1
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Kaminow B, Ballouz S, Gillis J, Dobin A. Pan-human consensus genome significantly improves the accuracy of RNA-seq analyses. Genome Res 2022; 32:738-749. [PMID: 35256454 PMCID: PMC8997357 DOI: 10.1101/gr.275613.121] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 03/02/2022] [Indexed: 11/25/2022]
Abstract
The Human Reference Genome serves as the foundation for modern genomic analyses. However, in its present form, it does not adequately represent the vast genetic diversity of the human population. In this study, we explored the consensus genome as a potential successor of the current reference genome and assessed its effect on the accuracy of RNA-seq read alignment. In order to find the best haploid genome representation, we constructed consensus genomes at the pan-human, super-population, and population levels, utilizing variant information from the 1000 Genomes Project. Using personal haploid genomes as the ground truth, we compared mapping errors for real RNA-seq reads aligned to the consensus genomes versus the reference genome. For reads overlapping homozygous variants, we found that the mapping error decreased by a factor of ~2-3 when the reference was replaced with the pan-human consensus genome. We also found that using more population-specific consensuses resulted in little to no increase overusing the pan-human consensus, suggesting a limit in the utility of incorporating more specific genomic variation. Replacing reference with consensus genomes impacts functional analyses, such as differential expressions of isoforms, genes, and splice junctions.
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Affiliation(s)
- Benjamin Kaminow
- Cold Spring Harbor Laboratory; Weill Cornell Graduate School of Medical Sciences
| | - Sara Ballouz
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research; School of Medical Sciences, University of New South Wales; Cold Spring Harbor Laboratory
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2
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Bray D, Hook H, Zhao R, Keenan JL, Penvose A, Osayame Y, Mohaghegh N, Chen X, Parameswaran S, Kottyan LC, Weirauch MT, Siggers T. CASCADE: high-throughput characterization of regulatory complex binding altered by non-coding variants. CELL GENOMICS 2022; 2. [PMID: 35252945 PMCID: PMC8896503 DOI: 10.1016/j.xgen.2022.100098] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Non-coding DNA variants (NCVs) impact gene expression by altering binding sites for regulatory complexes. New high-throughput methods are needed to characterize the impact of NCVs on regulatory complexes. We developed CASCADE (Customizable Approach to Survey Complex Assembly at DNA Elements), an array-based high-throughput method to profile cofactor (COF) recruitment. CASCADE identifies DNA-bound transcription factor-cofactor (TF-COF) complexes in nuclear extracts and quantifies the impact of NCVs on their binding. We demonstrate CASCADE sensitivity in characterizing condition-specific recruitment of COFs p300 and RBBP5 (MLL subunit) to the CXCL10 promoter in lipopolysaccharide (LPS)-stimulated human macrophages and quantify the impact of all possible NCVs. To demonstrate applicability to NCV screens, we profile TF-COF binding to ~1,700 single-nucleotide polymorphism quantitative trait loci (SNP-QTLs) in human macrophages and identify perturbed ETS domain-containing complexes. CASCADE will facilitate high-throughput testing of molecular mechanisms of NCVs for diverse biological applications. Bray et al. develop CASCADE, a method to profile transcription factor (TF)-cofactor (COF) complexes binding to DNA. They demonstrate the approach by profiling complex binding across the CXCL10 cytokine promoter and to ~1,700 single-nucleotide polymorphisms (SNPs). They anticipate that CASCADE can be applied to diverse biological systems to examine regulatory complex binding to DNA.
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Affiliation(s)
- David Bray
- Department of Biology, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
- Bioinformatics Program, Boston University, Boston, MA, USA
| | - Heather Hook
- Department of Biology, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
| | - Rose Zhao
- Department of Biology, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
| | - Jessica L. Keenan
- Department of Biology, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
- Bioinformatics Program, Boston University, Boston, MA, USA
| | - Ashley Penvose
- Department of Biology, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
| | - Yemi Osayame
- Department of Biology, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
| | - Nima Mohaghegh
- Department of Biology, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
| | - Xiaoting Chen
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - Sreeja Parameswaran
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - Leah C. Kottyan
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Allergy and Immunology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio 45229, USA
| | - Matthew T. Weirauch
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Trevor Siggers
- Department of Biology, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
- Corresponding author
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3
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Abstract
The use of the human reference genome has shaped methods and data across modern genomics. This has offered many benefits while creating a few constraints. In the following opinion, we outline the history, properties, and pitfalls of the current human reference genome. In a few illustrative analyses, we focus on its use for variant-calling, highlighting its nearness to a 'type specimen'. We suggest that switching to a consensus reference would offer important advantages over the continued use of the current reference with few disadvantages.
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Affiliation(s)
- Sara Ballouz
- Cold Spring Harbor Laboratory, The Stanley Institute for Cognitive Genomics, Cold Spring Harbor, NY, 11724, USA
| | - Alexander Dobin
- Cold Spring Harbor Laboratory, The Stanley Institute for Cognitive Genomics, Cold Spring Harbor, NY, 11724, USA
| | - Jesse A Gillis
- Cold Spring Harbor Laboratory, The Stanley Institute for Cognitive Genomics, Cold Spring Harbor, NY, 11724, USA.
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4
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Open Chromatin Profiling in Adipose Tissue Marks Genomic Regions with Functional Roles in Cardiometabolic Traits. G3-GENES GENOMES GENETICS 2019; 9:2521-2533. [PMID: 31186305 PMCID: PMC6686932 DOI: 10.1534/g3.119.400294] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Identifying the regulatory mechanisms of genome-wide association study (GWAS) loci affecting adipose tissue has been restricted due to limited characterization of adipose transcriptional regulatory elements. We profiled chromatin accessibility in three frozen human subcutaneous adipose tissue needle biopsies and preadipocytes and adipocytes from the Simpson Golabi-Behmel Syndrome (SGBS) cell strain using an assay for transposase-accessible chromatin (ATAC-seq). We identified 68,571 representative accessible chromatin regions (peaks) across adipose tissue samples (FDR < 5%). GWAS loci for eight cardiometabolic traits were enriched in these peaks (P < 0.005), with the strongest enrichment for waist-hip ratio. Of 110 recently described cardiometabolic GWAS loci colocalized with adipose tissue eQTLs, 59 loci had one or more variants overlapping an adipose tissue peak. Annotated variants at the SNX10 waist-hip ratio locus and the ATP2A1-SH2B1 body mass index locus showed allelic differences in regulatory assays. These adipose tissue accessible chromatin regions elucidate genetic variants that may alter adipose tissue function to impact cardiometabolic traits.
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5
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Hwang KB, Lee IH, Li H, Won DG, Hernandez-Ferrer C, Negron JA, Kong SW. Comparative analysis of whole-genome sequencing pipelines to minimize false negative findings. Sci Rep 2019; 9:3219. [PMID: 30824715 PMCID: PMC6397176 DOI: 10.1038/s41598-019-39108-2] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Accepted: 01/16/2019] [Indexed: 12/30/2022] Open
Abstract
Comprehensive and accurate detection of variants from whole-genome sequencing (WGS) is a strong prerequisite for translational genomic medicine; however, low concordance between analytic pipelines is an outstanding challenge. We processed a European and an African WGS samples with 70 analytic pipelines comprising the combination of 7 short-read aligners and 10 variant calling algorithms (VCAs), and observed remarkable differences in the number of variants called by different pipelines (max/min ratio: 1.3~3.4). The similarity between variant call sets was more closely determined by VCAs rather than by short-read aligners. Remarkably, reported minor allele frequency had a substantial effect on concordance between pipelines (concordance rate ratio: 0.11~0.92; Wald tests, P < 0.001), entailing more discordant results for rare and novel variants. We compared the performance of analytic pipelines and pipeline ensembles using gold-standard variant call sets and the catalog of variants from the 1000 Genomes Project. Notably, a single pipeline using BWA-MEM and GATK-HaplotypeCaller performed comparable to the pipeline ensembles for ‘callable’ regions (~97%) of the human reference genome. While a single pipeline is capable of analyzing common variants in most genomic regions, our findings demonstrated the limitations and challenges in analyzing rare or novel variants, especially for non-European genomes.
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Affiliation(s)
- Kyu-Baek Hwang
- School of Computer Science and Engineering, Soongsil University, Seoul, 06978, Korea
| | - In-Hee Lee
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Honglan Li
- School of Computer Science and Engineering, Soongsil University, Seoul, 06978, Korea
| | - Dhong-Geon Won
- School of Computer Science and Engineering, Soongsil University, Seoul, 06978, Korea
| | - Carles Hernandez-Ferrer
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Jose Alberto Negron
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Sek Won Kong
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, 02115, USA. .,Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA.
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6
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Abstract
Genome-wide association studies (GWASs) have identified thousands of loci associated with hundreds of complex diseases and traits, and progress is being made toward elucidating the causal variants and genes underlying these associations. Functional characterization of mechanisms at GWAS loci is a multi-faceted challenge. Challenges include linkage disequilibrium and allelic heterogeneity at each locus, the noncoding nature of most loci, and the time and cost needed for experimentally evaluating the potential mechanistic contributions of genes and variants. As GWAS sample sizes increase, more loci are identified, and the complexities of individual loci emerge. Loci can consist of multiple association signals, each of which can reflect the influence of multiple variants, inseparable by association analyses. Each signal within a locus can influence the same or different target genes. Experimental studies of genes and variants can differ on the basis of cell type, cellular environment, or other context-specific variables. In this review, we describe the complexity of mechanisms at GWAS loci-including multiple signals, multiple variants, and/or multiple genes-and the implications these complexities hold for experimental study design and interpretation of GWAS mechanisms.
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Affiliation(s)
- Maren E Cannon
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA.
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7
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Harley JB, Chen X, Pujato M, Miller D, Maddox A, Forney C, Magnusen AF, Lynch A, Chetal K, Yukawa M, Barski A, Salomonis N, Kaufman KM, Kottyan LC, Weirauch MT. Transcription factors operate across disease loci, with EBNA2 implicated in autoimmunity. Nat Genet 2018; 50:699-707. [PMID: 29662164 PMCID: PMC6022759 DOI: 10.1038/s41588-018-0102-3] [Citation(s) in RCA: 222] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Accepted: 01/31/2018] [Indexed: 01/10/2023]
Abstract
Explaining the genetics of many diseases is challenging because most associations localize to incompletely characterized regulatory regions. We show that transcription factors (TFs) occupy multiple loci of individual complex genetic disorders using novel computational methods. Application to 213 phenotypes and 1,544 TF binding datasets identifies 2,264 relationships between hundreds of TFs and 94 phenotypes, including AR in prostate cancer and GATA3 in breast cancer. Strikingly, nearly half of the systemic lupus erythematosus risk loci are occupied by the Epstein-Barr virus EBNA2 protein and many co-clustering human TFs, revealing gene-environment interaction. Similar EBNA2-anchored associations exist in multiple sclerosis, rheumatoid arthritis, inflammatory bowel disease, type 1 diabetes, juvenile idiopathic arthritis, and celiac disease. Instances of allele-dependent DNA binding and downstream effects on gene expression at plausibly causal variants support genetic mechanisms dependent upon EBNA2. Our results nominate mechanisms that operate across risk loci within disease phenotypes, suggesting new paradigms for disease origins.
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Affiliation(s)
- John B Harley
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. .,Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. .,Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. .,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA. .,US Department of Veterans Affairs Medical Center, Cincinnati, OH, USA.
| | - Xiaoting Chen
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Mario Pujato
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Daniel Miller
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Avery Maddox
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Carmy Forney
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Albert F Magnusen
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Arthur Lynch
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Kashish Chetal
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Masashi Yukawa
- Division of Allergy & Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Artem Barski
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.,Division of Allergy & Immunology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Nathan Salomonis
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.,Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Kenneth M Kaufman
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.,US Department of Veterans Affairs Medical Center, Cincinnati, OH, USA
| | - Leah C Kottyan
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. .,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
| | - Matthew T Weirauch
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. .,Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. .,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA. .,Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
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8
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Roman TS, Cannon ME, Vadlamudi S, Buchkovich ML, Wolford BN, Welch RP, Morken MA, Kwon GJ, Varshney A, Kursawe R, Wu Y, Jackson AU, Erdos MR, Kuusisto J, Laakso M, Scott LJ, Boehnke M, Collins FS, Parker SCJ, Stitzel ML, Mohlke KL. A Type 2 Diabetes-Associated Functional Regulatory Variant in a Pancreatic Islet Enhancer at the ADCY5 Locus. Diabetes 2017; 66:2521-2530. [PMID: 28684635 PMCID: PMC5860374 DOI: 10.2337/db17-0464] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2017] [Accepted: 06/22/2017] [Indexed: 12/13/2022]
Abstract
Molecular mechanisms remain unknown for most type 2 diabetes genome-wide association study identified loci. Variants associated with type 2 diabetes and fasting glucose levels reside in introns of ADCY5, a gene that encodes adenylate cyclase 5. Adenylate cyclase 5 catalyzes the production of cyclic AMP, which is a second messenger molecule involved in cell signaling and pancreatic β-cell insulin secretion. We demonstrated that type 2 diabetes risk alleles are associated with decreased ADCY5 expression in human islets and examined candidate variants for regulatory function. rs11708067 overlaps a predicted enhancer region in pancreatic islets. The type 2 diabetes risk rs11708067-A allele showed fewer H3K27ac ChIP-seq reads in human islets, lower transcriptional activity in reporter assays in rodent β-cells (rat 832/13 and mouse MIN6), and increased nuclear protein binding compared with the rs11708067-G allele. Homozygous deletion of the orthologous enhancer region in 832/13 cells resulted in a 64% reduction in expression level of Adcy5, but not adjacent gene Sec22a, and a 39% reduction in insulin secretion. Together, these data suggest that rs11708067-A risk allele contributes to type 2 diabetes by disrupting an islet enhancer, which results in reduced ADCY5 expression and impaired insulin secretion.
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Affiliation(s)
- Tamara S Roman
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Maren E Cannon
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | | | - Martin L Buchkovich
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Brooke N Wolford
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Ryan P Welch
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Mario A Morken
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Grace J Kwon
- The Jackson Laboratory for Genomic Medicine, Farmington, CT
- Department of Genetics and Genome Sciences, University of Connecticut Health, Farmington, CT
| | - Arushi Varshney
- Department of Human Genetics, University of Michigan, Ann Arbor, MI
| | - Romy Kursawe
- The Jackson Laboratory for Genomic Medicine, Farmington, CT
| | - Ying Wu
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | | | - Michael R Erdos
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Johanna Kuusisto
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Markku Laakso
- Internal Medicine, Institute of Clinical Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Laura J Scott
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI
| | - Francis S Collins
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Stephen C J Parker
- Department of Human Genetics, University of Michigan, Ann Arbor, MI
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI
| | - Michael L Stitzel
- The Jackson Laboratory for Genomic Medicine, Farmington, CT
- Institute for Systems Genomics, University of Connecticut, Farmington, CT
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
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9
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Roman TS, Marvelle AF, Fogarty MP, Vadlamudi S, Gonzalez AJ, Buchkovich ML, Huyghe JR, Fuchsberger C, Jackson AU, Wu Y, Civelek M, Lusis AJ, Gaulton KJ, Sethupathy P, Kangas AJ, Soininen P, Ala-Korpela M, Kuusisto J, Collins FS, Laakso M, Boehnke M, Mohlke KL. Multiple Hepatic Regulatory Variants at the GALNT2 GWAS Locus Associated with High-Density Lipoprotein Cholesterol. Am J Hum Genet 2015; 97:801-15. [PMID: 26637976 PMCID: PMC4678431 DOI: 10.1016/j.ajhg.2015.10.016] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2015] [Accepted: 10/28/2015] [Indexed: 11/24/2022] Open
Abstract
Genome-wide association studies (GWASs) have identified more than 150 loci associated with blood lipid and cholesterol levels; however, the functional and molecular mechanisms for many associations are unknown. We examined the functional regulatory effects of candidate variants at the GALNT2 locus associated with high-density lipoprotein cholesterol (HDL-C). Fine-mapping and conditional analyses in the METSIM study identified a single locus harboring 25 noncoding variants (r(2) > 0.7 with the lead GWAS variants) strongly associated with total cholesterol in medium-sized HDL (e.g., rs17315646, p = 3.5 × 10(-12)). We used luciferase reporter assays in HepG2 cells to test all 25 variants for allelic differences in regulatory enhancer activity. rs2281721 showed allelic differences in transcriptional activity (75-fold [T] versus 27-fold [C] more than the empty-vector control), as did a separate 780-bp segment containing rs4846913, rs2144300, and rs6143660 (49-fold [AT(-) haplotype] versus 16-fold [CC(+) haplotype] more). Using electrophoretic mobility shift assays, we observed differential CEBPB binding to rs4846913, and we confirmed this binding in a native chromatin context by performing chromatin-immunoprecipitation (ChIP) assays in HepG2 and Huh-7 cell lines of differing genotypes. Additionally, sequence reads in HepG2 DNase-I-hypersensitivity and CEBPB ChIP-seq signals spanning rs4846913 showed significant allelic imbalance. Allelic-expression-imbalance assays performed with RNA from primary human hepatocyte samples and expression-quantitative-trait-locus (eQTL) data in human subcutaneous adipose tissue samples confirmed that alleles associated with increased HDL-C are associated with a modest increase in GALNT2 expression. Together, these data suggest that at least rs4846913 and rs2281721 play key roles in influencing GALNT2 expression at this HDL-C locus.
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Affiliation(s)
- Tamara S Roman
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Curriculum in Genetics and Molecular Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Amanda F Marvelle
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Marie P Fogarty
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Swarooparani Vadlamudi
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Arlene J Gonzalez
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Martin L Buchkovich
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jeroen R Huyghe
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Christian Fuchsberger
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ying Wu
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Mete Civelek
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Biomedical Engineering, Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Aldons J Lusis
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Deparment of Microbiology, Immunology, and Molecular Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Kyle J Gaulton
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Praveen Sethupathy
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Antti J Kangas
- Computational Medicine, Institute of Health Sciences, University of Oulu, 90014 Oulu, Finland
| | - Pasi Soininen
- Computational Medicine, Institute of Health Sciences, University of Oulu, 90014 Oulu, Finland; Nuclear Magnetic Resonance Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, 70211 Kuopio, Finland
| | - Mika Ala-Korpela
- Computational Medicine, Institute of Health Sciences, University of Oulu, 90014 Oulu, Finland; Nuclear Magnetic Resonance Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, 70211 Kuopio, Finland; Oulu University Hospital, 90220 Oulu, Finland; Computational Medicine, School of Social and Community Medicine and Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK
| | - Johanna Kuusisto
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, 70210 Kuopio, Finland
| | - Francis S Collins
- National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, 70210 Kuopio, Finland
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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10
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Yuan S, Johnston HR, Zhang G, Li Y, Hu YJ, Qin ZS. One Size Doesn't Fit All - RefEditor: Building Personalized Diploid Reference Genome to Improve Read Mapping and Genotype Calling in Next Generation Sequencing Studies. PLoS Comput Biol 2015; 11:e1004448. [PMID: 26267278 PMCID: PMC4534450 DOI: 10.1371/journal.pcbi.1004448] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2015] [Accepted: 07/13/2015] [Indexed: 12/13/2022] Open
Abstract
With rapid decline of the sequencing cost, researchers today rush to embrace whole genome sequencing (WGS), or whole exome sequencing (WES) approach as the next powerful tool for relating genetic variants to human diseases and phenotypes. A fundamental step in analyzing WGS and WES data is mapping short sequencing reads back to the reference genome. This is an important issue because incorrectly mapped reads affect the downstream variant discovery, genotype calling and association analysis. Although many read mapping algorithms have been developed, the majority of them uses the universal reference genome and do not take sequence variants into consideration. Given that genetic variants are ubiquitous, it is highly desirable if they can be factored into the read mapping procedure. In this work, we developed a novel strategy that utilizes genotypes obtained a priori to customize the universal haploid reference genome into a personalized diploid reference genome. The new strategy is implemented in a program named RefEditor. When applying RefEditor to real data, we achieved encouraging improvements in read mapping, variant discovery and genotype calling. Compared to standard approaches, RefEditor can significantly increase genotype calling consistency (from 43% to 61% at 4X coverage; from 82% to 92% at 20X coverage) and reduce Mendelian inconsistency across various sequencing depths. Because many WGS and WES studies are conducted on cohorts that have been genotyped using array-based genotyping platforms previously or concurrently, we believe the proposed strategy will be of high value in practice, which can also be applied to the scenario where multiple NGS experiments are conducted on the same cohort. The RefEditor sources are available at https://github.com/superyuan/refeditor.
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Affiliation(s)
- Shuai Yuan
- Mathematics & Computer Science Department, Emory University, Atlanta, Georgia, United States of America
| | - H. Richard Johnston
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Guosheng Zhang
- Department of Genetics, Department of Biostatistics, Department of Computer Science, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Yun Li
- Department of Genetics, Department of Biostatistics, Department of Computer Science, University of North Carolina, Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Yi-Juan Hu
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
| | - Zhaohui S. Qin
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America
- * E-mail:
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