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Abstract In countries where farming is largely subsistence, no pedigree records of farm animals are maintained at farmers’ herd and scientific mating plans are not observed which leads to the accumulation of inbreeding and loss of production potential. Microsatellites have been widely used as reliable molecular markers to measure inbreeding. We attempted to correlate autozygosity estimated from microsatellite data with the inbreeding coefficient (F) calculated from pedigree data in Vrindavani crossbred cattle developed in India. The inbreeding coefficient was calculated from the pedigree of ninety-six Vrindavani cattle. Animals were further grouped into three groups viz. acceptable/low (F: 0–5%), moderate (F: 5–10%) and high (F: ≥10%), based on their inbreeding coefficients. The overall mean of the inbreeding coefficient was found to be 0.070 ± 0.007. A panel of twenty-five bovine-specific loci were chosen for the study according to ISAG/FAO. The mean F IS, F ST, and F IT values were 0.0548 ± 0.025, 0.012 ± 0.001 and 0.0417 ± 0.025, respectively. There was no significant correlation between the FIS values obtained and the pedigree F values. The locus-wise individual autozygosity was estimated using the method-of-moments estimator (MME) formula for locus-specific autozygosity. The autozygosities ascribing to CSSM66 and TGLA53 were found to be significantly (p < .01 and p < .05, respectively) correlated with pedigree F values. [ABSTRACT FROM AUTHOR]

GENOME-wide association studies, GENOTYPES, SINGLE nucleotide polymorphisms, and CATTLE genetics

Simple Summary: The effectiveness of selecting individuals based on pedigree information (MCA) and genomic information (MCG) as a reference population for genotype imputation was assessed using a Japanese Black cow population. Pedigree depth to construct the pedigree-based additive genetic relationship matrix (A matrix) affected the results of MCA, and the method of calculating the genomic relationship matrix (G matrix) affected the results of MCG. For the studied population, MCG appeared to be a better choice than MCA to select cows as a reference population for higher-density genotype imputation to perform genomic prediction and a genome-wide association study. As optimization methods to identify the best animals for dense genotyping to construct a reference population for genotype imputation, the MCA and MCG methods, which use the pedigree-based additive genetic relationship matrix (A matrix) and the genomic relationship matrix (G matrix), respectively, have been proposed. We assessed the performance of MCA and MCG methods using 575 Japanese Black cows. Pedigree data were provided to trace back up to five generations to construct the A matrix with changing the pedigree depth from 1 to 5 (five MCA methods). Genotype information on 36,426 single-nucleotide polymorphisms was used to calculate the G matrix based on VanRaden's methods 1 and 2 (two MCG methods). The MCG always selected one cow per iteration, while MCA sometimes selected multiple cows. The number of commonly selected cows between the MCA and MCG methods was generally lower than that between different MCA methods or between different MCG methods. For the studied population, MCG appeared to be more reasonable than MCA in selecting cows as a reference population for higher-density genotype imputation to perform genomic prediction and a genome-wide association study. [ABSTRACT FROM AUTHOR]

GOAT breeds, SHEEP breeds, GOATS, SHEEP breeding, INBREEDING, WILDLIFE conservation, and SHEEP farming

Simple Summary: The inbreeding coefficient is relevant for managing livestock and safeguarding biodiversity, especially in small populations. Small ruminant breeders mainly rely on pedigree information, but genomics is increasingly gaining ground as a tool to face possible pedigree inaccuracies. This study investigates pedigree-based (FPED) and genomic (FROH and FGRM) inbreeding in a representative number of Italian sheep and goat populations. Indeed, even though it has been widely studied in cattle, there is still little knowledge about the relationship between these coefficients in small ruminants, which are characterized by a different population structure, often with unconnected farms. Mean inbreeding values were low, FROH being the highest, with breed differences due to different managements; the correlation between FPED and FROH was the strongest and directly related to pedigree depth. Moreover, we estimated FPED from FROH using a linear regression model. Since massive genotyping is not affordable to small ruminant breeders, it is important to understand the distinction and relationship between differently calculated inbreeding coefficients, also in view of the introduction of genomic enhanced breeding values. Our study highlights the importance of accurate pedigree information and, especially if not obtainable, of genotyping animals. Correct data contribute to mitigate inbreeding depression and loss of genetic variability. The inbreeding coefficient is an important parameter for livestock management. Small ruminant breeders and associations mainly rely on pedigree information, but genomic tools are gaining relevance, overcoming possible pedigree inconsistencies. This study investigates the relationship between pedigree-based and genomic inbreeding in two goat and four sheep dairy breeds. Pedigree and genomic data (medium-density SNPchip) were obtained for 3107 goats and 2511 sheep. We estimated pedigree depth (number of fully traced generations, FullGen) and inbreeding (FPED), as well as two genomic inbreeding indexes, using runs of the homozygosity (FROH) and genomic relationship matrix (FGRM). The correlation between the inbreeding coefficients was assessed. A linear regression model (LRM) was fitted for estimating FPED from FROH. After quality control on genomic data, we retained 5085 animals. Mean inbreeding values were low, with higher FROH than FPED and FGRM. Breed differences can partially depend on different managements. The correlation between FPED and FROH was the highest and directly related to pedigree depth. The best LRM was chosen for FullGen ≥4 and ≥6 for goats and sheep, respectively; after excluding animals with extreme residuals, a new refined regression equation was calculated. Since massive genotyping is not affordable to small ruminant breeders, it is important to understand the distinction and relationship between differently calculated inbreeding coefficients, also in view of the introduction of genomic enhanced breeding values. Our study highlights the importance of accurate pedigree information and, especially if not obtainable, of calculating genomic-based inbreeding coefficients. A better estimation of animals' relatedness contributes to improve animal breeding and conservation. [ABSTRACT FROM AUTHOR]

Cardiovascular, Genetics, Human Genome, Prevention, Aetiology, 2.1 Biological and endogenous factors, Humans, Genome-Wide Association Study, Veterans, Pedigree, Aortic Aneurysm, Thoracic, Aortic Dissection, VA Million Veteran Program, Biological Sciences, Medical and Health Sciences, and Developmental Biology

The current understanding of the genetic determinants of thoracic aortic aneurysms and dissections (TAAD) has largely been informed through studies of rare, Mendelian forms of disease. Here, we conducted a genome-wide association study (GWAS) of TAAD, testing ~25 million DNA sequence variants in 8,626 participants with and 453,043 participants without TAAD in the Million Veteran Program, with replication in an independent sample of 4,459 individuals with and 512,463 without TAAD from six cohorts. We identified 21 TAAD risk loci, 17 of which have not been previously reported. We leverage multiple downstream analytic methods to identify causal TAAD risk genes and cell types and provide human genetic evidence that TAAD is a non-atherosclerotic aortic disorder distinct from other forms of vascular disease. Our results demonstrate that the genetic architecture of TAAD mirrors that of other complex traits and that it is not solely inherited through protein-altering variants of large effect size.

CHRONIC kidney failure, DISEASE progression, CROSS-sectional method, RESEARCH methodology, PATIENTS' attitudes, DESCRIPTIVE statistics, RESEARCH funding, GENETIC techniques, CYSTIC kidney disease, DATA analysis software, GENEALOGY, FAMILY history (Medicine), and EARLY diagnosis

Background: Polycystic kidney disease (PKD), an inheritance disorder which is the fourth leading cause of the end-stage renal disease. The inheritance pattern can be diagnosed and confirmed by pedigree analysis. The aim of the present research was to determine the type and frequency of PKD using pedigree analysis. Materials and Methods: The present research was designed as a cross-sectional descriptive study. Thirty-eight adult Bangladeshi PKD patients were recruited using a selection checklist from the Department of Nephrology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh. Data were collected using a data collection sheet after taking informed written consent. The pedigree was drawn using the genetic pedigree chart creation software f-tree V4.0.6. The percentage frequencies of different types of pedigree were calculated using the Statistical Package for the Social Sciences software, version 23. Results: A total of 24 (63.20%) had a positive family history and 36.80% (14) had no positive family history. All of the patients with a positive family history had vertical transmission; male and female were equally inheriting the gene. Out of these 24 patients, 4.17% (one), 8.33% (two), and 16.67% (four) had a homozygous/heterozygous state, skip generation, and male to male transmission, respectively. Conclusions: Pedigree analysis of PKD patients showed an increased value in early diagnosis and better management and prognosis of the disease. [ABSTRACT FROM AUTHOR]