The complete linkage disequilibrium test: a test that points to causative mutations underlying quantitative traits

<p>Abstract</p> <p>Background</p> <p>Genetically, SNP that are in complete linkage disequilibrium with the causative SNP cannot be distinguished from the causative SNP. The Complete Linkage Disequilibrium (CLD) test presented here tests whether a SNP is in complete LD with the causative mutation or not. The performance of the CLD test is evaluated in 1000 simulated datasets.</p> <p>Methods</p> <p>The CLD test consists of two steps i.e. analysis I and analysis II. Analysis I consists of an association analysis of the investigated region. The log-likelihood values from analysis I are next ranked in descending order and in analysis II the CLD test evaluates differences in log-likelihood ratios between the best and second best markers. Under the null-hypothesis distribution, the best SNP is in greater LD with the QTL than the second best, while under the alternative-CLD-hypothesis, the best SNP is alike-in-state with the QTL. To find a significance threshold, the test was also performed on data excluding the causative SNP. The 5^th, 10^thand 50^thhighest T_CLDvalue from 1000 replicated analyses were used to control the type-I-error rate of the test at p = 0.005, p = 0.01 and p = 0.05, respectively.</p> <p>Results</p> <p>In a situation where the QTL explained 48% of the phenotypic variance analysis I detected a QTL in 994 replicates (p = 0.001), where 972 were positioned in the correct QTL position. When the causative SNP was excluded from the analysis, 714 replicates detected evidence of a QTL (p = 0.001). In analysis II, the CLD test confirmed 280 causative SNP from 1000 simulations (p = 0.05), i.e. power was 28%. When the effect of the QTL was reduced by doubling the error variance, the power of the test reduced relatively little to 23%. When sequence data were used, the power of the test reduced to 16%. All SNP that were confirmed by the CLD test were positioned in the correct QTL position.</p> <p>Conclusions</p> <p>The CLD test can provide evidence for a causative SNP, but its power may be low in situations with closely linked markers. In such situations, also functional evidence will be needed to definitely conclude whether the SNP is causative or not.</p

The effect of emergency department delays on 30-day mortality in Central Norway

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Comparing population and incident data for optimal air ambulance base locations in Norway

Background: Helicopter emergency medical services are important in many health care systems. Norway has a nationwide physician manned air ambulance service servicing a country with large geographical variations in population density and incident frequencies. The aim of the study was to compare optimal air ambulance base locations using both population and incident data. Methods: We used municipality population and incident data for Norway from 2015. The 428 municipalities had a median (5-95 percentile) of 4675 (940-36,264) inhabitants and 10 (2-38) incidents. Optimal helicopter base locations were estimated using the Maximal Covering Location Problem (MCLP) optimization model, exploring the number and location of bases needed to cover various fractions of the population for time thresholds 30 and 45 min, in green field scenarios and conditioned on the existing base structure. Results: The existing bases covered 96.90% of the population and 91.86% of the incidents for time threshold 45 min. Correlation between municipality population and incident frequencies was -0.0027, and optimal base locations varied markedly between the two data types, particularly when lowering the target time. The optimal solution using population density data put focus on the greater Oslo area, where one third of Norwegians live, while using incident data put focus on low population high incident areas, such as northern Norway and winter sport resorts. Conclusion: Using population density data as a proxy for incident frequency is not recommended, as the two data types lead to different optimal base locations. Lowering the target time increases the sensitivity to choice of data

Effects of the number of markers per haplotype and clustering of haplotypes on the accuracy of QTL mapping and prediction of genomic breeding values

The aim of this paper was to compare the effect of haplotype definition on the precision of QTL-mapping and on the accuracy of predicted genomic breeding values. In a multiple QTL model using identity-by-descent (IBD) probabilities between haplotypes, various haplotype definitions were tested i.e. including 2, 6, 12 or 20 marker alleles and clustering base haplotypes related with an IBD probability of > 0.55, 0.75 or 0.95. Simulated data contained 1100 animals with known genotypes and phenotypes and 1000 animals with known genotypes and unknown phenotypes. Genomes comprising 3 Morgan were simulated and contained 74 polymorphic QTL and 383 polymorphic SNP markers with an average r2 value of 0.14 between adjacent markers. The total number of haplotypes decreased up to 50% when the window size was increased from two to 20 markers and decreased by at least 50% when haplotypes related with an IBD probability of > 0.55 instead of > 0.95 were clustered. An intermediate window size led to more precise QTL mapping. Window size and clustering had a limited effect on the accuracy of predicted total breeding values, ranging from 0.79 to 0.81. Our conclusion is that different optimal window sizes should be used in QTL-mapping versus genome-wide breeding value prediction

Mapping carcass and meat quality QTL on Sus Scrofa chromosome 2 in commercial finishing pigs

Quantitative trait loci (QTL) affecting carcass and meat quality located on SSC2 were identified using variance component methods. A large number of traits involved in meat and carcass quality was detected in a commercial crossbred population: 1855 pigs sired by 17 boars from a synthetic line, which where homozygous (A/A) for IGF2. Using combined linkage and linkage disequilibrium mapping (LDLA), several QTL significantly affecting loin muscle mass, ham weight and ham muscles (outer ham and knuckle ham) and meat quality traits, such as Minolta-L* and -b*, ultimate pH and Japanese colour score were detected. These results agreed well with previous QTL-studies involving SSC2. Since our study is carried out on crossbreds, different QTL may be segregating in the parental lines. To address this question, we compared models with a single QTL-variance component with models allowing for separate sire and dam QTL-variance components. The same QTL were identified using a single QTL variance component model compared to a model allowing for separate variances with minor differences with respect to QTL location. However, the variance component method made it possible to detect QTL segregating in the paternal line (e.g. HAMB), the maternal lines (e.g. Ham) or in both (e.g. pHu). Combining association and linkage information among haplotypes improved slightly the significance of the QTL compared to an analysis using linkage information only

Evaluating the ability of a trauma team activation tool to identify severe injury: a multicentre cohort study

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Fine mapping of multiple QTL using combined linkage and linkage disequilibrium mapping – A comparison of single QTL and multi QTL methods

Two previously described QTL mapping methods, which combine linkage analysis (LA) and linkage disequilibrium analysis (LD), were compared for their ability to detect and map multiple QTL. The methods were tested on five different simulated data sets in which the exact QTL positions were known. Every simulated data set contained two QTL, but the distances between these QTL were varied from 15 to 150 cM. The results show that the single QTL mapping method (LDLA) gave good results as long as the distance between the QTL was large ($>90$ cM). When the distance between the QTL was reduced, the single QTL method had problems positioning the two QTL and tended to position only one QTL, i.e. a “ghost” QTL, in between the two real QTL positions. The multi QTL mapping method (MP-LDLA) gave good results for all evaluated distances between the QTL. For the large distances between the QTL ($>90$ cM) the single QTL method more often positioned the QTL in the correct marker bracket, but considering the broader likelihood peaks of the single point method it could be argued that the multi QTL method was more precise. Since the distances were reduced the multi QTL method was clearly more accurate than the single QTL method. The two methods combine well, and together provide a good tool to position single or multiple QTL in practical situations, where the number of QTL and their positions are unknown