Flavio S Schenkel

Bio: Flavio S Schenkel is an academic researcher from University of Guelph. The author has contributed to research in topics: Population & Single-nucleotide polymorphism. The author has an hindex of 41, co-authored 290 publications receiving 7877 citations. Previous affiliations of Flavio S Schenkel include University of Rio Grande.

A new approach for efficient genotype imputation using information from relatives

Abstract: Genotype imputation can help reduce genotyping costs particularly for implementation of genomic selection In applications entailing large populations, recovering the genotypes of untyped loci using information from reference individuals that were genotyped with a higher density panel is computationally challenging Popular imputation methods are based upon the Hidden Markov model and have computational constraints due to an intensive sampling process A fast, deterministic approach, which makes use of both family and population information, is presented here All individuals are related and, therefore, share haplotypes which may differ in length and frequency based on their relationships The method starts with family imputation if pedigree information is available, and then exploits close relationships by searching for long haplotype matches in the reference group using overlapping sliding windows The search continues as the window size is shrunk in each chromosome sweep in order to capture more distant relationships The proposed method gave higher or similar imputation accuracy than Beagle and Impute2 in cattle data sets when all available information was used When close relatives of target individuals were present in the reference group, the method resulted in higher accuracy compared to the other two methods even when the pedigree was not used Rare variants were also imputed with higher accuracy Finally, computing requirements were considerably lower than those of Beagle and Impute2 The presented method took 28 minutes to impute from 6 k to 50 k genotypes for 2,000 individuals with a reference size of 64,429 individuals The proposed method efficiently makes use of information from close and distant relatives for accurate genotype imputation In addition to its high imputation accuracy, the method is fast, owing to its deterministic nature and, therefore, it can easily be used in large data sets where the use of other methods is impractical

Association of a single nucleotide polymorphism in the calpastatin gene with carcass and meat quality traits of beef cattle.

Abstract: Studies with different populations are required to properly characterize the robustness of associations of polymorphisms in candidate genes with economically important traits across beef cattle populations before this sort of genetic information can be used efficiently in breeding and management decisions. The objective of this study was to evaluate the association of previously reported SNP in the bovine leptin gene with carcass and meat quality traits from a large sample of crossbred beef cattle. Five SNP (UASMS1, UASMS2, UASMS3, E2JW, and E2FB) were genotyped on 1,111 crossbred bulls, heifers, and steers. The measured traits included fat, lean, and bone yield (%) by partial rib dissection, grade fat, LM area, HCW, quality grade, LM i.m. fat, and tenderness evaluation of LM and semitendinosus muscle. Only four SNP were analyzed (UASMS1, UASMS2, E2JW, and E2FB), because UASMS1 and UASMS3 were completely linked. A univariate mixed-inheritance animal model was used to evaluate the association of either genotypes or haplotypes with the traits. The two leptin exon 2 SNP were associated with fat and lean yield and grade fat (E2JW, P < 0.01; E2FB, P < 0.05), and they interacted in their effect on LM tenderness (P < 0.01). The leptin promoter SNP were either not associated with any of the traits (UASMS2) or with fat yield only (UASMS1). Three haplotypes (TCAC, CCAT, TTAC) were at high frequency in the population (88%) and had similar effects on all the traits. Compared with the common haplotypes, one haplotype (CCTT) showed a significantly different effect on fat and lean yield and grade fat (P < 0.01), and one haplotype (TTTT) had a different effect on LM tenderness (P < 0.03). Therefore, important associations between SNP within the leptin gene with lean yield, fatness (fat yield and subcutaneous fat), and tenderness were detected. Results confirm some of the previously reported associations, but diverge with respect to others, showing that further efforts are required to validate some prospective associations.

QMSim: a large-scale genome simulator for livestock.

Abstract: QMSim was designed to simulate large-scale genotyping data in multiple and complex livestock pedigrees. The simulation is basically carried out in two steps. In the first step, a historical population is simulated to establish mutation-drift equilibrium, and in the second step, recent population structures are generated, which can be very complex. A wide variety of genome architectures, ranging from infinitesimal model to single-locus model, can be simulated. The program is efficient in terms of computing time and memory requirements. Availability: Executable versions of QMSim for Windows and Linux are freely available at http://www.aps.uoguelph.ca/∼msargol/qmsim/. Contact: msargol@uoguelph.ca

Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals.

Abstract: Stature is affected by many polymorphisms of small effect in humans 1 . In contrast, variation in dogs, even within breeds, has been suggested to be largely due to variants in a small number of genes2,3. Here we use data from cattle to compare the genetic architecture of stature to those in humans and dogs. We conducted a meta-analysis for stature using 58,265 cattle from 17 populations with 25.4 million imputed whole-genome sequence variants. Results showed that the genetic architecture of stature in cattle is similar to that in humans, as the lead variants in 163 significantly associated genomic regions (P < 5 × 10-8) explained at most 13.8% of the phenotypic variance. Most of these variants were noncoding, including variants that were also expression quantitative trait loci (eQTLs) and in ChIP-seq peaks. There was significant overlap in loci for stature with humans and dogs, suggesting that a set of common genes regulates body size in mammals.

Human biochemical genetics

Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Hundreds of variants clustered in genomic loci and biological pathways affect human height

Abstract: Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

A User’s Guide

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