Mouse Genetic Reference Populations: Cellular Platforms for Integrative Systems Genetics.

Interrogation of disease-relevant cellular and molecular traits exhibited by genetically diverse cell populations enables in vitro systems genetics approaches for uncovering the basic properties of cellular function and identity. Primary cells, stem cells, and organoids derived from genetically diverse mouse strains, such as Collaborative Cross and Diversity Outbred populations, offer the opportunity for parallel in vitro/in vivo screening. These panels provide genetic resolution for variant discovery and functional characterization, as well as disease modeling and in vivo validation capabilities. Here we review mouse cellular systems genetics approaches for characterizing the influence of genetic variation on signaling networks and phenotypic diversity, and we discuss approaches for data integration and cross-species validation.

Strategies for genomic predictions of an indicine multi-breed population using single-step GBLUP.

Brazilian livestock breeding programmes strive to enhance the genetics of beef cattle, with a strong emphasis on the Nellore breed, which has an extensive database and has achieved significant genetic progress in the last years. There are other indicine breeds that are economically important in Brazil; however, these breeds have more modest sets of phenotypes, pedigree and genotypes, slowing down their genetic progress as their predictions are less accurate. Combining several breeds in a multi-breed evaluation could help enhance predictions for those breeds with less information available. This study aimed to evaluate the feasibility of multi-breed, single-step genomic best linear unbiased predictor genomic evaluations for Nellore, Brahman, Guzerat and Tabapua. Multi-breed evaluations were contrasted to the single-breed ones. Data were sourced from the National Association of Breeders and Researchers of Brazil and included pedigree (4,207,516), phenotypic (328,748), and genomic (63,492) information across all breeds. Phenotypes were available for adjusted weight at 210 and 450 days of age, and scrotal circumference at 365 days of age. Various scenarios were evaluated to ensure pedigree and genomic information compatibility when combining different breeds, including metafounders (MF) or building the genomic relationship matrix with breed-specific allele frequencies. Scenarios were compared using the linear regression method for bias, dispersion and accuracy. The results showed that using multi-breed evaluations significantly improved accuracy, especially for smaller breeds like Guzerat and Tabapua. The validation statistics indicated that the MF approach provided accurate predictions, albeit with some bias. While single-breed evaluations tended to have lower accuracy, merging all breeds in multi-breed evaluations increased accuracy and reduced dispersion. This study demonstrates that multi-breed genomic evaluations are proper for indicine beef cattle breeds. The MF approach may be particularly beneficial for less-represented breeds, addressing limitations related to small reference populations and incompatibilities between G and A22. By leveraging genomic information across breeds, breeders and producers can make more informed selection decisions, ultimately improving genetic gain in these cattle populations.

Effects of reference population size and structure on genomic prediction of maternal traits in two pig lines using whole-genome sequence-, high-density- and combined annotation-dependent depletion genotypes.

The aim of this study was to investigate the reference population size required to obtain substantial prediction accuracy within- and across-lines and the effect of using a multi-line reference population for genomic predictions of maternal traits in pigs. The data consisted of two nucleus pig populations, one pure-bred Landrace (L) and one Synthetic (S) Yorkshire/Large White line. All animals were genotyped with up to 30 K animals in each line, and all had records on maternal traits. Prediction accuracy was tested with three different marker data sets: High-density SNP (HD), whole genome sequence (WGS), and markers derived from WGS based on pig combined annotation dependent depletion-score (pCADD). Also, two different genomic prediction methods (GBLUP and Bayes GC) were compared for four maternal traits; total number piglets born (TNB), total number of stillborn piglets (STB), Shoulder Lesion Score and Body Condition Score. The main results from this study showed that a reference population of 3 K-6 K animals for within-line prediction generally was sufficient to achieve high prediction accuracy. However, when the number of animals in the reference population was increased to 30 K, the prediction accuracy significantly increased for the traits TNB and STB. For multi-line prediction accuracy, the accuracy was most dependent on the number of within-line animals in the reference data. The S-line provided a generally higher prediction accuracy compared to the L-line. Using pCADD scores to reduce the number of markers from WGS data in combination with the GBLUP method generally reduced prediction accuracies relative to GBLUP using HD genotypes. The BayesGC method benefited from a large reference population and was less dependent on the different genotype marker datasets to achieve a high prediction accuracy.

Mouse Systems Genetics as a Prelude to Precision Medicine.

Mouse models have been instrumental in understanding human disease biology and proposing possible new treatments. The precise control of the environment and genetic composition of mice allows more rigorous observations, but limits the generalizability and translatability of the results into human applications. In the era of precision medicine, strategies using mouse models have to be revisited to effectively emulate human populations. Systems genetics is one promising paradigm that may promote the transition to novel precision medicine strategies. Here, we review the state-of-the-art resources and discuss how mouse systems genetics helps to understand human diseases and to advance the development of precision medicine, with an emphasis on the existing resources and strategies.

Effect of subdivision of the Lacaune dairy sheep breed on the accuracy of genomic prediction.

Genomic selection was deployed in Lacaune dairy breed in 2015. Lacaune population split in 1972 into 2 breeding companies with associated flocks, and there have been very few exchanges of animals between the subpopulations, leading to divergence of the 2 subpopulations. In spite of that, there is a joint genomic prediction. The objective of this study is to understand how this structuring affects prediction accuracy. We analyzed all the data available from Lacaune breeding program for milk yield: around 6 million phenotypes, 2 million animals in the pedigree and more than 29,000 genotyped animals, including 3,434 and 2,868 AI rams for each company. To consider missing pedigree, we set up genetic groups using the theory of metafounders. First, we studied the pedigree and genomic structures of the 2 subpopulations calculating Fst, evolution of average pedigree relationships across time and principal components analysis of genomic relationships. In a second part, we compared the reliability between different scenarios: an evaluation with a single reference population (Alone), an evaluation with a joint reference population (Together) and an evaluation of one subpopulation based on the reference population of the other group (Indirect). The low Fst value (0.02) reveals that the 2 subpopulations are still genetically close. Nevertheless, a low and constant average relationship between the animals of the 2 subpopulations confirms the absence of recent connections between them. We can see with principal component analysis results that even if they are close, they diverge over time. Finally, we observe small gains in accuracy of Together versus Alone, in spite of whereas doubling the reference population size in Together. These gains vary across years and subpopulations: less than 0.08 (0.46 to 0.54; ratio of accuracy for the partial and whole evaluations-corresponding to the greatest change in this ratio for breeding company 1, observed for the cohort 2016) for one subpopulation and between 0.03 (0.55 to 0.58) and 0.17 (0.48 to 0.65) for the other. To conclude, the 2 subpopulations remain close enough genetically so that their combined evaluation is advantageous, even if only slightly.

Variant interpretation using population databases: Lessons from gnomAD.

Reference population databases are an essential tool in variant and gene interpretation. Their use guides the identification of pathogenic variants amidst the sea of benign variation present in every human genome, and supports the discovery of new disease-gene relationships. The Genome Aggregation Database (gnomAD) is currently the largest and most widely used publicly available collection of population variation from harmonized sequencing data. The data is available through the online gnomAD browser (https://gnomad.broadinstitute.org/) that enables rapid and intuitive variant analysis. This review provides guidance on the content of the gnomAD browser, and its usage for variant and gene interpretation. We introduce key features including allele frequency, per-base expression levels, constraint scores, and variant co-occurrence, alongside guidance on how to use these in analysis, with a focus on the interpretation of candidate variants and novel genes in rare disease.

The statistical foundation of the reference population for semen analysis included in the sixth edition of the WHO manual: a critical reappraisal of the evidence.

In the most recent version of the 'WHO Laboratory Manual For The Examination And Processing Of Human Semen', the updated target population used to infer reference values included 3589 fertile subjects, representative of 12 countries and 5 continents, and 10 studies. We have critically evaluated the newly proposed distribution of semen examination results using an approach borrowed from clinical chemistry laboratories and based on the recommendations of the International Federation of Clinical Chemistry for estimation of reference intervals. Surprisingly, most prerequisites to produce common reference intervals through multicentric data were not met. Moreover, when we assessed with the bootstrap method the descriptive reference values obtained from raw data of the 10 individual studies for sperm concentration, sperm number, motility and normal forms, we found that none of the populations was completely correctly described by the reference centiles. We concluded that aggregated data used to build the reference distribution cannot be considered to originate from the same population, and this can result from real differences among individuals or different methodological approaches used in the various studies. Transferability conditions across studies did not seem to have been met. Our findings strengthen the relevance of concerns regarding the use of reference populations in the World Health Organization manual to discriminate between fertile and infertile men.

Driving mosaicism: somatic variants in reference population databases and effect on variant interpretation in rare genetic disease.

BACKGROUND: Genetic variation databases provide invaluable information on the presence and frequency of genetic variants in the 'untargeted' human population, aggregated with the primary goal to facilitate the interpretation of clinically important variants. The presence of somatic variants in such databases can affect variant assessment in undiagnosed rare disease (RD) patients. Previously, the impact of somatic mosaicism was only considered in relation to two Mendelian disease-associated genes. Here, we expand the analyses to identify additional mosaicism-prone genes in blood-derived reference population databases. RESULTS: To identify additional mosaicism-prone genes relevant to RDs, we focused on known/previously established ClinVar pathogenic and likely pathogenic single-nucleotide variants, residing in genes associated with early onset, severe autosomal dominant diseases. We asked whether any of these variants are present in a higher-than-expected frequency in the reference population databases and whether there is evidence of somatic origin (i.e., allelic imbalance) rather than germline heterozygosity (~ half of the reads supporting alternative allele). The mosaicism-prone genes identified were further categorized according to the processes they are involved in. Beyond the previously reported ASXL1 and DNMT3A, we identified 7 additional autosomal dominant RD-associated genes with known pathogenic single-nucleotide variants present in the reference population databases and good evidence of allelic imbalance: BRAF, CBL, FGFR3, IDH2, KRAS, PTPN11 and SETBP1. From this group of 9 genes, the majority (n = 7) was important for hematopoiesis. In addition, 4 of these genes were involved in cell proliferation. Further assessment of the known 156 hematopoietic genes led to identification of 48 genes (21 not yet associated with RDs) with at least some evidence of mosaicism detectable in reference population databases. CONCLUSIONS: These results stress the importance of considering genes involved in hematopoiesis and cell proliferation when interpreting the presence and frequency of genetic variants in blood-derived reference population databases, both public and private. This is especially important when considering new variants of uncertain significance in known hematopoietic/cell proliferation RD genes and future novel gene-disease associations involving this class of genes.

Determination of Age- and Sex-Specific 99th Percentile Upper Reference Limits for High-Sensitivity Cardiac Troponin I in Healthy Chinese Adults.

INTRODUCTION: Age- and sex-specific reference intervals (RIs) for high-sensitivity cardiac troponin I (hs-cTnI) are not available in China. The objective of the present research was to determine the assay-specific 99th percentile upper reference limits (URLs) for hs-cTnI in healthy Chinese adults. METHODS: Apparently healthy individuals were first screened with a questionnaire. The reference population was selected according to the International Federation of Clinical Chemistry (IFCC) criteria using surrogate biomarker for diabetes, myocardial dysfunction, and renal dysfunction. The serum concentration of hs-cTnI was measured using the automatized VITROS 5600 Immunodiagnostic system (Ortho Clinical Diagnostics, Raritan, NJ, USA). RESULTS: A total of 2,183 healthy individuals (1,051 men and 1,132 women aged from 20 to 95 years) were enrolled in the study. The serum overall 99th percentile URLs of hs-cTnI were 11.1 ng/L (12.5 ng/L in men, 9.6 ng/L in women). In both men and women, the 99th percentile URLs were significantly higher in individuals ≥55 years old than those in the <55 years old, especially in women. Moreover, 78.8% of males and 70.1% of females presented with hs-cTnI concentrations above the limit of detection of 0.43 ng/L. CONCLUSION: The hs-cTnI-VITROS assay coincided with the performance standard of the IFCC for high-sensitivity cTnI assays. The 99th percentile URLs for hs-cTnI in healthy Chinese adults were different from the manufacturer declared and appeared heterogeneous, potentially susceptible to factors such as age and sex.

The Influence of DXA Hardware, Software, Reference Population and Software Analysis Settings on the Bone Mineral Density and T-Score Relationship.

Different dual-energy x-ray absorptiometry (DXA) hardware can affect bone mineral density (BMD) measurements and different reference populations can affect t-scores. Long-term analyses describing differences in the relationship between BMD and t-scores are lacking. BMD-values were plotted against t-scores for 241 Lunar DXA scans on females obtained over 18 years from several centers in Sweden and Norway. The result of the plot was compared to hardware/software versions, reference populations and different software analysis settings (Basic vs Enhanced analysis for total body and Single Photon Absorptiometry (SPA) vs Lunar calibration for forearm). For the forearm compartments, we found different BMD-t-score relationships depending on the use of SPA or Lunar calibration (p<0.001). With Lunar calibration, BMD-values were 24% higher, but there was no effect on t-scores. Total body measurements with iDXA scanners and Enhanced analysis for Prodigy scanners (software version 14.10) resulted in a different BMD-t-score relationship compared to the other hardware/software versions (p<0.001), with the largest discrepancy for lower BMD-values. Switching from Basic to Enhanced analysis generally decreased BMD-values and often changed t-scores (both increased and decreased). For the femoral neck, there were two different BMD-t-score relationships caused by different reference populations (p<0.001). In contrast to total body, the difference for femoral neck was more pronounced for higher values, with little impact in the clinical decision-making area. Hardware, software, reference populations and software analysis settings can affect the BMD-t-score relationship, but do so differently for different compartments. The BMD-t-score-plot is a simple and effective tool to discover systematic differences. Longitudinal analyses of DXA scans should be performed based on raw data analyzed in "one run" with the same software version and settings, in order to avoid systematic differences.

Novel type of references for BMI aligned for onset of puberty - using the QEPS growth model.

OBJECTIVES: Despite inter-individual variations in pubertal timing, growth references are conventionally constructed relative to chronological age (C-age). Thus, they are based on reference populations containing a mix of prepubertal and pubertal individuals, making them of limited use for detecting abnormal growth during adolescence. Recently we developed new types of height and weight references, with growth aligned to age at onset of the pubertal growth spurt (P-age). Here, we aim to develop a corresponding reference for pubertal BMI. METHODS: The QEPS-height and weight models were used to define a corresponding QEPS-BMI model. QEPS-BMI was modified by the same individual, constitutional weight-height-factor (WHF) as computed for QEPS-weight. QEPS-BMI functions were computed with QEPS weight and height functions fitted on longitudinal measurements from 1418 individuals (698 girls) from GrowUp1990Gothenburg cohort. These individual BMI functions were used to develop BMI references aligned for height at AgeP5; when 5% of specific puberty-related (P-function) height had been attained. Pubertal timing, stature at pubertal onset, and childhood BMI, were investigated in subgroups of children from the cohort GrowUp1974Gothenburg using the new references. RESULTS: References (median, standard deviation score (SDS)) were generated for total BMI (QEPS-functions), for ongoing prepubertal growth (QE-function) vs C-age, and for total BMI and separated into BMI specific to puberty (P-function) and BMI gain from ongoing basic growth (QES-functions), allowing individual growth to be aligned based on P-age. Growth in basic BMI was greater than average for children categorized as tall and/or with high-BMI at puberty-start. In children categorized as short at puberty-start, P-function-related-BMI was greater than average. CONCLUSIONS: Use of these new pubertal BMI references will make it possible for the first time to consider individual variations owing to pubertal timing when evaluating BMI. This will improve the detection of abnormal changes in body composition when used in combination with pubertal height and weight references also abnormal growth. Other benefits in the clinic will include improved growth monitoring during treatment for children who are overweight/obese or underweight. Furthermore, in research settings these new references represent a novel tool for exploring human growth.

Assessing accuracy of genotype imputation in the Afrikaner and Brahman cattle breeds of South Africa.

Imputation may be used to rescue genomic data from animals that would otherwise be eliminated due to a lower than desired call rate. The aim of this study was to compare the accuracy of genotype imputation for Afrikaner, Brahman, and Brangus cattle of South Africa using within- and multiple-breed reference populations. A total of 373, 309, and 101 Afrikaner, Brahman, and Brangus cattle, respectively, were genotyped using the GeneSeek Genomic Profiler 150 K panel that contained 141,746 markers. Markers with MAF ≤ 0.02 and call rates ≤ 0.95 or that deviated from Hardy Weinberg Equilibrium frequency with a probability of ≤ 0.0001 were excluded from the data as were animals with a call rate ≤ 0.90. The remaining data included 99,086 SNPs and 360 Afrikaner, 75,291 SNPs and 288 animals Brahman, and 97,897 SNPs and 99 Brangus animals. A total of 7986, 7002, and 7000 SNP from 50 Afrikaner and Brahman and 30 Brangus cattle, respectively, were masked and then imputed using BEAGLE v3 and FImpute v2. The within-breed imputation yielded accuracies ranging from 89.9 to 96.6% for the three breeds. The multiple-breed imputation yielded corresponding accuracies from 69.21 to 88.35%. The results showed that population homogeneity and numerical representation for within and across breed strategies, respectively, are crucial components for improving imputation accuracies.

Exploring the size of reference population for expected accuracy of genomic prediction using simulated and real data in Japanese Black cattle.

BACKGROUND: Size of reference population is a crucial factor affecting the accuracy of prediction of the genomic estimated breeding value (GEBV). There are few studies in beef cattle that have compared accuracies achieved using real data to that achieved with simulated data and deterministic predictions. Thus, extent to which traits of interest affect accuracy of genomic prediction in Japanese Black cattle remains obscure. This study aimed to explore the size of reference population for expected accuracy of genomic prediction for simulated and carcass traits in Japanese Black cattle using a large amount of samples. RESULTS: A simulation analysis showed that heritability and size of reference population substantially impacted the accuracy of GEBV, whereas the number of quantitative trait loci did not. The estimated numbers of independent chromosome segments (Me) and the related weighting factor (w) derived from simulation results and a maximum likelihood (ML) approach were 1900-3900 and 1, respectively. The expected accuracy for trait with heritability of 0.1-0.5 fitted well with empirical values when the reference population comprised > 5000 animals. The heritability for carcass traits was estimated to be 0.29-0.41 and the accuracy of GEBVs was relatively consistent with simulation results. When the reference population comprised 7000-11,000 animals, the accuracy of GEBV for carcass traits can range 0.73-0.79, which is comparable to estimated breeding value obtained in the progeny test. CONCLUSION: Our simulation analysis demonstrated that the expected accuracy of GEBV for a polygenic trait with low-to-moderate heritability could be practical in Japanese Black cattle population. For carcass traits, a total of 7000-11,000 animals can be a sufficient size of reference population for genomic prediction.

Diagnostic algorithms for non-ST-segment elevation myocardial infarction: open issues.

The use of serial measurement of cardiac troponin (cTn) is recommended by international guidelines for the diagnosis of myocardial infarction (MI) since 2000. This article focuses on factors influencing temporal changes in high-sensitive cTn (hs)-cTn and the impact of these factors on the diagnosis of non-ST-segment elevation MI (NSTEMI). The recommendations proposed by three different international guidelines published in 2020-2021 for the management of acute coronary syndromes (ACS) in patients presenting without persistent ST-segment elevation (NSTE) show some discrepancies. In particular, there is no agreement among these guidelines about cut-off or absolute change values to be used for the rule-in, especially regarding the use of sex-specific cut-off values. Furthermore, there are no sufficient evidences on the diagnostic accuracy and cost effectiveness related to cut-off values suggested for algorithms to be used by some hs-cTnI methods.

Novel type of references for weight aligned for onset of puberty - using the QEPS growth model.

BACKGROUND: Growth references are traditionally constructed relative to chronological age, despite inter-individual variations in pubertal timing. A new type of height reference was recently developed allowing growth to be aligned based on onset of pubertal height growth. We here aim to develop a corresponding reference for pubertal weight. METHODS: To model QEPS-weight, 3595 subjects (1779 girls) from GrowUp1974Gothenburg and GrowUp1990Gothenburg were used. The QEPS-height-model was transformed to a corresponding QEPS-weight-model; thereafter, QEPS-weight was modified by an individual, constitutional weight-height-factor. Longitudinal weight and length/height measurements from 1418 individuals (698 girls) from GrowUp1990Gothenburg were then used to create weight references aligned for height at pubertal onset (the age at 5% of P-function growth, AgeP5). GrowUp1974Gothenburg subgroups based on pubertal timing, stature at pubertal onset, and childhood body composition were assessed using the references. RESULTS: References (median, SDS) for total weight (QEPS-functions), weight specific to puberty (P-function), and weight gain in the absence of specific pubertal growth (basic weight, QES-functions), allowing alignment of individual growth based on age at pubertal onset. For both sexes, basic weight was greater than average for late maturing, tall and high-BMI subgroups. The P-function-related weight was greater than average in short and lower than average in tall children, in those with high BMI, and in girls but not boys with low BMI. CONCLUSIONS: New pubertal weight references allow individual variations in pubertal timing to be taken into consideration when evaluating growth. When used together with the comparable pubertal height reference, this will improve growth monitoring in clinical practice for identifying abnormal growth and serve as a valuable research tool providing insight into human growth.

Swedish references for weight, weight-for-height and body mass index: The GrowUp 1990 Gothenburg study.

AIM: To update the Swedish references for weight, weight-for-height and body mass index (BMI) considering the secular trend for height but not including that for weight. METHODS: Longitudinal measures of height and weight were obtained (0-18 years) from 1418 (698 girls) healthy children from the GrowUp 1990 Gothenburg cohort born at term to non-smoking mothers and Nordic parents. A total of 145 individuals with extreme BMI value vs GrowUp 1974 BMI SDS reference were excluded (0-2 years: ±4SDS, 2 < years: -3SDS, +2.3SDS). References were constructed using the LMS method. RESULTS: The updated weight reference became similar to the GrowUp 1974 Gothenburg reference: BMI increased rapidly up to lower levels in the 1990 cohort during infancy/early childhood, similar in both groups in late childhood/adolescence, despite lower values at +2SDS. Compared with the WHO weight standard, median and -2SDS weight values were higher for the 1990 cohort, whereas +2SDS values were lower, resulting in narrower normal range. Median values were greater and ±2SDS narrower for the 1990 vs the WHO weight-for-height reference. International Obesity Task force (IOTF) BMI lines for definitions for over- and underweight were added. CONCLUSION: We present updated references for weight, weight-for-height and BMI, providing a healthy goal for weight development when monitoring growth within healthcare settings.

Applying an association weight matrix in weighted genomic prediction of boar taint compounds.

Biological information regarding markers and gene association may be used to attribute different weights for single nucleotide polymorphism (SNP) in genome-wide selection. Therefore, we aimed to evaluate the predictive ability and the bias of genomic prediction using models that allow SNP weighting in the genomic relationship matrix (G) building, with and without incorporating biological information to obtain the weights. Firstly, we performed a genome-wide association studies (GWAS) in data set containing single- (SL) or a multi-line (ML) pig population for androstenone, skatole and indole levels. Secondly, 1%, 2%, 5%, 10%, 30% and 50% of the markers explaining the highest proportions of the genetic variance for each trait were selected to build gene networks through the association weight matrix (AWM) approach. The number of edges in the network was computed and used to derive weights for G (AWM-WssGBLUP). The single-step GBLUP (ssGBLUP) and weighted ssGBLUP (WssGBLUP) were used as standard scenarios. All scenarios presented predictive abilities different from zero; however, the great overlap in their confidences interval suggests no differences among scenarios. Most of scenarios of based on AWM provide overestimations for skatole in both SL and ML populations. On the other hand, the skatole and indole prediction were no biased in the ssGBLUP (S1) in both SL and ML populations. Most of scenarios based on AWM provide no biased predictions for indole in both SL and ML populations. In summary, using biological information through AWM matrix and gene networks to derive weights for genomic prediction resulted in no increase in predictive ability for boar taint compounds. In addition, this approach increased the number of analyses steps. Thus, we can conclude that ssGBLUP is most appropriate for the analysis of boar taint compounds in comparison with the weighted strategies used in the present work.

Identifying modifier genes for hypertrophic cardiomyopathy.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) severity greatly varies among patients even with the same HCM gene mutations. This variation is largely regulated by modifier gene(s), which, however, remain largely unknown. The current study is aimed to identify modifier genes using BXD strains, a large murine genetic reference population (GRP) derived from crosses between C57BL/6 J (B6) and D2 DBA/2 J (D2) mice. D2 mice natualy carrythe genetic basis and phenotypes of HCM. METHODS: Myocardial hypertrophy, the major phenotype of HCM, was determined by cardiomyocyte size on cardiac sections in 30 BXD strains, and their parental B6 and D2 strains and morphometric analysis was performed. Quantitative Trait Locus (QTL) mapping for cardiomyocyte sizes was conducted with WebQTL in GeneNetwork. Correlation of cardiomyocyte size and cardiac gene expression in BXDs accessed from GeneNetwork were evaluated. QTL candidate genes associated with cardiomyocyte sizes were prioritized based on the score system. RESULTS: Cardiomyocyte size varied significantly among BXD strains. Interval mapping on cardiomyocyte size data showed a significant QTL on chromosome (Chr) 2 at 66- 73.5 Mb and a suggestive QTL on Chr 5 at 20.9-39.7 Mb. Further score system revealed a high QTL score for Xirp2 in Chr 2. Xirp2 encodes xin actin-binding repeat containing 2, which is highly expressed in cardiac tissue and associate with cardiomyopathy and heart failure. In Chr5 QTL, Nos3, encoding nitric oxide synthase 3, received the highest score, which is significantly correlated with cardiomyocyte size. CONCLUSION: These results indicate that Xirp2 and Nos3 serve as novel candidate modifier genes for myocardial hypertrophy in HCM. These candidate genes will be validated in our future studies.

Evidence on clinical relevance of cardiovascular risk evaluation in the general population using cardio-specific biomarkers.

In recent years, the formulation of some immunoassays with high-sensitivity analytical performance allowed the accurate measurement of cardiac troponin I (cTnI) and T (cTnT) levels in reference subjects. Several studies have demonstrated the association between the risk of major cardiovascular events and cardiac troponin concentrations even for biomarker values within the reference intervals. High-sensitivity cTnI and cTnT methods (hs-cTn) enable to monitor myocardial renewal and remodelling, and to promptly identify patients at highest risk ofheart failure. An early and effective treatment of individuals at higher cardiovascular risk may revert the initial myocardial remodelling and slow down heart failure progression. Specific clinical trials should be carried out to demonstrate the efficacy and efficiency of the general population screening by means of cost-benefit analysis, in order to better identify individuals at higher risk for heart failure (HF) progression with hs-cTn methods.

Determination of high-sensitivity cardiac troponin T upper reference limits under the improved selection criteria in a Chinese population.

BACKGROUND: There is no common consensus on how to define the reference population for determination of high-sensitivity cardiac troponin (hs-cTn) upper reference limit (URL). This study aimed to establish 99th percentile URLs of hs-cTnT under both 2018 AACC/IFCC criteria and improved selection criteria for further judging whether two URLs are different. METHODS: Applying the stratified cluster sampling protocol, this study took 1848 apparently healthy subjects in communities of Shenyang China as the screening objects. We first followed 2018 AACC/IFCC criteria using surrogate biomarker for diabetes, myocardial dysfunction, renal dysfunction, and electrocardiogram. Then, we followed improved selection criteria to exclude hypertension, overweight and obesity, and dyslipidemia by physical examination and laboratory screening. Accordingly, 99th percentile URLs of hs-cTnT were established. RESULTS: If the 2018 AACC/IFCC criteria were applied, 99th percentile URLs (90% confidence interval) of hs-cTnT male, female, and total were 19 (17-20) ng/L, 16 (15-17) ng/L, and 18 (16-19) ng/L, respectively. If added a single supplementary selection criteria, 99th percentile URLs of hs-cTnT total reduced to 16 ng/L, 17 ng/L, and 16 ng/L, respectively. If the improved selection criteria were applied, 99th percentile URLs (90% confidence interval) of hs-cTnT male, female, and total were 18 (14-24) ng/L, 13 (11-16) ng/L, and 16 (13-17) ng/L, respectively. The 99th percentile URLs of hs-cTnT male were higher than those of female in every age group. CONCLUSIONS: Improved selection criteria through questionnaire survey, physical examination, and laboratory screening to further exclude hypertension, overweight and obesity, and dyslipidemia can avoid overestimation of the 99th percentile URL of hs-cTnT.