scholarly journals Robust Estimation of Experimentwise P Values Applied to a Genome Scan of Multiple Asthma Traits Identifies a New Region of Significant Linkage on Chromosome 20q13

2005 ◽  
Vol 77 (6) ◽  
pp. 1075-1085 ◽  
Author(s):  
Manuel A.R. Ferreira ◽  
Louise O'Gorman ◽  
Peter Le Souëf ◽  
Paul R. Burton ◽  
Brett G. Toelle ◽  
...  
2020 ◽  
Vol 48 (22) ◽  
pp. 12604-12617
Author(s):  
Pengpeng Long ◽  
Lu Zhang ◽  
Bin Huang ◽  
Quan Chen ◽  
Haiyan Liu

Abstract We report an approach to predict DNA specificity of the tetracycline repressor (TetR) family transcription regulators (TFRs). First, a genome sequence-based method was streamlined with quantitative P-values defined to filter out reliable predictions. Then, a framework was introduced to incorporate structural data and to train a statistical energy function to score the pairing between TFR and TFR binding site (TFBS) based on sequences. The predictions benchmarked against experiments, TFBSs for 29 out of 30 TFRs were correctly predicted by either the genome sequence-based or the statistical energy-based method. Using P-values or Z-scores as indicators, we estimate that 59.6% of TFRs are covered with relatively reliable predictions by at least one of the two methods, while only 28.7% are covered by the genome sequence-based method alone. Our approach predicts a large number of new TFBs which cannot be correctly retrieved from public databases such as FootprintDB. High-throughput experimental assays suggest that the statistical energy can model the TFBSs of a significant number of TFRs reliably. Thus the energy function may be applied to explore for new TFBSs in respective genomes. It is possible to extend our approach to other transcriptional factor families with sufficient structural information.


animal ◽  
2019 ◽  
Vol 13 (4) ◽  
pp. 683-693 ◽  
Author(s):  
Z. Wang ◽  
H. Sun ◽  
Q. Chen ◽  
X. Zhang ◽  
Q. Wang ◽  
...  

2015 ◽  
Vol 23 ◽  
pp. 77-86 ◽  
Author(s):  
Román Vilas ◽  
Sara G. Vandamme ◽  
Manuel Vera ◽  
Carmen Bouza ◽  
Gregory E. Maes ◽  
...  

PLoS ONE ◽  
2011 ◽  
Vol 6 (6) ◽  
pp. e21158 ◽  
Author(s):  
Elsa García-Gámez ◽  
Antonio Reverter ◽  
Vicki Whan ◽  
Sean M. McWilliam ◽  
Juan José Arranz ◽  
...  
Keyword(s):  

2006 ◽  
Vol 26 (1) ◽  
pp. 46-54 ◽  
Author(s):  
Philip Hanlon ◽  
William Andrew Lorenz ◽  
Zhihong Shao ◽  
James M. Harper ◽  
Andrzej T. Galecki ◽  
...  

A previous analysis of serum insulin-like growth factor I (IGF-I) levels in a mouse population ( n = 961) derived from a cross of (BALB/cJ × C57BL/6J) F1 females and (C3H/HeJ × DBA/2J) F1 males documented quantitative trait loci (QTL) on chromosomes 1, 10, and 17. We employed a newly developed, random walk-based method to search for three- and four-way allelic combinations that might influence IGF-I levels through nonadditive (conditional or epistatic) interactions among 185 genotyped biallelic loci and with significance defined by experiment-wide permutation ( P < 0.05). We documented a three-locus combination in which an epistatic interaction between QTL on paternal-derived chromosomes 5 and 18 had an opposite effect on the phenotype based on the allele inherited at a third locus on maternal-derived chromosome 17. The search also revealed three four-locus combinations that influence IGF-I levels through nonadditive genetic interactions. In two cases, the four-allele combinations were associated with animals having high levels of IGF-I, and, in the third case, a four-allele combination was associated with animals having low IGF-I levels. The multiple-locus genome scan algorithm revealed new IGF-I QTL on chromosomes 2, 4, 5, 7, 8, and 12 that had not been detected in the single-locus genome search and showed that levels of this hormone can be regulated by complex, nonadditive interactions among multiple loci. The analysis method can detect multilocus interactions in a genome scan experiment and may provide new ways to explore the genetic architecture of complex physiological phenotypes.


2000 ◽  
Vol 165 (9) ◽  
pp. 5278-5286 ◽  
Author(s):  
Jeffrey M. Otto ◽  
Raman Chandrasekeran ◽  
Csaba Vermes ◽  
Katalin Mikecz ◽  
Alison Finnegan ◽  
...  

2006 ◽  
Vol 27 (2) ◽  
pp. 103-107 ◽  
Author(s):  
Timo A. Lakka ◽  
Tuomo Rankinen ◽  
Treva Rice ◽  
Arthur S. Leon ◽  
D. C. Rao ◽  
...  

C-reactive protein (CRP) is a sensitive marker of systemic low-grade inflammation. Increased plasma levels of CRP predict the risk of cardiovascular and metabolic diseases. Although genetic factors account for 30–40% of individual differences in plasma CRP levels, genomic regions contributing to CRP levels remain unknown. We performed a genome-wide linkage scan for plasma CRP levels in healthy whites from the HERITAGE Family Study. CRP was measured with a high-sensitivity assay. Multipoint linkage analyses were performed in 280 sibling pairs with 654 markers using regression and variance components-based methods. Data were adjusted for independent correlates of plasma CRP. We showed the strongest evidence of linkage for plasma CRP levels on chromosome 20q13. Markers which gave suggestive linkages in this region were D20S52 [logarithm of odds (LOD) score 3.18, P = 0.00006], D20S857 (LOD score 2.87, P = 0.00014), D20S869 (LOD score 2.75, P = 0.0002), D20S480 (LOD score 2.59, P = 0.0003), D20S501 (LOD score 2.55, P = 0.0003), D20S840 (LOD score 2.18, P = 0.0008), and D20S876 (LOD score 2.07, P = 0.001). We also detected suggestive linkage on chromosome 5p13 for marker D5S1470 (LOD score 2.23, P = 0.0007). Chromosome 20q13 may contribute to plasma CRP levels in healthy whites. This region contains genes that are important in the inflammatory process and may play a role in the development of chronic inflammatory diseases. The present findings may be useful in the ongoing effort to search for genes contributing to inflammation and to identify individuals at an increased risk of chronic inflammatory diseases.


Author(s):  
Benjamin M. Neale ◽  
Patrick F. Sullivan ◽  
Kenneth S. Kendler
Keyword(s):  

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