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Goldilocks: a tool for identifying genomic regions that are 'just right'.

ABSTRACT: : We present Goldilocks: a Python package providing functionality for collecting summary statistics, identifying shifts in variation, discovering outlier regions and locating and extracting interesting regions from one or more arbitrary genomes for further analysis, for a user-provided definition of interesting.Goldilocks is freely available open-source software distributed under the MIT licence. Source code is hosted publicly at https://github.com/SamStudio8/goldilocks and the package may also be installed using pip install goldilocks. Documentation can be found at https://goldilocks.readthedocs.org: msn@aber.ac.ukSupplementary data are available at Bioinformatics online.

SUBMITTER: Nicholls SM

PROVIDER: S-EPMC4920124 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Goldilocks: a tool for identifying genomic regions that are 'just right'.

Nicholls Samuel M SM Clare Amanda A Randall Joshua C JC

Bioinformatics (Oxford, England) 20160307 13

<h4>Unlabelled</h4>: We present Goldilocks: a Python package providing functionality for collecting summary statistics, identifying shifts in variation, discovering outlier regions and locating and extracting interesting regions from one or more arbitrary genomes for further analysis, for a user-provided definition of interesting.<h4>Availability and implementation</h4>Goldilocks is freely available open-source software distributed under the MIT licence. Source code is hosted publicly at https:/ ...[more]

PMID: 27153673

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Project description:Although the genetic correlations between complex traits have been estimated for more than a century, only recently we have started to map and understand the precise localization of the genomic region(s) that underpin these correlations. Reproductive traits are often genetically correlated. Yet, we don't fully understand the complexities, synergism, or trade-offs between male and female fertility. In this study, we used reproductive traits in two cattle populations (Brahman; BB, Tropical Composite; TC) to develop a novel framework termed correlation scan (CS). This framework was used to identify local regions associated with the genetic correlations between male and female fertility traits. Animals were genotyped with bovine high-density single nucleotide polymorphisms (SNPs) chip assay. The data used consisted of ~1000 individual records measured through frequent ovarian scanning for age at first corpus luteum (AGECL) and a laboratory assay for serum levels of insulin growth hormone (IGF1 measured in bulls, IGF1b, or cows, IGF1c). The methodology developed herein used correlations of 500-SNP effects in a 100-SNPs sliding window in each chromosome to identify local genomic regions that either drive or antagonize the genetic correlations between traits. We used Fisher's Z-statistics through a permutation method to confirm which regions of the genome harboured significant correlations. About 30% of the total genomic regions were identified as driving and antagonizing genetic correlations between male and female fertility traits in the two populations. These regions confirmed the polygenic nature of the traits being studied and pointed to genes of interest. For BB, the most important chromosome in terms of local regions is often located on bovine chromosome (BTA) 14. However, the important regions are spread across few different BTA's in TC. Quantitative trait loci (QTLs) and functional enrichment analysis revealed many significant windows co-localized with known QTLs related to milk production and fertility traits, especially puberty. In general, the enriched reproductive QTLs driving the genetic correlations between male and female fertility are the same for both cattle populations, while the antagonizing regions were population specific. Moreover, most of the antagonizing regions were mapped to chromosome X. These results suggest regions of chromosome X for further investigation into the trade-offs between male and female fertility. We compared the CS with two other recently proposed methods that map local genomic correlations. Some genomic regions were significant across methods. Yet, many significant regions identified with the CS were overlooked by other methods.

Dataset Information

Goldilocks: a tool for identifying genomic regions that are 'just right'.

Publications

Goldilocks: a tool for identifying genomic regions that are 'just right'.

Similar Datasets

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