Identification of Wound Healing/Regeneration Quantitative Trait Loci (QTL) at Multiple Time Points that Explain Seventy Percent of Variance in (MRL/MpJ and SJL/J) Mice F(2) Population

Studies on genetic mechanisms of wound healing in mammals are very few, although injury is a leading cause of the global burden of disease. In this study, we performed a high-density, genome-wide scan using 633 (MRL/MPJ × SJL/J) F(2) intercross at multiple time points (days 15, 21, and 25) to identify quantitative trait loci (QTL) involved in wound healing/regeneration. The hypothesis of the study was that QTL and unique epistatic interactions are involved at each time point to promote wound healing/regeneration. Ten QTL were identified from chromosomes 1, 4, 6, 7, 9, and 13. Of the 10 QTL, eight from chromosomes 1, 4, 6, and 9 were novel as compared to QTL identified in the McBrearty et al. (1998) study. The 10 QTL altogether explained 70% of variance in F(2) mice. The same QTL were identified at each time point, with simple linear correlation between days 15, 21, and 25, showing very high significant relationships (R >0.92, P <0.0001). Unique epistatic interactions were identified at each time point except those from chromosomes 4, 6, 9, and 13 that were found at all three time points, showing that some loci are involved at all the three time points of wound healing (days 15, 21, and 25). Therefore, loci-to-loci interactions may play a major role in wound healing. Information from these studies may help in the identification of genes that could be involved in wound healing/regeneration

Bayesian Shrinkage Estimation of Quantitative Trait Loci Parameters

Mapping multiple QTL is a typical problem of variable selection in an oversaturated model because the potential number of QTL can be substantially larger than the sample size. Currently, model selection is still the most effective approach to mapping multiple QTL, although further research is needed. An alternative approach to analyzing an oversaturated model is the shrinkage estimation in which all candidate variables are included in the model but their estimated effects are forced to shrink toward zero. In contrast to the usual shrinkage estimation where all model effects are shrunk by the same factor, we develop a Bayesian method that allows the shrinkage factor to vary across different effects. The new shrinkage method forces marker intervals that contain no QTL to have estimated effects close to zero whereas intervals containing notable QTL have estimated effects subject to virtually no shrinkage. We demonstrate the method using both simulated and real data for QTL mapping. A simulation experiment with 500 backcross (BC) individuals showed that the method can localize closely linked QTL and QTL with effects as small as 1% of the phenotypic variance of the trait. The method was also used to map QTL responsible for wound healing in a family of a (MRL/MPJ × SJL/J) cross with 633 F(2) mice derived from two inbred lines

A critical evaluation of the effect of population size and phenotypic measurement on QTL detection and localization using a large F2 murine mapping population

Population size and phenotypic measurement are two key factors determining the detection power of quantitative trait loci (QTL) mapping. We evaluated how these two controllable factors quantitatively affect the detection of QTL and their localization using a large F2 murine mapping population and found that three main points emerged from this study. One finding was that the sensitivity of QTL detection significantly decreased as the population size decreased. The decrease in the percentage logarithm of the odd score (LOD score, which is a statistical measure of the likelihood of two loci being lied near each other on a chromosome) can be estimated using the formula 1 - n/N, where n is the smaller and N the larger population size. This empirical formula has several practical implications in QTL mapping. We also found that a population size of 300 seems to be a threshold for the detection of QTL and their localization, which challenges the small population sizes commonly-used in published studies, in excess of 60% of which cite population sizes <300. In addition, it seems that the precision of phenotypic measurement has a limited capacity to affect detection power, which means that quantitative traits that cannot be measured precisely can also be used in QTL mapping for the detection of major QTL

Emergence of FY*A(null) in a Plasmodium vivax-endemic region of Papua New Guinea

In Papua New Guinea (PNG), numerous blood group polymorphisms and hemoglobinopathies characterize the human population. Human genetic polymorphisms of this nature are common in malarious regions, and all four human malaria parasites are holoendemic below 1500 meters in PNG. At this elevation, a prominent condition characterizing Melanesians is α(+)-thalassemia. Interestingly, recent epidemiological surveys have demonstrated that α(+)-thalassemia is associated with increased susceptibility to uncomplicated malaria among young children. It is further proposed that α(+)-thalassemia may facilitate so-called “benign” Plasmodium vivax infection to act later in life as a “natural vaccine” against severe Plasmodium falciparum malaria. Here, in a P. vivax-endemic region of PNG where the resident Abelam-speaking population is characterized by a frequency of α(+)-thalassemia ≥0.98, we have discovered the mutation responsible for erythrocyte Duffy antigen-negativity (Fy[a−b−]) on the FY*A allele. In this study population there were 23 heterozygous and no homozygous individuals bearing this new allele (allele frequency, 23/1062 = 0.022). Flow cytometric analysis illustrated a 2-fold difference in erythroid-specific Fy-antigen expression between heterozygous (FY*A/FY*A(null)) and homozygous (FY*A/FY*A) individuals, suggesting a gene-dosage effect. In further comparisons, we observed a higher prevalence of P. vivax infection in FY*A/FY*A (83/508 = 0.163) compared with FY*A/FY*A(null) (2/23 = 0.087) individuals (odds ratio = 2.05, 95% confidence interval = 0.47–8.91). Emergence of FY*A(null) in this population suggests that P. vivax is involved in selection of this erythroid polymorphism. This mutation would ultimately compromise α(+)-thalassemia/P. vivax-mediated protection against severe P. falciparum malaria

Lessons From Mass-Testing for Coronavirus Disease 2019 in Long-Term Care Facilities for the Elderly in San Francisco.

Coronavirus disease 2019 can cause significant mortality in the elderly in long-term care facilities (LTCF). We describe 4 LTCF outbreaks where mass testing identified a high proportion of asymptomatic infections (4%-41% in healthcare workers and 20%-75% in residents), indicating that symptom-based screening alone is insufficient for monitoring for COVID-19 transmission