Mapping Quantitative Trait Loci Controlling the Early Height Growth of Longleaf Pine and Slash Pine.

The delay in early height growth (EHG) known as the grass stage has been one important factor that limits the artificial regeneration of longleaf pine (Pinus palstris Mill.). Genetic improvement of the grass stage by interspecific hybridization between longleaf pine and slash pine (Pinus elliottii Engelm) followed by recurrent backcrosses aiming at the introgression of genes controlling the EHG from slash pine into longleaf pine may be a solution. Developing markers tightly linked to these genes and using them in backcross breeding programs may speed the process of the introgression. Random amplified polymorphic DNA (RAPD) markers were employed to map the genorne of longleaf pine and slash pine in a (longleaf pine x slash pine) x slash pine BC1 family consisting of 258 progeny. A total of 266 RAPD markers were identified for both the F1 parent and the slash pine parent. One hundred and thirteen of the 150 F1 parent-specific markers were mapped into 17 linkage groups covering a genetic distance of 1338.2cM. Eighty-three of the 116 slash pine parent-specific markers were mapped into 19 linkage groups covering a genetic distance of 994.6cM. Single marker regression and MapMaker/QTL were used to detect QTLs. The two methods gave similar results. By using MapMaker/QTL, a total of 19 putative QTLs were detected for 6 height growth measurements and 6 collar diameter measurements at three growth stages using a LOD threshold of 2.0. Seventeen of the 19 putative QTLs were from the F1 parent and only two were from the slash pine parent. The amount of phenotypic variance explained by the putative QTLs ranged from 3.6 to 11.0%. The derivation of sequence characterized amplified region (SCAR) markers from random amplified polymorphic DNAs (RAPDs) were demonstrated to be feasible. Nine RAPD fragments that segregate in a longleaf pine x slash pine F1 family were cloned and end sequenced. A total of 13 SCAR primer pairs, with lengths between 17 and 24 nucleotides, were developed. Six of the 13 SCARs were found to be polymorphic. The segregation of four of the six polymorphic SCARs was confirmed in 64 longleaf x slash F1 individuals

Which mouse multiparental population is right for your study? The Collaborative Cross inbred strains, their F1 hybrids, or the Diversity Outbred population.

Multiparental populations (MPPs) encompass greater genetic diversity than traditional experimental crosses of two inbred strains, enabling broader surveys of genetic variation underlying complex traits. Two such mouse MPPs are the Collaborative Cross (CC) inbred panel and the Diversity Outbred (DO) population, which are descended from the same eight inbred strains. Additionally, the F1 intercrosses of CC strains (CC-RIX) have been used and enable study designs with replicate outbred mice. Genetic analyses commonly used by researchers to investigate complex traits in these populations include characterizing how heritable a trait is, i.e. its heritability, and mapping its underlying genetic loci, i.e. its quantitative trait loci (QTLs). Here we evaluate the relative merits of these populations for these tasks through simulation, as well as provide recommendations for performing the quantitative genetic analyses. We find that sample populations that include replicate animals, as possible with the CC and CC-RIX, provide more efficient and precise estimates of heritability. We report QTL mapping power curves for the CC, CC-RIX, and DO across a range of QTL effect sizes and polygenic backgrounds for samples of 174 and 500 mice. The utility of replicate animals in the CC and CC-RIX for mapping QTLs rapidly decreased as traits became more polygenic. Only large sample populations of 500 DO mice were well-powered to detect smaller effect loci (7.5-10%) for highly complex traits (80% polygenic background). All results were generated with our R package musppr, which we developed to simulate data from these MPPs and evaluate genetic analyses from user-provided genotypes

Selective improvement of rainbow trout: assessment of potential in UK strains.

The research assessed the potential of developing a selective breeding programme for the UK rainbow trout industry. Levels of genetic variation at 12 microsatellite loci were first compared in seven different commercial strains. The Observed heterozygosity ranged from Ho = 48.1% in a gold rainbow trout strain (GTR) to Ho = 66.4% in a newly derived broodstock population constructed from a number of different sources (GIT). The Expected Heterozygosity (He) was highest in GIM1 (He= 79.5%) and lowest in the GTR strain (He = 56.9%). The Effective number of alleles (Mae) showed that the GIM1, GIM2, GIM3, and GIT strain (5.4; 5.2; 4.8; 4.2) were significantly more variable than the other strains and that GTR strain had the lowest value (2.5). There appears to be substantial genetic variability within the commercial United Kingdom rainbow trout strains surveyed in this study. This appears to be the case despite very different management histories and levels of record keeping. The strains appear to be genetically distinct (based on population genetic analyses), though the reasons for this remain unclear (and possibly unanswerable given the poor records kept by the different companies). The Glenwyllin farm strains (GIM) were chosen to form the base population for the project because of their high genetic variability, disease free status and because the farm produced around 20 million ova per year, so any genetic gains would have a widespread impact. The farm has an early (Strain A) and a late spawning (Strain B) and these were mated in a partial factorial design, 20 females and 20 neomales per strain (A & B) were chosen on the basis of maturity and gamete quality in November 2002 so that each male was crossed to 4 females (2 in the same strain and 2 in the other), a total of 160 families were created. All broodstock were biopsied to enable them to be genotyped. The families were reared separately up to the eyed stage at which point the eggs from each family were divided into three to generate three communal replicate populations. One of these was sent to a fingerling producer (Iwerne Spring) for ongrowing to fingerling size and formed the basis of a commercial production trial at Test Valley Trout farm (TVT) in Hampshire. When the fish reached an average weight of 5 g they were transferred from Iwerne Spring to TVT and 1500 were randomly selected, PIT tagged and biopsied to enable them to be assigned to their family using 11 multiplexed microsatellite loci. Parental assignment was based on exclusion (FAP) but the results were compared with another parental assignment based on likelihood (PAPA). Of the 1500 offspring (OIM) PIT tagged 1242 82.8% could be assigned to a single family utilizing different combinations of more than 6 loci (6 to 11). The growth of the 1500 OIM fish was tracked throughout the grow out period before they were finally harvested and fully processed. The results of OIM strain at the end of the trial period were mean weight of 415.5 g, and a mean length of 314.5 mm. The visual measurement of colour gave a mean flesh colour values of 26.01 on the 20-34 scale (SalmoFan™), and 11.0 with the colotimetry evaluation of colour (a*). The heritability results for the IOM strain were 43 ± 9% for weight, 42 ± 9% for gutted, and 28 ± 8% for length. The heritability estimates for the visual colour variables were 19 ± 7% and when using the colorimeter, the red chromaticity (a*) heritability was 14 ± 6%. Therefore, the heritability results of the IOM strain indicate that there are opportunities of substantial and rapid improvement of the growth rate and flesh colour traits. Also no line effects were observed or indications of non-additive genetic variation. In contrast to these last results, the overall survival of the GIM strain from the time of the physical tagging with PIT until harvest was 52.8%, and survival heritability was extremely low, 3 ± 2%, hardly significant

Identification of quantitative trait loci influencing early height growth in longleaf pine (Pinus palustris Mill)

The delay in early height growth (EHG) has been a limiting factor for artificial regeneration of longleaf pine (Pinus palustris Mill.). Simple Sequence Repeat (SSR) markers have been used to map the genome and quantitative trait loci controlling the EHG in a backcross family (longleaf pine x slash pine) x longleaf pine. A total of 228 locus specific SSR markers were screened against 6 longleaf pine recurrent parents and a sample of 7 longlef x slash pine hybrid parents. In total, 135 polymorphic markers were identified. Based on the genetic variance in EHG, available sample size, and the number of SSR marker polymorphisms, a half-sib family with a common paternal parent (Derr488) and 6 longleaf maternal parents were selected from 27 backcross families as the final mapping population. One hundred and twenty three (123) polymorphic markers showed polymorphisms across the half-sib family. An individual linkage map was built for each full-sib family first, and then the linkage maps from different full-sib families were integrated by common orthologous SSR markers with software JoinMap (ver3.0). There were 112 polymorphic markers mapped to the integrated map which contained 16 linkage groups. The observed map length was 1874.3 cM and covered 79.85% of genome. The estimated 95% confidence interval for genome length was 1781.3-2411.6 cM. Seventeen (17) QTLs were identified by single marker regression using 305 backcross progenies. For the interval mapping, the tallest and shortest 8 percent of seedlings were selected for QTL detection (phase I), and then random selections of 8 percent of the seedlings from the rest of the population and 25 seedlings from both tails of the within family distributions were used for unbiased QTL verification and mapping (phase II). Nine QTLs were detected and verified as associated with the 5 growth traits under P=0.05 chromosome-wide threshold. There was only weak evidence of QTL stability during the three years of growth under this study

Quantifying the genetic component of the metabolic syndrome using a novel proposal score and SNP-based heritability

Introduction. Metabolic syndrome (MetS) is a complex, multifactorial disease that poses a major public health problem. MetS increases the risk of coronary heart disease (CHD), atherosclerotic cardiovascular diseases (ASCVD), type 2 diabetes mellitus (T2DM), and all-cause mortality. Currently, there are a many different criteria that define MetS but the physiopathology is not completely understood both in terms of clinical progression and genetic contribution. Aims. The present work characterizes MetS components (obesity, hypertension, glucose, etc.) as one continuous phenotype and genetic components of the proposed MetS score were estimated using both family-based samples and population-based samples. Methods. In the first step, Confirmatory Factor Analysis (CFA) was used to select a model with the best fit. After the selection of the best factor structure and development an algorithm to calculate the score, heritability was performed in both pedigrees and SNPs/markers data. For the first sample, SOLAR (Sequential Oligogenic Linkage Analysis Routines) software was used to obtain the estimates. For the second sample, genetic variance components were calculated by fitting a linear mixed model (LMM) using two types of genetic relatedness matrices (Identity-By-Descend, IBD and Genome-Wide Complex Trait Analysis, GCTA), different levels of Linkage Disequilibrium (LD) pruning (0.20 – 0.80 and no LD pruning), and suggestive Genome-Wide Association Study (GWAS) SNPs. Results. According to the analyses, the best CFA model was the bifactor model; estimated coefficients were used to calculate the MetS score. The score showed good performance and good agreement compared to the International Diabetes Federation (IDF) criteria, the gold standard used for clinical diagnosis. With regards to the estimation of genetic variance, heritability was significant and ranged from 0.1 to 0.4 in whole samples and in all models. The heterogeneity of the results was due to the different samples and different types of matrix inputs into the LMMs. Heritability obtained using the GCTA matrix was significantly increased compared to the IBD matrix. No significant differences between family data and genetic data (markers) in Sardinia samples were observed using an LD threshold of 0.80 with no pruning. Conclusions. Evidence of complex interactions in metabolic syndrome and significant genetic contributions were obtained from these analyses. Increased knowledge of the environmental and genetic components could allow for better assessment and identification of patients with this syndrome