scholarly journals Whole-genome sequence-based analysis of thyroid function

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Peter N. Taylor ◽  
◽  
Eleonora Porcu ◽  
Shelby Chew ◽  
Purdey J. Campbell ◽  
...  
Keyword(s):  
Thyroid Function ◽  
Genome Sequence ◽  
Rare Variants ◽  
Sequence Data ◽  
Association Studies ◽  
Meta Analysis ◽  
Low Frequency ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Common Variants

Abstract Normal thyroid function is essential for health, but its genetic architecture remains poorly understood. Here, for the heritable thyroid traits thyrotropin (TSH) and free thyroxine (FT4), we analyse whole-genome sequence data from the UK10K project (N=2,287). Using additional whole-genome sequence and deeply imputed data sets, we report meta-analysis results for common variants (MAF≥1%) associated with TSH and FT4 (N=16,335). For TSH, we identify a novel variant in SYN2 (MAF=23.5%, P=6.15 × 10−9) and a new independent variant in PDE8B (MAF=10.4%, P=5.94 × 10−14). For FT4, we report a low-frequency variant near B4GALT6/SLC25A52 (MAF=3.2%, P=1.27 × 10−9) tagging a rare TTR variant (MAF=0.4%, P=2.14 × 10−11). All common variants explain ≥20% of the variance in TSH and FT4. Analysis of rare variants (MAF<1%) using sequence kernel association testing reveals a novel association with FT4 in NRG1. Our results demonstrate that increased coverage in whole-genome sequence association studies identifies novel variants associated with thyroid function.

scholarly journals A multi-trait meta-analysis with imputed sequence variants reveals twelve QTL for mammary gland morphology in Fleckvieh cattle

2015 ◽  
Author(s):  
Hubert Pausch ◽  
Reiner Emmerling ◽  
Hermann Schwarzenbacher ◽  
Ruedi Fries
Keyword(s):  
Mammary Gland ◽  
Association Analysis ◽  
Genome Sequence ◽  
Sequence Data ◽  
Association Studies ◽  
Whole Genome Sequence ◽  
Sequence Variants ◽  
Whole Genome ◽  
Genome Sequence Data ◽  
Mammary Gland Morphology

Background: The availability of whole-genome sequence data from key ancestors provides an exhaustive catalogue of polymorphic sites segregating within and across cattle breeds. Sequence variants from key ancestors can be imputed in animals that have been genotyped using medium- and high-density genotyping arrays. Association analysis with imputed sequences, particularly if applied to multiple traits simultaneously, is a very powerful approach to revealing candidate causal variants underlying complex phenotypes. Results: We used whole-genome sequence data from 157 key ancestors of the German Fleckvieh population to impute 20 561 798 sequence variants in 10 363 animals that had (partly imputed) array-derived genotypes at 634 109 SNP. The imputed sequence data were enriched for rare variants. Association studies with imputed sequence variants were performed using seven correlated udder conformation traits as response variables. The calculation of an approximate multi-trait test statistic enabled us to detect twelve major QTL (P<2.97 x 10-9) controlling different aspects of mammary gland morphology. Imputed sequence variants were the most significantly associated at eleven QTL, whereas the top association signal at a QTL on BTA14 resulted from an array-derived variant. Seven QTL were associated with multiple phenotypes. Most QTL were located in non-coding regions of the genome in close neighborhood, however, to plausible candidate genes for mammary gland morphology (SP5, GC, NPFFR2, CRIM1, RXFP2, TBX5, RBM19, ADAM12). Conclusions: Association analysis with imputed sequence variants allows QTL characterization at maximum resolution. Multi-trait approaches can reveal QTL that are not detected in single-trait association studies. Most QTL for udder conformation traits were located in non-coding elements of the genome suggesting regulatory mutations to be the major determinants of variation in mammary gland morphology in cattle.

scholarly journals Aquila: diploid personal genome assembly and comprehensive variant detection based on linked reads

2019 ◽  
Author(s):  
Xin Zhou ◽  
Lu Zhang ◽  
Ziming Weng ◽  
David L. Dill ◽  
Arend Sidow
Keyword(s):  
Genetic Variation ◽  
Genome Sequence ◽  
Genome Assembly ◽  
Sequence Data ◽  
Association Studies ◽  
Cost Effective ◽  
Whole Genome Sequence ◽  
Personal Genome ◽  
Whole Genome ◽  
Nucleotide Polymorphisms

AbstractVariant discovery in personal, whole genome sequence data is critical for uncovering the genetic contributions to health and disease. We introduce a new approach, Aquila, that uses linked-read data for generating a high quality diploid genome assembly, from which it then comprehensively detects and phases personal genetic variation. Assemblies cover >95% of the human reference genome, with over 98% in a diploid state. Thus, the assemblies support detection and accurate genotyping of the most prevalent types of human genetic variation, including single nucleotide polymorphisms (SNPs), small insertions and deletions (small indels), and structural variants (SVs), in all but the most difficult regions. All heterozygous variants are phased in blocks that can approach arm-level length. The final output of Aquila is a diploid and phased personal genome sequence, and a phased VCF file that also contains homozygous and a few unphased heterozygous variants. Aquila represents a cost-effective evolution of whole-genome reconstruction that can be applied to cohorts for variation discovery or association studies, or to single individuals with rare phenotypes that could be caused by SVs or compound heterozygosity.

scholarly journals A reference panel of 64,976 haplotypes for genotype imputation

2015 ◽  
Author(s):  
Shane McCarthy ◽  
Sayantan Das ◽  
Warren Kretzschmar ◽  
Olivier Delaneau ◽  
Andrew R. Wood ◽  
...  
Keyword(s):  
Genome Sequence ◽  
Sequence Data ◽  
Association Studies ◽  
Allele Frequencies ◽  
Genotype Imputation ◽  
Reference Panel ◽  
Whole Genome Sequence ◽  
European Ancestry ◽  
Whole Genome ◽  
Remote Server

We describe a reference panel of 64,976 human haplotypes at 39,235,157 SNPs constructed using whole genome sequence data from 20 studies of predominantly European ancestry. Using this resource leads to accurate genotype imputation at minor allele frequencies as low as 0.1%, a large increase in the number of SNPs tested in association studies and can help to discover and refine causal loci. We describe remote server resources that allow researchers to carry out imputation and phasing consistently and efficiently.

scholarly journals Linkage disequilibrium maps for European and African populations constructed from whole genome sequence data

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Alejandra Vergara-Lope ◽  
M. Reza Jabalameli ◽  
Clare Horscroft ◽  
Sarah Ennis ◽  
Andrew Collins ◽  
...  
Keyword(s):  
Linkage Disequilibrium ◽  
Genome Sequence ◽  
Sequence Data ◽  
Association Studies ◽  
Large Population ◽  
Whole Genome Sequence ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Genome Wide

Abstract Quantification of linkage disequilibrium (LD) patterns in the human genome is essential for genome-wide association studies, selection signature mapping and studies of recombination. Whole genome sequence (WGS) data provides optimal source data for this quantification as it is free from biases introduced by the design of array genotyping platforms. The Malécot-Morton model of LD allows the creation of a cumulative map for each choromosome, analogous to an LD form of a linkage map. Here we report LD maps generated from WGS data for a large population of European ancestry, as well as populations of Baganda, Ethiopian and Zulu ancestry. We achieve high average genetic marker densities of 2.3–4.6/kb. These maps show good agreement with prior, low resolution maps and are consistent between populations. Files are provided in BED format to allow researchers to readily utilise this resource.

scholarly journals Methods for Collapsing Multiple Rare Variants in Whole-Genome Sequence Data

2014 ◽  
Vol 38 (S1) ◽  
pp. S13-S20 ◽  
Author(s):  
Yun Ju Sung ◽  
Keegan D. Korthauer ◽  
Michael D. Swartz ◽  
Corinne D. Engelman
Keyword(s):  
Genome Sequence ◽  
Rare Variants ◽  
Sequence Data ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Genome Sequence Data

scholarly journals A conditional multi-trait sequence GWAS discovers pleiotropic candidate genes and variants for sheep wool, skin wrinkle and breech cover traits

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Sunduimijid Bolormaa ◽  
Andrew A. Swan ◽  
Paul Stothard ◽  
Majid Khansefid ◽  
Nasir Moghaddar ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Genome Sequence ◽  
Sequence Data ◽  
Association Studies ◽  
Whole Genome Sequence ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Merino Wool ◽  
Causal Variants ◽  
Wool Proteins

Abstract Background Imputation to whole-genome sequence is now possible in large sheep populations. It is therefore of interest to use this data in genome-wide association studies (GWAS) to investigate putative causal variants and genes that underpin economically important traits. Merino wool is globally sought after for luxury fabrics, but some key wool quality attributes are unfavourably correlated with the characteristic skin wrinkle of Merinos. In turn, skin wrinkle is strongly linked to susceptibility to “fly strike” (Cutaneous myiasis), which is a major welfare issue. Here, we use whole-genome sequence data in a multi-trait GWAS to identify pleiotropic putative causal variants and genes associated with changes in key wool traits and skin wrinkle. Results A stepwise conditional multi-trait GWAS (CM-GWAS) identified putative causal variants and related genes from 178 independent quantitative trait loci (QTL) of 16 wool and skin wrinkle traits, measured on up to 7218 Merino sheep with 31 million imputed whole-genome sequence (WGS) genotypes. Novel candidate gene findings included the MAT1A gene that encodes an enzyme involved in the sulphur metabolism pathway critical to production of wool proteins, and the ESRP1 gene. We also discovered a significant wrinkle variant upstream of the HAS2 gene, which in dogs is associated with the exaggerated skin folds in the Shar-Pei breed. Conclusions The wool and skin wrinkle traits studied here appear to be highly polygenic with many putative candidate variants showing considerable pleiotropy. Our CM-GWAS identified many highly plausible candidate genes for wool traits as well as breech wrinkle and breech area wool cover.

scholarly journals Evaluating the performance of tools used to call minority variants from whole genome short-read data

2018 ◽  
Vol 3 ◽  
pp. 21 ◽  
Author(s):  
Khadija Said Mohammed ◽  
Nelson Kibinge ◽  
Pjotr Prins ◽  
Charles N. Agoti ◽  
Matthew Cotten ◽  
...  
Keyword(s):  
Genome Sequence ◽  
Sequence Data ◽  
False Positive Rate ◽  
Low Frequency ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Short Read ◽  
Genome Sequence Data ◽  
Minority Variants ◽  
Minority Variant

Background: High-throughput whole genome sequencing facilitates investigation of minority virus sub-populations from virus positive samples. Minority variants are useful in understanding within and between host diversity, population dynamics and can potentially assist in elucidating person-person transmission pathways. Several minority variant callers have been developed to describe low frequency sub-populations from whole genome sequence data. These callers differ based on bioinformatics and statistical methods used to discriminate sequencing errors from low-frequency variants. Methods: We evaluated the diagnostic performance and concordance between published minority variant callers used in identifying minority variants from whole-genome sequence data from virus samples. We used the ART-Illumina read simulation tool to generate three artificial short-read datasets of varying coverage and error profiles from an RSV reference genome. The datasets were spiked with nucleotide variants at predetermined positions and frequencies. Variants were called using FreeBayes, LoFreq, Vardict, and VarScan2. The variant callers’ agreement in identifying known variants was quantified using two measures; concordance accuracy and the inter-caller concordance. Results: The variant callers reported differences in identifying minority variants from the datasets. Concordance accuracy and inter-caller concordance were positively correlated with sample coverage. FreeBayes identified the majority of variants although it was characterised by variable sensitivity and precision in addition to a high false positive rate relative to the other minority variant callers and which varied with sample coverage. LoFreq was the most conservative caller. Conclusions: We conducted a performance and concordance evaluation of four minority variant calling tools used to identify and quantify low frequency variants. Inconsistency in the quality of sequenced samples impacts on sensitivity and accuracy of minority variant callers. Our study suggests that combining at least three tools when identifying minority variants is useful in filtering errors when calling low frequency variants.

scholarly journals Evaluating the performance of tools used to call minority variants from whole genome short-read data

2018 ◽  
Vol 3 ◽  
pp. 21
Author(s):  
Khadija Said Mohammed ◽  
Nelson Kibinge ◽  
Pjotr Prins ◽  
Charles N. Agoti ◽  
Matthew Cotten ◽  
...  
Keyword(s):  
Genome Sequence ◽  
Sequence Data ◽  
False Positive Rate ◽  
Low Frequency ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Short Read ◽  
Genome Sequence Data ◽  
Minority Variants ◽  
Minority Variant

Background: High-throughput whole genome sequencing facilitates investigation of minority sub-populations from virus positive samples. Minority variants are useful in understanding within and between host diversity, population dynamics and can potentially help to elucidate person-person transmission chains. Several minority variant callers have been developed to describe the minority variants sub-populations from whole genome sequence data. However, they differ on bioinformatics and statistical approaches used to discriminate sequencing errors from low-frequency variants. Methods: We evaluated the diagnostic performance and concordance between published minority variant callers used in identifying minority variants from whole-genome sequence data. The ART-Illumina read simulation tool was used to generate three artificial short-read datasets of varying coverage and error profiles from an RSV reference genome. The datasets were spiked with nucleotide variants at predetermined positions and frequencies. Variants were called using FreeBayes, LoFreq, Vardict, and VarScan2. The variant callers’ agreement in identifying known variants was quantified using two measures; concordance accuracy and the inter-caller concordance. Results: The variant callers reported differences in identifying minority variants from the datasets. Concordance accuracy and inter-caller concordance were positively correlated with sample coverage. FreeBayes identified majority of the variants although it was characterised by variable sensitivity and precision in addition to a high false positive rate relative to the other minority variant callers and which varied with sample coverage. LoFreq was the most conservative caller. Conclusions: We conducted a performance and concordance evaluation of four minority variant calling tools used to identify and quantify low frequency variants. Inconsistency in the quality of sequenced samples impact on sensitivity and accuracy of minority variant callers. Our study suggests that combining at least three tools when identifying minority variants is useful in filtering errors when calling low frequency variants.

scholarly journals Imputation accuracy to whole-genome sequence in Nellore cattle

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Gerardo A. Fernandes Júnior ◽  
Roberto Carvalheiro ◽  
Henrique N. de Oliveira ◽  
Mehdi Sargolzaei ◽  
Roy Costilla ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Genome Sequence ◽  
Rare Variants ◽  
Imputation Accuracy ◽  
Low Frequency ◽  
Cost Effective ◽  
Target Population ◽  
Genotype Imputation ◽  
Whole Genome Sequence ◽  
Whole Genome

Abstract Background A cost-effective strategy to explore the complete DNA sequence in animals for genetic evaluation purposes is to sequence key ancestors of a population, followed by imputation mechanisms to infer marker genotypes that were not originally reported in a target population of animals genotyped with single nucleotide polymorphism (SNP) panels. The feasibility of this process relies on the accuracy of the genotype imputation in that population, particularly for potential causal mutations which may be at low frequency and either within genes or regulatory regions. The objective of the present study was to investigate the imputation accuracy to the sequence level in a Nellore beef cattle population, including that for variants in annotation classes which are more likely to be functional. Methods Information of 151 key sequenced Nellore sires were used to assess the imputation accuracy from bovine HD BeadChip SNP (~ 777 k) to whole-genome sequence. The choice of the sires aimed at optimizing the imputation accuracy of a genotypic database, comprised of about 10,000 genotyped Nellore animals. Genotype imputation was performed using two computational approaches: FImpute3 and Minimac4 (after using Eagle for phasing). The accuracy of the imputation was evaluated using a fivefold cross-validation scheme and measured by the squared correlation between observed and imputed genotypes, calculated by individual and by SNP. SNPs were classified into a range of annotations, and the accuracy of imputation within each annotation classification was also evaluated. Results High average imputation accuracies per animal were achieved using both FImpute3 (0.94) and Minimac4 (0.95). On average, common variants (minor allele frequency (MAF) > 0.03) were more accurately imputed by Minimac4 and low-frequency variants (MAF ≤ 0.03) were more accurately imputed by FImpute3. The inherent Minimac4 Rsq imputation quality statistic appears to be a good indicator of the empirical Minimac4 imputation accuracy. Both software provided high average SNP-wise imputation accuracy for all classes of biological annotations. Conclusions Our results indicate that imputation to whole-genome sequence is feasible in Nellore beef cattle since high imputation accuracies per individual are expected. SNP-wise imputation accuracy is software-dependent, especially for rare variants. The accuracy of imputation appears to be relatively independent of annotation classification.

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