Fine mapping a major obesity locus (jObes1) using a Berlin Fat Mouse × B6N advanced intercross population

Arends, Danny; Heise, Sebastian; Kärst, Stefan; Trost, Jan; Brockmann, Gudrun A.

doi:10.1038/ijo.2016.150

Cited by 17 publications

(32 citation statements)

References 21 publications

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“…The SNP density was approximately 290 K, and some minichromosomes were not included. AIL population was commonly used for QTL fine-mapping in animal genetics [28,46–48]. Applying the SNP markers that we identified on our chicken population, we noticed that r 2 in the F 9 generation, which was r 2 0.1 = 3.1 Kb, was substantially lower than the F 0 generation (r 2 0.1 > 50 Kb) (Fig 5).…”

Section: Discussionmentioning

confidence: 87%

Optimized double-digest genotyping by sequencing (ddGBS) method with high-density SNP markers and high genotyping accuracy for chickens

et al. 2017

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High-density single nucleotide polymorphism (SNP) markers are crucial to improve the resolution and accuracy of genome-wide association study (GWAS) and genomic selection (GS). Numerous approaches, including whole genome sequencing, genome sampling sequencing, and SNP chips are able to discover or genotype markers at different densities and costs. Achieving an optimal balance between sequencing resolution and budgets, especially in large-scale population genetics research, constitutes a major challenge. Here, we performed improved double-enzyme digestion genotyping by sequencing (ddGBS) on chicken. We evaluated eight double-enzyme digestion combinations, and EcoR I- Mse I was chosen as the optimal combination for the chicken genome. We firstly proposed that two parameters, optimal read-count point (ORP) and saturated read-count point (SRP), could be utilized to determine the optimal sequencing volume. A total of 291,772 high-density SNPs from 824 animals were identified. By validation using the SNP chip, we found that the consistency between ddGBS data and the SNP chip is over 99%. The approach that we developed in chickens, which is high-quality, high-density, cost-effective (300 K, $30/sample), and time-saving (within 48 h), will have broad applications in animal breeding programs.

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Section: Discussionmentioning

confidence: 87%

Optimized double-digest genotyping by sequencing (ddGBS) method with high-density SNP markers and high genotyping accuracy for chickens

et al. 2017

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“…We have a long track record of developing genotyping arrays for the laboratory mouse, from the Mouse Diversity Array (MDA, Yang et al 2009) to the previous versions of the Mouse Universal Genotyping Array (MUGA, ). These tools were originally designed for the genetic characterization of two popular genetic reference populations, the Collaborative Cross (CC) and the Diversity Outbred (DO), and then used in experiments involving other laboratory strains as well as wild mice (Yang et al 2011;Collaborative Cross Consortium 2012;Carbonetto et al 2014;Arends et al 2016;Didion et al 2016;Shorter et al 2017;Srivastava et al 2017;Rosshart et al 2017;Veale et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Content and Performance of the MiniMUGA Genotyping Array: A New Tool To Improve Rigor and Reproducibility in Mouse Research

et al. 2020

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The laboratory mouse is the most widely used animal model for biomedical research, due in part to its well annotated genome, wealth of genetic resources and the ability to precisely manipulate its genome. Despite the importance of genetics for mouse research, genetic quality control (QC) is not standardized, in part due to the lack of cost effective, informative and robust platforms. Genotyping arrays are standard tools for mouse research and remain an attractive alternative even in the era of high-throughput whole genome sequencing. Here we describe the content and performance of a new iteration of the Mouse Universal Genotyping Array, MiniMUGA, an array-based genetic QC platform with over 11,000 probes. In addition to robust discrimination between most classical and wild-derived laboratory strains, MiniMUGA was designed to contain features not available in other platforms: 1) chromosomal sex determination, 2) discrimination between substrains from multiple commercial vendors, 3) diagnostic SNPs for popular laboratory strains, 4) detection of constructs used in genetically engineered mice, and 5) an easy-to-interpret report summarizing these results. In-depth annotation of all probes should facilitate custom analyses by individual researchers. To determine the performance of MiniMUGA we genotyped 6,899 samples from a wide variety of genetic backgrounds. The performance of MiniMUGA compares favorably with three previous iterations of the MUGA family of arrays both in discrimination capabilities and robustness. We have generated publicly available consensus genotypes for 241 inbred strains including classical, wild-derived and recombinant inbred lines. Here we also report the detection of a substantial number of XO and XXY individuals across a variety of sample types, new markers that expand the utility of reduced complexity crosses to genetic backgrounds other than C57BL/6, and the robust detection of 17 genetic constructs. We provide preliminary evidence that the array can be used to identify both partial sex chromosome duplication and mosaicism, and that diagnostic SNPs can be used to determine how long inbred mice have been bred independently from the relevant main stock. We conclude that MiniMUGA is a valuable platform for genetic QC and an important new tool to the increase rigor and reproducibility of mouse research.

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“…To date, AIL has been used as a common strategy to improve the mapping resolution for the genome wide association studies (GWASs) of model animals, such as fruit flies ( Mackay et al, 2012 ), mice ( Gonzales et al, 2018 ), chicken ( Zan et al, 2017 ), and C. elegans ( Doitsidou et al, 2016 ). The significant advantages of AILs include reducing the QTL confidence interval by 3- to 27-fold and finely splitting the original QTL into two linked QTLs ( Besnier et al, 2011 ; Parker et al, 2014 ; Arends et al, 2016 ). However, we should always pay attention to the tradeoff between mapping resolution and statistical power, as the causal allele may become rare with a continuous increase of the inbreeding coefficient in the AIL ( Yalcin et al, 2010 ; Parker et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Genetic Dissection of Growth Traits in a Unique Chicken Advanced Intercross Line

Wang

Cao

et al. 2020

Front. Genet.

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The advanced intercross line (AIL) that is created by successive generations of pseudorandom mating after the F 2 generation is a valuable resource, especially in agricultural livestock and poultry species, because it improves the precision of quantitative trait loci (QTL) mapping compared with traditional association populations by introducing more recombination events. The growth traits of broilers have significant economic value in the chicken industry, and many QTLs affecting growth traits have been identified, especially on chromosomes 1, 4, and 27, albeit with large confidence intervals that potentially contain dozens of genes. To promote a better understanding of the underlying genetic architecture of growth trait differences, specifically body weight and bone development, in this study, we report a nine-generation AIL derived from two divergent outbred lines: High Quality chicken Line A (HQLA) and Huiyang Bearded (HB) chicken. We evaluate the genetic architecture of the F 0 , F 2 , F 8 , and F 9 generations of AIL and demonstrate that the population of the F 9 generation sufficiently randomized the founder genomes and has the characteristics of rapid linkage disequilibrium decay, limited allele frequency decline, and abundant nucleotide diversity. This AIL yielded a much narrower QTL than the F 2 generations, especially the QTL on chromosome 27, which was reduced to 120 Kb. An ancestral haplotype association analysis showed that most of the dominant haplotypes are inherited from HQLA but with fluctuation of the effects between them. We highlight the important role of four candidate genes (PHOSPHO1, IGF2BP1, ZNF652, and GIP) in bone growth. We also retrieved a missing QTL from AIL on chromosome 4 by identifying the founder selection signatures, which are explained by the loss of association power that results from rare alleles. Our study provides a reasonable resource for detecting quantitative trait genes and tracking ancestor history and will facilitate our understanding of the genetic mechanisms underlying chicken bone growth.

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Fine mapping a major obesity locus (jObes1) using a Berlin Fat Mouse × B6N advanced intercross population

Cited by 17 publications

References 21 publications

Optimized double-digest genotyping by sequencing (ddGBS) method with high-density SNP markers and high genotyping accuracy for chickens

Optimized double-digest genotyping by sequencing (ddGBS) method with high-density SNP markers and high genotyping accuracy for chickens

Content and Performance of the MiniMUGA Genotyping Array: A New Tool To Improve Rigor and Reproducibility in Mouse Research

Genetic Dissection of Growth Traits in a Unique Chicken Advanced Intercross Line

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