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- Sampson, Joshua N., et al.
(author)
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Analysis of Heritability and Shared Heritability Based on Genome-Wide Association Studies for 13 Cancer Types
- 2015
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In: Journal of the National Cancer Institute. - : Oxford University Press (OUP). - 0027-8874 .- 1460-2105. ; 107:12
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Journal article (peer-reviewed)abstract
- Background: Studies of related individuals have consistently demonstrated notable familial aggregation of cancer. We aim to estimate the heritability and genetic correlation attributable to the additive effects of common single-nucleotide polymorphisms (SNPs) for cancer at 13 anatomical sites. Methods: Between 2007 and 2014, the US National Cancer Institute has generated data from genome-wide association studies (GWAS) for 49 492 cancer case patients and 34 131 control patients. We apply novel mixed model methodology (GCTA) to this GWAS data to estimate the heritability of individual cancers, as well as the proportion of heritability attributable to cigarette smoking in smoking-related cancers, and the genetic correlation between pairs of cancers. Results: GWAS heritability was statistically significant at nearly all sites, with the estimates of array-based heritability, h(l)(2), on the liability threshold (LT) scale ranging from 0.05 to 0.38. Estimating the combined heritability of multiple smoking characteristics, we calculate that at least 24% (95% confidence interval [CI] = 14% to 37%) and 7% (95% CI = 4% to 11%) of the heritability for lung and bladder cancer, respectively, can be attributed to genetic determinants of smoking. Most pairs of cancers studied did not show evidence of strong genetic correlation. We found only four pairs of cancers with marginally statistically significant correlations, specifically kidney and testes (rho = 0.73, SE = 0.28), diffuse large B-cell lymphoma (DLBCL) and pediatric osteosarcoma (rho = 0.53, SE = 0.21), DLBCL and chronic lymphocytic leukemia (CLL) (rho = 0.51, SE = 0.18), and bladder and lung (rho = 0.35, SE = 0.14). Correlation analysis also indicates that the genetic architecture of lung cancer differs between a smoking population of European ancestry and a nonsmoking Asian population, allowing for the possibility that the genetic etiology for the same disease can vary by population and environmental exposures. Conclusion: Our results provide important insights into the genetic architecture of cancers and suggest new avenues for investigation.
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| 3. |
- Guo, Ying, et al.
(author)
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Researching on the fine structure and admixture of the worldwide chicken population reveal connections between populations and important events in breeding history
- 2021
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In: Evolutionary Applications. - : John Wiley & Sons. - 1752-4571.
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Journal article (peer-reviewed)abstract
- Here, we have evaluated the general genomic structure and diversity and studied the divergence resulting from selection and historical admixture events for a collection of worldwide chicken breeds. In total, 636 genomes (43 populations) were sequenced from chickens of American, Chinese, Indonesian, and European origin. Evaluated populations included wild junglefowl, rural indigenous chickens, breeds that have been widely used to improve modern western poultry populations and current com-mercial stocks bred for efficient meat and egg production. In-depth characteriza-tions of the genome structure and genomic relationships among these populations were performed, and population admixture events were investigated. In addition, the genomic architectures of several domestication traits and central documented events in the recent breeding history were explored. Our results provide detailed insights into the contributions from population admixture events described in the historical literature to the genomic variation in the domestic chicken. In particular, we find that the genomes of modern chicken stocks used for meat production both in eastern (Asia) and western (Europe/US) agriculture are dominated by contributions from heavy Asian breeds. Further, by exploring the link between genomic selective divergence and pigmentation, connections to functional genes feather coloring were confirmed.
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| 4. |
- Ou, Jen-Hsiang, et al.
(author)
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Complex genetic architecture of the chicken Growth1 QTL region
- 2024
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In: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203.
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Journal article (peer-reviewed)abstract
- The genetic complexity of polygenic traits represents a captivating and intricate facet of biological inheritance. Unlike Mendelian traits controlled by a single gene, polygenic traits are influenced by multiple genetic loci, each exerting a modest effect on the trait. This cumulative impact of numerous genes, interactions among them, environmental factors, and epigenetic modifications results in a multifaceted architecture of genetic contributions to complex traits.Given the well-characterized genome, diverse traits, and range of genetic resources, chicken (Gallus gallus) was employed as a model organism to dissect the intricate genetic makeup of a previously identified major Quantitative Trait Loci (QTL) for body weight on chromosome 1.A multigenerational advanced intercross line (AIL) of 3215 chickens whose genomes had been sequenced to an average of 0.4x was analyzed using genome-wide association study (GWAS) and variance-heterogeneity GWAS (vGWAS) to identify markers associated with 8-week body weight. Additionally, epistatic interactions were studied using the natural and orthogonal interaction (NOIA) model.Six genetic modules, two from GWAS and four from vGWAS, were strongly associated with the studied trait. We found evidence of both additive- and non-additive interactions between these modules and constructed a putative local epistasis network for the region. Our screens for functional alleles revealed a missense variant in the gene ribonuclease H2 subunit B (RNASEH2B), which has previously been associated with growth-related traits in chickens and Darwin’s finches. In addition, one of the most strongly associated SNPs identified is located in a non-coding region upstream of the long non-coding RNA, ENSGALG00000053256, previously suggested as a candidate gene for regulating chicken body weight. By studying large numbers of individuals from a family material using approaches to capture both additive and non-additive effects, this study advances our understanding of genetic complexities in a highly polygenic trait and has practical implications for poultry breeding and agriculture.
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| 5. |
- Ou, Jen-Hsiang
(author)
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Exploring the Genetic Landscape of Chicken Populations : Admixture, Growth QTLs, and Long-Term Selection Dynamics
- 2024
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Doctoral thesis (other academic/artistic)abstract
- This thesis analyzes the genetic structure of chicken populations across different breeding histories and environments. Genomic methodologies were used to uncover complex traits and domestication history over time. The work consists of three studies contributing to a broader understanding of chicken genetic diversity and the impact of selective breeding practices.The first study delves into the global chicken population, using genome-wide analysis to uncover the intricate fine structure and historical admixture events that have shaped these populations. The research has unveiled significant connections between populations and pivotal breeding events, highlighting the complex relationships within chicken populations. This study offers intriguing insights into the genetic continuity and admixture patterns across diverse chicken breeds, from junglefowl to commercial lines.The second study focuses on the genetic complexity within a specific quantitative trait locus (QTL) region known as Growth1, which is influential in chicken growth. This study, conducted using an advanced intercross line from the Virginia body weight line, identifies significant additive, haplotype, and epistasis effects within the Growth1 QTL region. The findings challenge simplistic genetic models by demonstrating the involvement of multiple loci in regulating body weight and contribute to understanding complex trait architecture.The third study extends the investigation to the long-term effects of selection on chicken lines, providing a deeper understanding of the genetic mechanisms underlying selection responses. By mapping multiple additive QTLs associated with body weight compared with the GWA study results, several novel regions were determined and are still contributing to the selection response even after 40 generations of intense selection.These different views provide practical insights into chickens' intricate genetic makeup. By analyzing their domestication history, genetic variation effects, and the population's response to selective breeding, we better understand one of the most important economic organisms for humans — the chicken. This understanding can potentially inform and improve selective breeding practices, leading to more efficient and sustainable poultry production.
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| 6. |
- Rönneburg, Tilman, et al.
(author)
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Within-line segregation as contributors to long-term, single-trait selection-responses in the Virginia chicken lines
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Other publication (other academic/artistic)abstract
- Populations display a remarkable capability to adapt under natural or artificial selection, even far beyond the original phenotype range, given intense single trait selection. The genetic mechanisms to facilitate this however, are still unclear. Here we use an Advanced Intercross Line, generated after 40 generations of intense bi-directional selection from a common outbred founder population. in an attempt to quantify the contribution of still segregating variants to the selection response.While the selection response of the founding lines has been extensively profiled within this population, this has been done under the assumption that the most important regions were fixed for divergent alleles between the lines. Investigating beyond this paradigm has been previously hampered due to requirements in power, marker density and number of recombination events. Here we use a large low-coverage sequencing dataset that has been imputed to both founder-line haplotypes as well as dense marker coverage using high-quality, deep coverage sequenced founders. Utilizing this dataset for a multi-locus GWAS approach to contrast with a more traditional cross-QTL methodology, the aim of this study is to identify novel regions that contribute to the phenotype, and assess as to whether and how they contribute to the selection response. Out of 40 (a=890g, 23% of total phenotypic variance) Loci retained in the model, 24 (a=557.5g, 15% of total phenotypic variance) do not overlap known QTL. While some freely segregate between lines, 14 (a=346.6g, 9.3% of total phenotypic variance) of them are fixed in at least one founding line, and likely contribute a significant fraction of the selection response.
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| 7. |
- Rönneburg, Tilman, et al.
(author)
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Within-line segregation as contributors to long-term, single-trait selection responses in the Virginia chicken lines
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Other publication (other academic/artistic)abstract
- Populations display a remarkable capability to adapt under natural or artificial selection, even far beyond the original phenotype range, given intense single trait selection. The genetic mechanisms to facilitate this however, are still unclear. Here we use an Advanced Intercross Line, generated after 40 generations of intense bi-directional selection from a common outbred founder population, in an attempt to quantify the contribution of still segregating variants to the selection response.While the selection response of the founding lines has been extensively profiled within this population, this has been done under the assumption that the most important regions were fixed for divergent alleles between the lines. Investigating beyond this paradigm has been previously hampered due to requirements in power, marker density, and number of recombination events. Here we use a large low-coverage sequencing dataset that has been imputed to both founder-line haplotypes as well as dense marker coverage using high-quality, deep-coverage sequenced founders. Utilizing this dataset for a multi-locus GWAS approach to contrast with a more traditional cross-QTL methodology, the aim of this study is to identify novel regions that contribute to the phenotype and assess whether and how they contribute to the selection response. Out of 40 (a=890g, 23.9% of total phenotypic variance) Loci retained in the multilocus model, 24 (a=557.5g, 15% of total phenotypic variance) do not overlap known QTL. While some freely segregate between lines, 14 (a=346.6g, 9.3% of total phenotypic variance) of them are fixed in at least one founding line, and likely contribute a significant fraction of the selection response.The variance effect prediction result provides a functional view of markers. The RNASEH2B and TBXAS1 genes were considered as candidates, with previous research supporting body weight-related functions. For GALNT7, ENSGALG00000049347, TOM1, ENSGALG00000013583, ENSGALG00000039245, CHD7, and CNTNAP5 genes, the high conservation score provides a clue of being important for biological functions. However, the mechanism by which these genes regulate body weight still remains limited.
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