Disruption of<i>HDX</i>gene in premature ovarian failure

Ökten, Gülsen; Güneş, Sezgin; Onat, Onur Emre; Tükün, Ajlan; Özçelık, Tayfun; Koçak, İ̇dris

doi:10.3109/19396368.2013.769028

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…1994 ); and three candidate genes ( DACH2 , DIAPH2 , and HDX ) played an important role in maintaining normal function of the ovary ( Bione et al. 2004 ; Okten et al. 2013 ).…”

Section: Resultsmentioning

confidence: 99%

“…Importantly, the exon region of three genes was found to be altered by pattern-related SVs that were confirmed by PCR and Sanger sequencing: 1) the Highly Divergent Homeobox (HDX) gene (SINE-mediated 3,539-bp deletion in exon 3 of HDX , fig. 5 b ) played an important role in the development of ovarian follicles and regulation of ovarian gene expression ( Okten et al. 2013 ); 2) the Trophinin ( TRO ) gene (132-bp deletion in exon 12 of TRO , fig.…”

Section: Resultsmentioning

confidence: 99%

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PRE-1 Revealed Previous Unknown Introgression Events in Eurasian Boars during the Middle Pleistocene

Zhao

Jiang

et al. 2020

Genome Biology and Evolution

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Abstract Introgression events and population admixture occurred among Sus species across the Eurasian mainland in the Middle Pleistocene, which reflects the local adaption of different populations and contributes to evolutionary novelty. Previous findings on these population introgressions were largely based on extensive genome-wide single nucleotide polymorphism (SNP) information, ignoring structural variants (SVs) as an important alternative resource of genetic variations. Here, we profiled the genome-wide SVs and explored the formation of pattern-related SVs, indicating that PRE1-SS is a recently active subfamily that was strongly associated with introgression events in multiple Asian and European pig populations. As reflected by the three different combination haplotypes from two specific patterns and known phylogenetic relationships in Eurasian boars, we identified the Asian Northern wild pigs as having experienced introgression from European wild boars around 0.5 to 0.2 Mya and having received latitude-related selection. During further exploration of the influence of pattern-related SVs on gene functions, we found substantial sequence changes in 199 intron regions of 54 genes and 3 exon regions of 3 genes (HDX, TRO and SMIM1), implying that the pattern-related SVs were highly related to positive selection and adaption of pigs. Our findings revealed novel introgression events in Eurasian wild boars, providing a timeline of population admixture and divergence across the Eurasian mainland in the Middle Pleistocene.

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“…1994 ); and three candidate genes ( DACH2 , DIAPH2 , and HDX ) played an important role in maintaining normal function of the ovary ( Bione et al. 2004 ; Okten et al. 2013 ).…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

PRE-1 Revealed Previous Unknown Introgression Events in Eurasian Boars during the Middle Pleistocene

Zhao

Jiang

et al. 2020

Genome Biology and Evolution

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“…Genes in these antagonizing regions include PO1FB, ZNF711, APOOL, HDX, DACH2, FAM133A, among others. These genes are associated with different disorders including infertility, reproductive deficiencies, primary ovarian failure [ 57 , 58 , 59 ]. When some of these genes are over-expressed, it can dysregulate the cristae morphology of the mammalian mitochondria [ 60 ].…”

Section: Discussionmentioning

confidence: 99%

Correlation scan: identifying genomic regions that affect genetic correlations applied to fertility traits

et al. 2022

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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.

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“…Genes in these antagonizing regions include PO1FB, ZNF711, APOOL, HDX, DACH2, FAM133A, among others. These genes are associated with different disorders including infertility, reproductive deficiencies, primary ovarian failure [49][50][51]. When some of these genes are over-expressed, it can dysregulate the cristae morphology of the mammalian mitochondria [52].…”

Section: Discussionmentioning

confidence: 99%

Correlation scan: identifying genomic regions that affect genetic correlations applied to fertility traits

Olasege

Porto-Neto

Tahir

et al. 2021

Preprint

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Although the genetic correlation 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, and 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 to develop a novel framework termed correlation scan. This framework was used to identify regions associated with the genetic correlations between male and female fertility traits across the bovine genome. The traits used were age at first corpus luteum (AGECL) and serum levels of insulin growth hormone (IGF1 measured in bulls, IGF1b, or cows, IGF1c). The methodology developed herein used correlations of 500-SNP (single nucleotide polymorphism) effects in a 100-SNPs sliding window in each chromosome to identify regions in the genome that either drive (i.e., SNP effects on the same direction) or antagonize (i.e., SNP effects in the opposite direction) the genetic correlations between traits. We used a permutation test to confirm which regions of the genome harboured significant correlations. Hence, this framework can also identify neutral genomic regions with no effect on the pairwise trait studied. About 40% of the total genomic regions were identified as driving and antagonizing genetic correlations between male and female fertility traits in the two population. These regions confirmed the polygenic nature of the traits being studied and pointed to genes of interest. Quantitative trait loci (QTL) and functional enrichment analysis revealed that many significant windows co-located 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 the chromosome X. These results suggest regions of the chromosome X for further investigation into the trade-offs between male and female fertility. Although the methodology was applied to cattle phenotypes, using high-density SNP genotypes, the general framework developed can be applied to any species or traits, and it can easily accommodate genome sequence data.Author summaryIn animal breeding, it is often common to estimate genetic correlations between economically important traits. These estimated correlations represent the average of the shared genetic similarities between traits across the genome. Despite this knowledge, we are yet to uncover the regions in the genome that explain the genetic correlations estimated. Targeting reproductive traits in cattle, we developed a new framework and used it to identify multiple regions across the genome that affect genetic correlations between male and female fertility traits. While some regions have no effect on these trait correlations, other loci drive or antagonize these relationships. We further subjected the identified regions to functional analysis and annotation for biological insights. Although the methodology was applied to cattle phenotypes, using high-density SNP genotypes, the general framework can be applied to any species or traits. For example, the method could be used to identify genomic regions that explain the interplay between various mental illness phenotypes in humans.

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Disruption ofHDXgene in premature ovarian failure

Cited by 7 publications

References 12 publications

PRE-1 Revealed Previous Unknown Introgression Events in Eurasian Boars during the Middle Pleistocene

PRE-1 Revealed Previous Unknown Introgression Events in Eurasian Boars during the Middle Pleistocene

Correlation scan: identifying genomic regions that affect genetic correlations applied to fertility traits

Correlation scan: identifying genomic regions that affect genetic correlations applied to fertility traits

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