Jaspal S. Kooner, David Conen, John R. Attia, Alanna C. Morrison, Ruth J. F. Loos, Kati Kristiansson, Reinhold Schmidt, Andrew A. Hicks, Peter P. Pramstaller, Christopher P. Nelson, Nilesh J. Samani, Lorenz Risch, Ulf Gyllensten, Olle Melander, Harriette Riese, James F. Wilson, Harry Campbell, Stephen S. Rich, Bruce M. Psaty, Yingchang Lu, Jerome I. Rotter, Xiuqing Guo, Kenneth M. Rice, Peter Vollenweider, Johan Sundstrm, Claudia Langenberg, Martin D. Tobin, Vilmantas Giedraitis, Jianan Luan, Jaakko Tuomilehto, Zoltan Kutalik, Samuli Ripatti, Veikko Salomaa, Giorgia Girotto, Stella Trompet, J. Wouter Jukema, Pim van der Harst, Paul M. Ridker, Franco Giulianini, Veronique Vitart, Anuj Goel, Hugh Watkins, Sarah E. Harris, Ian J. Deary, Peter J. van der Most, Albertine J. Oldehinkel, Bernard D. Keavney, Caroline Hayward, Archie Campbell, Michael Boehnke, Laura J. Scott, Thibaud Boutin, Chrysovalanto Mamasoula, Marjo-Riitta Jrvelin, Annette Peters, Christian Gieger, Edward G. Lakatta, Francesco Cucca, Jennie Hui, Paul Knekt, Stefan Enroth, Martin H. De Borst, Ozren Polaek, Maria Pina Concas, Eulalia Catamo, Massimiliano Cocca, Ruifang Li-Gao, Edith Hofer, Helena Schmidt, Beatrice Spedicati, Melanie Waldenberger, David P. Strachan, Maris Laan, Alexander Teumer, Marcus Drr, Vilmundur Gudnason, James P. Cook, Daniela Ruggiero, Ivana Kolcic, Eric Boerwinkle, Michela Traglia, Terho Lehtimki, Olli T. Raitakari, Andrew D. Johnson, Christopher Newton-Cheh, Morris J. Brown, Anna F. Dominiczak, Peter J. Sever, Neil Poulter, John C. Chambers, Roberto Elosua, David Siscovick, Tnu Esko, Andres Metspalu, Rona J. Strawbridge, Markku Laakso, Anders Hamsten, Jouke-Jan Hottenga, Eco de Geus, Andrew D. Morris, Colin N. A. Palmer, Ilja M. Nolte, Yuri Milaneschi, Jonathan Marten, Alan Wright, Eleftheria Zeggini, Joanna M. M. Howson, Christopher J. ODonnell, Tim Spector, Mike A. Nalls, Eleanor M. Simonsick, Yongmei Liu, Cornelia M. van Duijn, Adam S. Butterworth, John N. Danesh, Cristina Menni, Nicholas J. Wareham, Kay-Tee Khaw, Yan V. Sun, Peter W. F. Wilson, Kelly Cho, Peter M. Visscher, Joshua C. Denny, Million Veteran Program, Lifelines Cohort Study, CHARGE consortium, ICBP Consortium, Daniel Levy, Todd L. Edwards, Patricia B. Munroe, Harold Snieder and Helen R. Warren, 30 April 2024, Nature Genetics. DOI: 10.1038/s41588-024-01714-w

The project was led by researchers at NHGRI in collaboration with Queen Mary University of London, Vanderbilt University Medical Center, Nashville, Tennessee, the University of Groningen in the Netherlands and other institutions, as part of the International Consortium of Blood Pressure. Over 140 investigators from more than 100 universities, institutes and government agencies contributed to this international study.

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Scientists Unveil the Genetic Blueprint of Blood Pressure - SciTechDaily

Genetics

Analysis | An NIH genetics study targets a long-standing challenge: Diversity – The Washington Post

Good morning. Im Lauren Sausser, a KFF Health News reporter based in Charleston, S.C., where my allergy shots seemed to stop working around mid-February. I cover health-care news across the South, often issues related to health equity. Send story ideas to lsausser@kff.org.

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Todays edition: Medicare is headed for insolvency, though the economy has bought it some extra time, according to a new report. The Biden administration says a public education campaign on vaccination saved tens of thousands of lives. But first

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Analysis | An NIH genetics study targets a long-standing challenge: Diversity - The Washington Post

Genetics

NSF grant to fund research on genetics and physiology of corn kernel development – Penn State University

Chopra suggested that studys findings may help provide the genetic and epigenetic basis of metabolic processes that are required for the development of a healthy corn kernel, which could inform increased quality and production in the future. Epigenetics refer to when genes functions are modified without any change in their DNA sequences, Chopra said, creating silent genes whose traits are not expressed in the organism. A better understanding of what induces gene silencing, and how they impact the organism, could help inform methods to create stress-resilient crops, according to the researchers.

The project will use cutting-edge instruments and expertise available through the Core Facilities of the Penn State Huck Institutes of the Life Sciences, including collaboration with Neela Yennawar, director of the X-Ray Crystallography and Automated Biological Calorimetry facility.

Also, the researchers in Chopras lab will work with Natasha Tirko, director of the Master of Biotechnology Degree Program of the Huck Institutes, to organize a program for local high schoolers to gain hands-on laboratory and field exercises to learn about the inheritance of crop traits. The program, called Corn Summer Internships in Gene Silencing, will provide participants the opportunity to use plant phenotypes observablecharacteristics resulting from the interaction of plants with their environment to study what triggers epigenetic gene silencing.

These outreach activities will be in collaboration with Caitlin Teti, director and Carol-Beth Book, education program specialist of the Penn State Office of Science Outreach. This project will also involve Virginia State University faculty members Sarah Witiak and Rafat Siddiquiand their undergraduates, who will participate in research and outreach activities at Penn State.

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NSF grant to fund research on genetics and physiology of corn kernel development - Penn State University

Genetics

Honey Creek Beef farm’s success spurred by focus on animal comfort, genetics and feed – Dayton Daily News

Generational farming

The couple, who have been married for almost 10 years, were inspired by Grimes family history in the farming industry. Her late-father had started grain farming in the 1980s after owning the John Deere Dealership in Urbana and his father had raised Angus and Hereford cattle on the same property. Grimes said her great-grandmother was the one that started the family in the farming industry in the 1930s raising everything from hogs and sheep to various crops.

Grimes originally went to college in Florida to study business, but returned home and started studying agricultural business at The Ohio State University. At this time, she was working on the grain farm with her dad.

Frantz did not come from a farming family, but said he had wanted to be a farmer since high school. He recalled working at a dairy farm milking cows his junior and senior years and falling in love with the personalities of the animals. With some guidance from a professor at Wilmington College, he graduated with a degree in agronomy. He was working for an agriculture retail supplier where he met Grimes because her dad was one of his customers.

The start of Honey Creek Beef

There were two unique instances that sparked the start of Honey Creek Beef.

The couple recalled going to a farming conference in Minnesota where one of the speakers suggested that as grain farmers we should be looking for alternative markets to sell our grain on a more regular basis, Frantz said.

This was at a time when grain prices were fairly low, Grimes added. One of the suggestions was to deliver grain to a cattle, chicken or hog farm and they thought why not take it to their own livestock operation.

They also recalled eating dinner at Seasons Bistro and Grille, previously located at 28 S. Limestone St. in Springfield, and talking with co-owner Doug McGregor about where he sourced the restaurants beef from. Frantz said McGregor was using a corporate distributor because he was having a hard time getting the same quality and consistency locally.

And that just kind of lit a fire under Adam, Grimes said.

Seasons Bistro and Grille became their first customer.

Its all about genetics

The couple is raising American Black Angus cattle due to their genetics.

Angus genetics have given us a set of cows and bulls that basically take care of themselves as long as they have good grass and fresh water, Frantz said. The American Angus Association has done a lot of genetic research to help create a modern cow that produces healthy calves and eventually high quality steaks.

Frantz is breeding for cows with a maternal characteristic meaning they are good mothers to their calves and dont need assistance. Hes looking at their carcass traits for marbling and the size of the ribeye loin. He said the way cows bodies develop is genetic. He also makes sure the cows on his farm are docile to protect those that work at their farm like farmhands Jacob Green and Katie Turner.

Feed is grown on the same farm

All cows at Honey Creek Beef are fed grass that is grown at the farm.

Nutritional cool season grasses have the best nutrition for cattle, Frantz said.

Their cows are fed a mix of orchard grass, perennial ryegrass, Kentucky bluegrass and Timothy grass. They also use Triticale, a hybrid grass with a high nutritional value, in the winter months when grasses dont grow very well. When they feed the cows Triticale, they also mix in haylage or corn silage based on what an animal nutritionist recommends.

We dont use any unnecessary medications on this farm, Frantz said. We dont use any feed additives that are medications or anything like that and we dont use any hormone injections. We think the genetics itself has all the right balance of what that animal needs.

Soil health and conservation are also extremely important at Honey Creek Beef.

We make decisions that have lasting impacts on the soil and we are constantly thinking of ways to make it better, Frantz said.

Cow comfort and care

Another important part of Honey Creek Beef is cow comfort and care. They want their cows to naturally exist without constant influence from humans to alleviant any stress.

Their comfort, ability to choose their own feed, and an all natural pasture environment, contribute heavily to the quality of the meat and are critical to the success of the farm, Frantz said.

If a cow is stressed, then the quality of its beef decreases. This could result in tougher and less marbled meat, Frantz said.

To create a less stress or no stress environment, Honey Creek Beef uses horses and horsemanship. Frantz said the cows are less stressed when their team is riding horseback through the pasture, rather than driving an ATV or pick-up truck.

Growing the herd from scratch

Honey Creek Beef started off with two heifers (female cows that havent given birth). They ended up breeding one and then buying three female calves from partner rancher Wesley Lambert, who has became a huge influence in helping Frantz grow his herd from scratch.

A lot of beef farmers they will buy calves just to feed out (and then sell the beef). All of our calves are born here, Grimes said.

The couple breeds every cow on the farm to calve every 11 months. They have two bulls that are rotated. With the bulls, they make sure bloodlines do not overlap and that their characteristics are a good match for their average cow.

When a cow has a bull, Honey Creek Beef castrates the bull at six months and separates it from the mom. The bull is fed on a separate pasture and lives until about one year old. Their goal is to raise the bulls naturally to 1,200 pounds before they are harvested. Frantz said the average age a steer is harvested in the U.S. is 18 to 24 months. Honey Creek Beef is able to harvest younger steers resulting in more tender and higher quality beef.

All steers are taken to Cavens Meats, an Ohio Department of Agriculture inspected facility located in Fletcher right outside of Piqua.

Its an important part of this process too for us to make sure its done with the same quality and care standards that animal has experienced for its entire life, Frantz said.

Honey Creek Beef harvests one steer a week resulting in 500 pounds of processed, package beef products like steaks, ground beef, roast, hamburger patties or bratwurst, Frantz said.

Where to find Honey Creek Beef

Honey Creek Beef can be found at the Springfield Farmers Market, Harmony Farm Market & Gifts in Springfield, Gills Quality Meat Market in Springfield, Charlos Provisions & Eatery in Springfield, Harens Market in Troy and Current Cuisine in Yellow Springs.

The cattle farm is also open from 11 a.m. to 2 p.m. every Sunday for customers to buy meat and see where their meat is coming from. If youve never had Honey Creek Beef, the couple recommends trying their burgers, ribeyes, New York strips or chuck roasts.

Honey Creek Beef also works with several chefs with restaurants and food trucks in the Dayton and Springfield area including CULTURE in Dayton, Little Fish Brewing Company in Dayton, Seasons Kitchen in Springfield, Sushi Hikari Moe in Springfield, Fully Cooked Sushi in Springfield and Yellow Springs Baking Company in Yellow Springs.

The couple said working with local chefs is one of the best parts because its amazing to see what they are able to create using their beef.

Chefs were where we wanted to go because raising this beef is an art for us, Frantz said. Cowboying is an art. The chef world is an art.

Future of Honey Creek Beef

Honey Creek Beef wants to continue growing their business in a sustainable and organic way in 2024. They hope to reach more customers and get more people on the farm to help make the connection of where their food is coming from.

They also plan to start a second pasture near their main farm location.

Im proud of being able to say when I was a little kid this is what I wanted to do and were actually doing it, Frantz said.

Honey Creek Beef is named after the creek located across the street from the farm. For more information or to order beef, visit honeycreekbeef.com or the farms Facebook or Instagram pages (@honeycreekbeef).

Natalie Jones writes about where our food comes from and dining in Dayton and the Southwest Ohio region. If you would like your business to be considered for this feature, email natalie.jones@coxinc.com or find her on TikTok @natalie_reports and Instagram @natalie_reports937.

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Honey Creek Beef farm's success spurred by focus on animal comfort, genetics and feed - Dayton Daily News

Genetics

Deciphering genetic diversity in conserved cattle bulls to achieve sustainable development goals | Scientific Reports – Nature.com

National Gene Bank, ICAR-NBAGR, Karnal is dedicated to achieve Sustainable Development Goals (SDG) through conserving genetic diversity through preserving semen, somatic cell, and embryos for long term storage. Along with the conservation of animal genetic resources, assessment of genetic diversity is crucial for preserving genetic diversity and preventing the loss of undesirable alleles. This finding of this study revealed excessive heterozygosity across all the cattle populations conserved at National Gene Bank, ICAR-NBAGR. This statement can be validated by comparing the expected (0.650.01) and observed (0.720.01) heterozygosity across all the cattle population conserved.

In this study, a total of 267 alleles were identifed across all the 192 cattle bulls samples using 17 microsatellite markers (1.36 alleles/individual). However, previous studies revealed 1.2217, 1.2518, and 0.70710 alleles per individual in Indian cattle populations. The detection of a higher number of alleles per individual in conserved cattle bulls indicates that substantial amount of allelic variation is being maintained at National Gene Bank. Moreover, it is worth mentioning that smaller number of microsatellite primer pairs (17) were used for this study as compared to the previous studies, which again provides an indication towards existence of sufficient allelic variation in the conserved semen samples. Interestingly, ILSTS34 marker contributed highest number of alleles (26) in the selected individuals, which is well corroborated with the previous studies10,18.

In this study, we observed wide range of average observed number of alleles per locus, ranging from 2.2350.202 in Amritmahal to 8.6470.790 in Haryana cattle. This variation may be attributed to significant differences in the sample sizes of the conserved cattle populations at the National Gene Bank. Further, average observed number of alleles across all the populations and loci was 5.2760.145, and was lower than other research reports published elsewhere10,18,19. However, when comparing specific breeds, the allelic diversity in Sahiwal cattle (8.00.928) and Haryana cattle (8.6470.790) was found to be higher than what was previously reported in studies by Mukesh et al.17 and Sharma et al.10. At global level, less allelic diversity was observed across all the populations as compared to exotic breeds such as Burlina, Brown Swiss and Holstein Friesian cattle20. Additionally, lower value of effective number of alleles as compared to observed number of alleles across all the cattle populations suggested that there were many low frequency alleles in the populations. This reduced allelic diversity in the current scenario can be attributed to the smaller sample sizes per breed compared to previous studies. It is recommended that maximum allelic diversity be conserved in various Gene Banks established worldwide to ensure future sustainability.

The detection of a high level of observed heterozygosity (0.720.01) across all loci and populations in the conserved cattle bulls signifies a remarkable degree of genetic diversity. This can be attributed to a reduced influence of human-driven selection pressures and suggests the presence of large effective population sizes in the considered Indian cattle populations. The substantial genetic variation observed in Indian cattle breeds has likely contributed to their adaptability across diverse agroclimatic regions. This genetic diversity is likely a result of environmental pressures for adaptability and natural processes of mutation. The indigenous Indian cattle populations, managed according to local use and traditional husbandry practices, have shown no signs of inbreeding issues and have successfully maintained a higher level of genetic variability. This enhanced genetic diversity has played a crucial role in their superior adaptation to the natural environment. This genetic diversity can be well exploited for cattle genetic improvement as well as to facilitate rapid adaptation to changed breeding goals21. Genetic diversity is essential for any population to adapt and survive in their environments. It also facilitates local population or breed adaptation to dynamic environments. Further, leveraging high genetic diversity becomes crucial for expanding the genetic pool when a concerned breed or population confronts issues such as inbreeding and diminished genetic diversity, which in turn increases the risk of extinction.

The overall estimate of observed heterozygosity in the present investigation (0.720.01) was higher than previous investigations such as Tharparkar (0.643) and Rathi (0.694) cattle22, Kherigarh cattle19, and 15 other Indian cattle breeds23. Moreover, it was found higher than Indonesian cattle breeds24 Hartn del Valle, Angus, Brangus, Holstein, and Senepol cattle breeds in Colombia Montoya et al.25 and selected Ethiopian indigenous cattle26. An interesting observation was made in this study, wherein it was found that expected heterozygosity is either equal or less than observed heterozygosity in all the populations under investigation. It is worth mentioning that many studies explaining genetic diversity using microsatellite markers have found out less observed heterozygosity than expected heterozygosity10,17,20,22,27 except few28. This further confirms that a substantial level of genetic diversity is being effectively maintained in the conserved cattle bulls at the National Gene Bank, ICAR-NBAGR.

All cattle populations conserved at National Gene Bank revealed no heterozygote deficit except the Amritmahal cattle (0.058). These finding may be interpreted as cattle bulls conserved might be produced through outcrossing. Further, these results are well corroborated with the pattern expressed in estimates of heterozygosity and suggests lack of inbreeding in the conserved cattle bulls The National Gene Bank's long-term efforts in conserving cattle bulls have successfully preserved high levels of genetic diversity. In India, lack of structured breeding programme at the village level and not culling of cattle bulls may contribute to the maintenance of substantial genetic diversity within and between Indian cattle populations. In contrast, many Indian cattle populations have revealed significant homozygote excess in the previous study10,22. This heterozygote deficit might be due to collection of samples from closed herd or from sampling error.

Wrights F-statistics, and particularly FST, are valuable tools for understanding the evolutionary processes that shape the structure of genetic variation within and between populations, and they are among the most widely used descriptive statistics in population and evolutionary genetics. In population differentiation, a FST value greater than 0.15 is typically considered significant29. The highest FST value were found between Gir and Amritmahal cattle (0.185), Red Kandhari and Amritmahal (0.182), and Gangatiri and Amritmahal (0.176). This result revealed within-breed genetic variation is more than between-breed genetic variation. Further, this genetic variation could be well utilised for genetic upgradation and conservation of cattle populations in India. Further, the overall FST, F IT and FIS value across the loci and population is 0.1460.009, 0.0540.038, and 0.1050.035, respectively. These estimates obtained in the present investigation suggests lack of inbreeding in the conserved bull semen. However, these type of findings are rarely observed in natural conditions. Mostly, FIS would be positive and FIT>FST, this could be considered as evidence of inbreeding30. It is commonly hypothesized that in a population where mating occurs randomly, genes would exhibit equal levels of relatedness both within individuals and between individuals. In such conditions FIT equals FST or FIS equals zero22. Sodhi et al.22 reported F-statistics: FIS=0.1120.029, F IT=0.1690.033, F ST=0.0650.017, and interpreted departure of populations from random mating. In addition, across all the loci under investigation, FST ranged from 0.068 (ILSTS11) to 0.199 (ETH3) with an average of 0.146. This FST values revealed that the most of total allelic variation (85.4%) corresponds to differences among individuals, and only 14.5% genetic variation could be attributed to differences among breeds. Further, ETH3 (0.199), TGLA122 (0.195), MM8 (0.192), ILSTS06 (0.188), ETH10 (0.171), MM12 (0.161), BM1824 (0.156) markers might be considered as more informative to differentiate the populations under investigation. However, this statement need to be validated in large number of individuals of populations under study.

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Deciphering genetic diversity in conserved cattle bulls to achieve sustainable development goals | Scientific Reports - Nature.com