Jeff O'Connell

Bio: Jeff O'Connell is an academic researcher from United States Department of Agriculture. The author has contributed to research in topics: SNP genotyping & Genotyping. The author has an hindex of 2, co-authored 3 publications receiving 885 citations. Previous affiliations of Jeff O'Connell include University of Maryland, Baltimore.

Development and Characterization of a High Density SNP Genotyping Assay for Cattle

Abstract: The success of genome-wide association (GWA) studies for the detection of sequence variation affecting complex traits in human has spurred interest in the use of large-scale high-density single nucleotide polymorphism (SNP) genotyping for the identification of quantitative trait loci (QTL) and for marker-assisted selection in model and agricultural species. A cost-effective and efficient approach for the development of a custom genotyping assay interrogating 54,001 SNP loci to support GWA applications in cattle is described. A novel algorithm for achieving a compressed inter-marker interval distribution proved remarkably successful, with median interval of 37 kb and maximum predicted gap of <350 kb. The assay was tested on a panel of 576 animals from 21 cattle breeds and six outgroup species and revealed that from 39,765 to 46,492 SNP are polymorphic within individual breeds (average minor allele frequency (MAF) ranging from 0.24 to 0.27). The assay also identified 79 putative copy number variants in cattle. Utility for GWA was demonstrated by localizing known variation for coat color and the presence/absence of horns to their correct genomic locations. The combination of SNP selection and the novel spacing algorithm allows an efficient approach for the development of high-density genotyping platforms in species having full or even moderate quality draft sequence. Aspects of the approach can be exploited in species which lack an available genome sequence. The BovineSNP50 assay described here is commercially available from Illumina and provides a robust platform for mapping disease genes and QTL in cattle.

PedHunter 2.0 and its usage to characterize the founder structure of the Old Order Amish of Lancaster County

Abstract: Because they are a closed founder population, the Old Order Amish (OOA) of Lancaster County have been the subject of many medical genetics studies. We constructed four versions of Anabaptist Genealogy Database (AGDB) using three sources of genealogies and multiple updates. In addition, we developed PedHunter, a suite of query software that can solve pedigree-related problems automatically and systematically. We report on how we have used new features in PedHunter to quantify the number and expected genetic contribution of founders to the OOA. The queries and utility of PedHunter programs are illustrated by examples using AGDB in this paper. For example, we calculated the number of founders expected to be contributing genetic material to the present-day living OOA and estimated the mean relative founder representation for each founder. New features in PedHunter also include pedigree trimming and pedigree renumbering, which should prove useful for studying large pedigrees. With PedHunter version 2.0 querying AGDB version 4.0, we identified 34,160 presumed living OOA individuals and connected them into a 14-generation pedigree descending from 554 founders (332 females and 222 males) after trimming. From the analysis of cumulative mean relative founder representation, 128 founders (78 females and 50 males) accounted for over 95% of the mean relative founder contribution among living OOA descendants. The OOA are a closed founder population in which a modest number of founders account for the genetic variation present in the current OOA population. Improvements to the PedHunter software will be useful in future studies of both the OOA and other populations with large and computerized genealogies.

Development and Characterization of a High DensitySNP Genotyping Assay for Cattle

Abstract: The success of genome-wide association (GWA) studies for the detection of sequence variation affecting complex traits in human has spurred interest in the use of large-scale high-density single nucleotide polymorphism (SNP) genotyping for the identification of quantitative trait loci (QTL) and for marker-assisted selection in model and agricultural species. A costeffective and efficient approach for the development of a custom genotyping assay interrogating 54,001 SNP loci to support GWA applications in cattle is described. A novel algorithm for achieving a compressed inter-marker interval distribution proved remarkably successful, with median interval of 37 kb and maximum predicted gap of ,350 kb. The assay was tested on a panel of 576 animals from 21 cattle breeds and six outgroup species and revealed that from 39,765 to 46,492 SNP are polymorphic within individual breeds (average minor allele frequency (MAF) ranging from 0.24 to 0.27). The assay also identified 79 putative copy number variants in cattle. Utility for GWA was demonstrated by localizing known variation for coat color and the presence/absence of horns to their correct genomic locations. The combination of SNP selection and the novel spacing algorithm allows an efficient approach for the development of high-density genotyping platforms in species having full or even moderate quality draft sequence. Aspects of the approach can be exploited in species which lack an available genome sequence. The BovineSNP50 assay described here is commercially available from Illumina and provides a robust platform for mapping disease genes and QTL in cattle. Citation: Matukumalli LK, Lawley CT, Schnabel RD, Taylor JF, Allan MF, et al. (2009) Development and Characterization of a High Density SNP Genotyping Assay for Cattle. PLoS ONE 4(4): e5350. doi:10.1371/journal.pone.0005350 Editor: Amanda Ewart Toland, Ohio State University Medical Center, United States of America Received January 13, 2009; Accepted March 23, 2009; Published April 24, 2009 Copyright: 2009 Matukumalli et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: J.F.T. and R.D.S. were supported by National Research Initiative (NRI) grants 2005-35205-15448, 2005-35604-15615, 2006-35205-16701, 2006-3561616697, 2008-35205-18864 and 2008-35205-04687 from the US Department of Agriculture (USDA) Cooperative State Research, Education and Extension Service (CSREES). C.P.V.T., T.S.S., and L.K.M. were supported by NRI grant 2006-35205-16888 and 2008-35205-04687 from the USDA-CSREES and by Projects 1265-31000098D from the USDA Agricultural Research Service (ARS). T.P.L.S. was supported by Project 5438-31000-073D from the USDA-ARS. L.K.M. was also supported by NRI grant 2006-35205-17878 from the USDA-CSREES. J. R.O. was supported by NRI grant 2006-35205-17878 from the USDA-CSREES and project 1265-31000-09609S from USDA-ARS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: Cynthia T. Lawley is an employee of Illumina Inc. * E-mail: curtvt@ars.usda.gov

Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program.

Abstract: The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1 In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals) These rare variants provide insights into mutational processes and recent human evolutionary history The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 001% The goals, resources and design of the NHLBI Trans-Omics for Precision Medicine (TOPMed) programme are described, and analyses of rare variants detected in the first 53,831 samples provide insights into mutational processes and recent human evolutionary history

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

Design of a High Density SNP Genotyping Assay in the Pig Using SNPs Identified and Characterized by Next Generation Sequencing Technology

Abstract: Background: The dissection of complex traits of economic importance to the pig industry requires the availability of a significant number of genetic markers, such as single nucleotide polymorphisms (SNPs). This study was conducted to discover several hundreds of thousands of porcine SNPs using next generation sequencing technologies and use these SNPs, as well as others from different public sources, to design a high-density SNP genotyping assay. Methodology/Principal Findings: A total of 19 reduced representation libraries derived from four swine breeds (Duroc, Landrace, Large White, Pietrain) and a Wild Boar population and three restriction enzymes (AluI, HaeIII and MspI) were sequenced using Illumina’s Genome Analyzer (GA). The SNP discovery effort resulted in the de novo identification of over 372K SNPs. More than 549K SNPs were used to design the Illumina Porcine 60K+SNP iSelect Beadchip, now commercially available as the PorcineSNP60. A total of 64,232 SNPs were included on the Beadchip. Results from genotyping the 158 individuals used for sequencing showed a high overall SNP call rate (97.5%). Of the 62,621 loci that could be reliably scored, 58,994 were polymorphic yielding a SNP conversion success rate of 94%. The average minor allele frequency (MAF) for all scorable SNPs was 0.274. Conclusions/Significance: Overall, the results of this study indicate the utility of using next generation sequencing technologies to identify large numbers of reliable SNPs. In addition, the validation of the PorcineSNP60 Beadchip demonstrated that the assay is an excellent tool that will likely be used in a variety of future studies in pigs.

Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program

Abstract: Summary paragraph The Trans-Omics for Precision Medicine (TOPMed) program seeks to elucidate the genetic architecture and disease biology of heart, lung, blood, and sleep disorders, with the ultimate goal of improving diagnosis, treatment, and prevention. The initial phases of the program focus on whole genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here, we describe TOPMed goals and design as well as resources and early insights from the sequence data. The resources include a variant browser, a genotype imputation panel, and sharing of genomic and phenotypic data via dbGaP. In 53,581 TOPMed samples, >400 million single-nucleotide and insertion/deletion variants were detected by alignment with the reference genome. Additional novel variants are detectable through assembly of unmapped reads and customized analysis in highly variable loci. Among the >400 million variants detected, 97% have frequency

A large maize (Zea mays L.) SNP genotyping array: development and germplasm genotyping, and genetic mapping to compare with the B73 reference genome.

Abstract: SNP genotyping arrays have been useful for many applications that require a large number of molecular markers such as high-density genetic mapping, genome-wide association studies (GWAS), and genomic selection. We report the establishment of a large maize SNP array and its use for diversity analysis and high density linkage mapping. The markers, taken from more than 800,000 SNPs, were selected to be preferentially located in genes and evenly distributed across the genome. The array was tested with a set of maize germplasm including North American and European inbred lines, parent/F1 combinations, and distantly related teosinte material. A total of 49,585 markers, including 33,417 within 17,520 different genes and 16,168 outside genes, were of good quality for genotyping, with an average failure rate of 4% and rates up to 8% in specific germplasm. To demonstrate this array's use in genetic mapping and for the independent validation of the B73 sequence assembly, two intermated maize recombinant inbred line populations – IBM (B73×Mo17) and LHRF (F2×F252) – were genotyped to establish two high density linkage maps with 20,913 and 14,524 markers respectively. 172 mapped markers were absent in the current B73 assembly and their placement can be used for future improvements of the B73 reference sequence. Colinearity of the genetic and physical maps was mostly conserved with some exceptions that suggest errors in the B73 assembly. Five major regions containing non-colinearities were identified on chromosomes 2, 3, 6, 7 and 9, and are supported by both independent genetic maps. Four additional non-colinear regions were found on the LHRF map only; they may be due to a lower density of IBM markers in those regions or to true structural rearrangements between lines. Given the array's high quality, it will be a valuable resource for maize genetics and many aspects of maize breeding.