Look Over the Horizon - Chicken Linkage Disequilibrium is Much More Complex Over Much Longer Distance than Previously Appreciated

BackgroundAppreciable Linkage Disequilibrium (LD) is commonly found between pairs of loci close to one another, decreasing rapidly with distance between the loci. This provides the basis studies to map Quantitative Trait Loci Regions (QTLRs), where it is custom to assume that the closest sites to a significant markers are the prime candidate to be the causative mutation. Nevertheless, Long-Range LD (LRLD) can also be found among well-separated sites. LD blocks are runs of genomic sites all having appreciable LD with one another. High LD and LRLD are often separated by genomic sites with which they have practically no LD. Thus, not only can LD be found among distant loci, but also its pattern may be complex, comprised of fragmented blocks. Here, chicken LRLD and LD blocks, and their relationship with previously described Marek’s Disease (MD) QTLRs, were studied in an F6 population from a full-sib advanced intercross line, and in eight commercial pure layer lines. Genome wide LRLD was studied in the F6 population by random samples of non-syntenic and syntenic marker pairs. To illustrate the relationship with QTLRs, LRLD and LD blocks in and between the MD QTLRs were studied by all possible marker pairs.Results LRLD was defined as r2 ≥ 0.7 over a distance ≥ 1 Mb, and 1.5% of all syntenic marker pairs were classified as LRLD. Complex fragmented and interdigitated LD blocks were found, ranging over distances from a few hundred to a few millions bases. Vast high, long-range, and complex LD was found between two of the MD QTLRs. Cross QTLRs STRING networks and gene interactions suggested possible origins of the exceptional LD between these two QTLRs.ConclusionsAll sites with high LD with a significant marker should be considered as candidate for the causative mutation, but, unlike the custom assumption, the causative mutation is not necessarily the one closest to the significant marker. Rather, the present results show that it can be located at a much larger distance from a significant marker than previously appreciated, beyond closer mutations. Thus, LRLD range and LD block complexity must be accounted for while interpreting genetic mapping studies.

Complex and long-range linkage disequilibrium and its relationship with QTL for Marek's Disease resistance in chicken populations

Chicken long-range linkage disequilibrium (LRLD) and LD blocks, and their relationship with previously described Mareks Disease (MD) quantitative trait loci regions (QTLRs), were studied in an F6 population from a full-sib advanced intercross line (FSAIL), and in eight commercial pure layer lines. Genome wide LRLD was studied in the F6 population by random samples of non-syntenic and syntenic marker pairs genotyped by Affymetrix HD 600K SNP array. To illustrate the relationship with QTLRs, LRLD and LD blocks in and between the MD QTLRs were studied by all possible marker pairs of all array markers in the QTLRs, using the same F6 QTLR genotypes and genotypes of the QTLR elements' markers in the eight lines used in the MD mapping study. LRLD was defined as r2 ≥ 0.7 over a distance ≥ 1 Mb, and 1.5% of all syntenic marker pairs were classified as LRLD. Complex fragmented and interdigitated LD blocks were found, over distances ranging from a few hundred to a few million bases. Vast high, long-range, and complex LD was found between two of the MD QTLRs. Cross QTLRs STRING networks and gene interactions suggested possible origins of this exceptional QTLRs' LD. Thus, causative mutations can be located at a much larger distance from a significant marker than previously appreciated. LRLD range and LD block complexity may be used to identify mapping errors, and should be accounted for while interpreting genetic mapping studies. All sites with high LD with a significant marker should be considered as candidate for the causative mutation.

Cosmological evolution of semilocal string networks

Semilocal strings—a particular limit of electroweak strings—are an interesting example of a stable non-topological defect whose properties resemble those of their topological cousins, the Abrikosov–Nielsen–Olesen vortices. There is, however, one important difference: a network of semilocal strings will contain segments. These are ‘dumbbells’ whose ends behave almost like global monopoles that are strongly attracted to one another. While closed loops of string will eventually shrink and disappear, the segments can either shrink or grow, and a cosmological network of semilocal strings will reach a scaling regime. We discuss attempts to find a ‘thermodynamic’ description of the cosmological evolution and scaling of a network of semilocal strings, by analogy with well-known descriptions for cosmic strings and for monopoles. We propose a model for the time evolution of an overall length scale and typical velocity for the network as well as for its segments, and some supporting (preliminary) numerical evidence. This article is part of a discussion meeting issue ‘Topological avatars of new physics’.

Network properties of cancer prognostic genes and their gene sets in the human protein interactome

Background Identifying prognostic genes (PG) is crucial for estimating survival time and providing pinpoint treatments for patients with cancer. However, prognostic genes sets (PGS) reported in most existing research have low reproducibility and overlap ever between the same cancers or their subtypes. Their common characteristic as well as the molecular mechanism of action is still elusive. Methods Here, we obtained nine prognostic gene sets (including 1,439 prognostic genes) of different types of cancer from 23 high quality literatures, and systemically investigated eight network topological properties for PG and PGS compared with background and four other gene sets (cancer gene set CA, essential gene set ES, housekeeping gene set HK, and metastasis-angiogenesis gene set MA) based on the HPRD and String networks. Results The results showed that PG did not occupy key positions in the human protein interactome network, and were more similar to ES rather than CA. Also, PGS had significantly small intraset distance (IAD) and interset distance (IED) in comparison with random sets. Further, we also found that PGS tended to have be distributed within network modules rather than between modules, the functional intersection of the modules enriched with PGS was closely related to cancer. Conclusions Our research reveals the common properties of cancer PG and PGS in the human protein interactome network, and can help us understand and discover cancer prognostic biomarkers.