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      Identification of candidate genes responsible for innate fear behavior in the chicken


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          Identifying the genes responsible for quantitative traits remains a major challenge. We previously found a major QTL on chromosome 4 affecting several innate fear behavioral traits obtained by an open-field test in an F 2 population between White Leghorn and Nagoya breeds of chickens ( Gallus gallus). Here, an integrated approach of transcriptome, haplotype frequency, and association analyses was used to identify candidate genes for the QTL in phenotypically extreme individuals selected from the same segregating F 2 population as that used in the initial QTL analysis. QTL mapping for the first principal component, which summarizes the variances of all affected behavioral traits in the F 2 population, revealed the behavioral QTL located at 14–35 Mb on chromosome 4 with 333 genes. After RNA-seq analysis using two pooled RNAs from extreme F 2 individuals, real-time qPCR analysis in the two parental breeds and their F 1 individuals greatly reduced the number of candidate genes in the QTL interval from 333 to 16 genes. Haplotype frequency analysis in the two extreme F 2 groups further reduced the number of candidate genes from 16 to 11. After comparing gene expression in the two extreme groups, a conditional correlation analysis of diplotypes between gene expression and phenotype of extreme individuals revealed that NPY5R and LOC101749214 genes were strong candidate genes for innate fear behavior. This study illustrates how the integrated approach can identify candidate genes more rapidly than fine mapping of the initial QTL interval and provides new information for studying the genetic basis of innate fear behavior in chickens.

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          Fast and accurate short read alignment with Burrows–Wheeler transform

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              10 Years of GWAS Discovery: Biology, Function, and Translation.

              Application of the experimental design of genome-wide association studies (GWASs) is now 10 years old (young), and here we review the remarkable range of discoveries it has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics. We predict the likely discoveries in the next 10 years, when GWASs will be based on millions of samples with array data imputed to a large fully sequenced reference panel and on hundreds of thousands of samples with whole-genome sequencing data.

                Author and article information

                G3 (Bethesda)
                G3: Genes|Genomes|Genetics
                Oxford University Press (US )
                February 2023
                01 December 2022
                01 December 2022
                : 13
                : 2
                : jkac316
                Laboratory of Animal Genetics and Breeding, Graduate School of Bioagricultural Sciences, Nagoya University , Chikusa-ku, Nagoya 464-8601, Japan
                Laboratory of Animal Genetics and Breeding, Graduate School of Bioagricultural Sciences, Nagoya University , Chikusa-ku, Nagoya 464-8601, Japan
                Laboratory of Animal Behavior and Physiology, Graduate School of Integrated Sciences for Life, Hiroshima University , Higashi-Hiroshima, Hiroshima 739-8528, Japan
                Laboratory of Animal Genetics and Breeding, Graduate School of Bioagricultural Sciences, Nagoya University , Chikusa-ku, Nagoya 464-8601, Japan
                Author notes
                Corresponding author: Laboratory of Animal Genetics and Breeding, Graduate School of Bioagricultural Sciences, Nagoya University, Chikusa-ku, Nagoya 464-8601, Japan. Email: ishikawa@ 123456agr.nagoya-u.ac.jp

                Conflicts of interest None declared.

                Author information
                © The Author(s) 2022. Published by Oxford University Press on behalf of the Genetics Society of America.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                : 16 November 2022
                : 19 November 2022
                : 11 January 2023
                Page count
                Pages: 11
                Funded by: JSPS, doi 10.13039/501100000646;
                Award ID: 22H02497

                qtl,candidate gene,chicken,innate fear behavior
                qtl, candidate gene, chicken, innate fear behavior


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