http://www.biomedcentral.com/bmcgenet/ The latest research articles published by BMC Genetics 2009-12-18T00:00:00Z This is an RSS newsfeed from BioMed Central It is intended to be used with an RSS reader. For more information about RSS newsfeeds from BioMed Central, visit http://www.biomedcentral.com/info/about/rss/ Background: Whole genome amplification (WGA) offers new possibilities for genome-wide association studies where limited DNA samples have been collected. This study provides a realistic and high-precision assessment of WGA DNA genotyping performance from 20-year old archived serum samples using the Affymetrix Genome-Wide Human SNP Array 6.0 (SNP6.0) platform. Results: Whole-genome amplified (WGA) DNA samples from 45 archived serum replicates and 5 fresh sera paired with non-amplified genomic DNA were genotyped in duplicate. All genotyped samples passed the imposed QC thresholds for quantity and quality. In general, WGA serum DNA samples produced low call rates (45.00+/-2.69 %), although reproducibility for successfully called markers was favorable (concordance= 95.61+/-4.39%). Heterozygote dropouts explained the majority (>85% in technical replicates, 50% in paired genomic/serum samples) of discordant results. Genotyping performance on WGA serum DNA samples was improved by implementation of Corrected Robust Linear Model with Maximum Likelihood Classification (CRLMM) algorithm but at the loss of many samples which failed to pass its quality threshold. Poor genotype clustering was evident in the samples that failed the CRLMM confidence threshold. Conclusion: We conclude that while it is possible to extract genomic DNA and subsequently perform whole-genome amplification from archived serum samples, WGA serum DNA did not perform well and appeared unsuitable for high-resolution genotyping on these arrays. http://www.biomedcentral.com/1471-2156/10/85 Kristine Bucasas Gagan Pandya Sonal Pradhan Robert Fleischmann Scott Peterson John Belmont BMC Genetics 2009, 10:85 2009-12-18T00:00:00Z doi:10.1186/1471-2156-10-85 BMC Genetics 1471-2156 10 85 2009-12-18T00:00:00Z PDF Background: Sample size is one of the critical factors affecting the accuracy of the estimation of population genetic diversity parameters. Small sample sizes often lead to significant errors in determining the allelic richness, which is one of the most important and commonly used estimators of genetic diversity in populations. Correct estimation of allelic richness in natural populations is challenging since they often do not conform to model assumptions. Here, we introduce a simple and robust approach to estimate the genetic diversity in large natural populations based on the empirical data for finite sample sizes. Results: We developed a non-linear regression model to infer genetic diversity estimates in large natural populations from finite sample sizes. The allelic richness values predicted by our model were in good agreement with those observed in the simulated data sets and the true allelic richness observed in the source populations. The model has been validated using simulated population genetic data sets with different evolutionary scenarios implied in the simulated populations, as well as large microsatellite and allozyme experimental data sets for four conifer species with contrasting patterns of inherent genetic diversity and mating systems. Our model was a better predictor for allelic richness in natural populations than the widely-used Ewens sampling formula, coalescent approach, and rarefaction algorithm. Conclusions: Our regression model was capable of accurately estimating allelic richness in natural populations regardless of the species and marker system. This regression modeling approach is free from assumptions and can be widely used for population genetic and conservation applications. http://www.biomedcentral.com/1471-2156/10/84 Stanislav Bashalkhanov Madhav Pandey Om Rajora BMC Genetics 2009, 10:84 2009-12-16T00:00:00Z doi:10.1186/1471-2156-10-84 BMC Genetics 1471-2156 10 84 2009-12-16T00:00:00Z PDF Background: The use of molecular genetic technologies for broodstock management and selective breeding of aquaculture species is becoming increasingly more common with the continued development of genome tools and reagents. Several laboratories have produced genetic maps for rainbow trout to aid in the identification of loci affecting phenotypes of interest. These maps have resulted in the identification of many quantitative/qualitative trait loci affecting phenotypic variation in traits associated with albinism, disease resistance, temperature tolerance, sex determination, embryonic development rate, spawning date, condition factor and growth. Unfortunately, the elucidation of the precise allelic variation and/or genes underlying phenotypic diversity has yet to be achieved in this species having low marker densities and lacking a whole genome reference sequence. Experimental designs which integrate segregation analyses with linkage disequilibrium (LD) approaches facilitate the discovery of genes affecting important traits. To date the extent of LD has been characterized for humans and several agriculturally important livestock species but not for rainbow trout. Results: We observed that the level of LD between syntenic loci decayed rapidly at distances greater than 2 cM which is similar to observations of LD in other agriculturally important species including cattle, sheep, pigs and chickens. However, in some cases significant LD was also observed up to 50 cM. Our estimate of effective population size based on genome wide estimates of LD for the NCCCWA broodstock population was 145, indicating that this population will respond well to high selection intensity. However, the range of effective population size based on individual chromosomes was 75.51 - 203.35, possibly indicating that suites of genes on each chromosome are disproportionately under selection pressures. Conclusions: Our results indicate that large numbers of markers, more than are currently available for this species, will be required to enable the use of genome-wide integrated mapping approaches aimed at identifying genes of interest in rainbow trout. http://www.biomedcentral.com/1471-2156/10/83 Caird Rexroad Roger Vallejo BMC Genetics 2009, 10:83 2009-12-14T00:00:00Z doi:10.1186/1471-2156-10-83 BMC Genetics 1471-2156 10 83 2009-12-14T00:00:00Z PDF Background: The use of pooled DNA on SNP microarrays (SNP-MaP) has been shown to be a cost effective and rapid manner to perform whole-genome association evaluations. While the accuracy of SNP-MaP was extensively evaluated on the early Affymetrix 10k and 100k platforms, there have not been as many similarly comprehensive studies on more recent platforms. In the present study, we used the data generated from the full Affymetrix 500k SNP set together with the polynomial-based probe-specific correction (PPC) to derive allele frequency estimates. These estimates were compared to genotyping results of the same individuals on the same platform, as the basis to evaluate the reliability and accuracy of pooled genotyping on these high-throughput platforms. We subsequently extended this comparison to the new SNP6.0 platform capable of genotyping 1.8 million genetic variants. Results: We showed that pooled genotyping on the 500k platform performed as well as those previously shown on the relatively lower throughput 10k and 100k array sets, with high levels of accuracy (correlation coefficient: 0.988) and low median error (0.036) in allele frequency estimates. Similar results were also obtained from the SNP6.0 array set. A novel pooling strategy of overlapping sub-pools was attempted and comparison of estimated allele frequencies showed this strategy to be as reliable as replicate pools. The importance of an appropriate reference genotyping data set for the application of the PPC algorithm was also evaluated; reference samples with similar ethnic background to the pooled samples were found to improve estimation of allele frequencies. Conclusion: We conclude that use of the PPC algorithm to estimate allele frequencies obtained from pooled genotyping on the high throughput 500k and SNP6.0 platforms is highly accurate and reproducible especially when a suitable reference sample set is used to estimate the beta values for PPC. http://www.biomedcentral.com/1471-2156/10/82 Ramani Anantharaman Fook Tim Chew BMC Genetics 2009, 10:82 2009-12-14T00:00:00Z doi:10.1186/1471-2156-10-82 BMC Genetics 1471-2156 10 82 2009-12-14T00:00:00Z PDF Background: The potential utility of haplotype association mapping (HAM) as a tool for dissecting the genetic architecture of complex traits has yet to be fully realized. Concerns have been raised in regards to limitations of statistical power and type I error control. To assess the utility of HAM as a quantitative trait locus (QTL) discovery tool, we conducted HAM analyses for red blood cell count (RBC) and high density lipoprotein cholesterol (HDL) in mice, and then experimentally tested the novel HAM QTLs using F2 intercrosses. Progenitor strains for the experimental crosses were chosen not for their phenotypic differences, but instead based on their differing haplotypes at the HAM peaks in question. Results: HAM for RBC using 33 classic inbred lines revealed two novel QTLs on chromosomes (Chrs) 2 and 12 verified by a HAM-guided (C57BL/6J X CBA/J)F2 intercross (n = 108). Known RBC QTLs on Chrs 4 and 11 were also detected. For HDL, the 33-strain panel lacked the power to detect significant QTLs. By including recombinant inbred lines and chromosome substitution strains (81 strains total), 89 significant HAM "peaks" detected at least 20 unique QTLs. Three of these were novel QTLs on Chrs 8 and 13 as verified by our HAM-guided (C57BL/6J X A/J)F2 intercross (n = 286). Another 28 significant HAM peaks were revealed to be false positives and were in significant allelic association with one or more real QTL, confirming suspicions that HAM analyses represent a workable trade-off between type I and type II errors. Conclusions: HAM analyses of complex traits provide a compelling starting point for hypothesis-driven experiments. Because type I errors (false positives) can be detected experimentally, we conclude that researchers should not be dissuaded from using HAM for QTL detection and narrowing. We advocate the powerful and economical combined approach demonstrated here: namely, the use of HAM for QTL discovery, followed by mitigation of the false positive problem by testing the HAM-predicted QTLs with small HAM-guided experimental crosses. http://www.biomedcentral.com/1471-2156/10/81 Sarah Burgess-Herbert Shirng-Wern Tsaih Ioannis Stylianou Kenneth Walsh Allison Cox Beverly Paigen BMC Genetics 2009, 10:81 2009-12-09T00:00:00Z doi:10.1186/1471-2156-10-81 BMC Genetics 1471-2156 10 81 2009-12-09T00:00:00Z PDF Background: Genetic studies have often produced conflicting results on the question of whether distant Jewish populations in different geographic locations share greater genetic similarity to each other or instead, to nearby non-Jewish populations. We perform a genome-wide population-genetic study of Jewish populations, analyzing 678 autosomal microsatellite loci in 78 individuals from four Jewish groups together with similar data on 321 individuals from 12 non-Jewish Middle Eastern and European populations. Results: We find that the Jewish populations show a high level of genetic similarity to each other, clustering together in several types of analysis of population structure. Further, Bayesian clustering, neighbor-joining trees, and multidimensional scaling place the Jewish populations as intermediate between the non-Jewish Middle Eastern and European populations. Conclusion: These results support the view that the Jewish populations largely share a common Middle Eastern ancestry and that over their history they have undergone varying degrees of admixture with non-Jewish populations of European descent. http://www.biomedcentral.com/1471-2156/10/80 Naama Kopelman Lewi Stone Chaolong Wang Dov Gefel Marcus Feldman Jossi Hillel Noah Rosenberg BMC Genetics 2009, 10:80 2009-12-08T00:00:00Z doi:10.1186/1471-2156-10-80 BMC Genetics 1471-2156 10 80 2009-12-08T00:00:00Z PDF Background: Leopards are the most widely distributed of the large cats, ranging from Africa to the Russian Far East. Because of habitat fragmentation, high human population densities and the inherent adaptability of this species, they now occupy landscapes close to human settlements. As a result, they are the most common species involved in human wildlife conflict in India, necessitating their monitoring. However, their elusive nature makes such monitoring difficult. Recent advances in DNA methods along with non-invasive sampling techniques can be used to monitor populations and individuals across large landscapes including human dominated ones. In this paper, we describe a DNA-based method for leopard individual identification where we used fecal DNA samples to obtain genetic material. Further, we apply our methods to non-invasive samples collected in a human-dominated landscape to estimate the minimum number of leopards in this human-leopard conflict area in Western India. Results: In this study, 25 of the 29 tested cross-specific microsatellite markers showed positive amplification in 37 wild-caught leopards. These loci revealed varied levels of polymorphism (four-12 alleles) and heterozygosity (0.05- 0.79). Combining data on amplification success (including non-invasive samples) and locus specific polymorphisms, we showed that eight loci provide a sibling probability of identity of 0.0005, suggesting that this panel can be used to discriminate individuals in the wild. When this microsatellite panel was applied to fecal samples collected from a human-dominated landscape, we identified 7 individuals, with a sibling probability of identity of 0.001. Amplification success of field collected scats was up to 72%, and genotype error ranged from 0- 7.4 %. Conclusions: Our results demonstrated that the selected panel of eight microsatellite loci can conclusively identify leopards from various kinds of biological samples. Our methods can be used to monitor leopards over small and large landscapes to assess population trends, as well as could be tested for population assignment in forensic applications. http://www.biomedcentral.com/1471-2156/10/79 Samrat Mondol Navya R. Vidya Athreya Kartik Sunagar Velu Selvaraj Uma Ramakrishnan BMC Genetics 2009, 10:79 2009-12-04T00:00:00Z doi:10.1186/1471-2156-10-79 BMC Genetics 1471-2156 10 79 2009-12-04T00:00:00Z PDF Background: Genomic control (GC) method is a useful tool to correct for the cryptic relatedness in population-based association studies. It was originally proposed for correcting for the variance inflation of Cochran-Armitage's additive trend test by using information from unlinked null markers, and was later generalized to be applicable to other tests with the additional requirement that the null markers are matched with the candidate marker in allele frequencies. However, matching allele frequencies limits the number of available null markers and thus limits the applicability of the GC method. On the other hand, errors in genotype/allele frequencies may cause further bias and variance inflation and thereby aggravate the effect of GC correction. Results: In this paper, we propose a regression-based GC method using null markers that are not necessarily matched in allele frequencies with the candidate marker. Variation of allele frequencies of the null markers is adjusted by a regression method. Conclusion: The proposed method can be readily applied to the Cochran-Armitage's trend tests other than the additive trend test, the Pearson's chi-square test and other robust efficiency tests. Simulation results show that the proposed method is effective in controlling type I error in the presence of population substructure. http://www.biomedcentral.com/1471-2156/10/78 Ting Yan Bo Hou Yaning Yang BMC Genetics 2009, 10:78 2009-12-02T00:00:00Z doi:10.1186/1471-2156-10-78 BMC Genetics 1471-2156 10 78 2009-12-02T00:00:00Z XML Background: The Glued gene of Drosophila melanogaster encodes the homologue of the vertebrate p150Glued subunit of dynactin. The Glued1 mutation compromises the dynein-dynactin retrograde motor complex and causes disruptions to the adult eye and the CNS, including sensory neurons and the formation of the giant fiber system neural circuit. Results: We performed a 2-stage genetic screen to identify mutations that modified phenotypes caused by over-expression of a dominant-negative Glued protein. We screened over 34,000 flies and isolated 41 mutations that enhanced or suppressed an eye phenotype. Of these, 12 were assayed for interactions in the giant fiber system by which they altered a giant fiber morphological phenotype and/or altered synaptic function between the giant fiber and the tergotrochanteral muscle motorneuron. Six showed interactions including a new allele of atypical protein kinase C (aPKC). We show that this cell polarity regulator interacts with Glued during central synapse formation. We have mapped the five other interacting mutations to discrete chromosomal regions. Conclusion: Our results show that an efficient way to screen for genes involved in central synapse formation is to use a two-step strategy in which a screen for altered eye morphology precedes the analysis of central synaptogenesis. This has highlighted a role for aPKC in the formation of an identified central synapse. http://www.biomedcentral.com/1471-2156/10/77 Lisha Ma Louise Johns Marcus Allen BMC Genetics 2009, 10:77 2009-11-30T00:00:00Z doi:10.1186/1471-2156-10-77 BMC Genetics 1471-2156 10 77 2009-11-30T00:00:00Z XML Background: Peroxisome proliferator-activated receptor delta belongs to the nuclear receptor superfamily of ligand-inducible transcription factors. It is a key regulator of lipid metabolism. The peroxisome proliferator-activated receptor delta gene (PPARD) has been assigned to a region on porcine chromosome 7, which harbours a quantitative trait locus for backfat. Thus, PPARD is considered a functional and positional candidate gene for backfat thickness. The purpose of this study was to test this candidate gene hypothesis in a cross of breeds that were highly divergent in lipid deposition characteristics. Results: Screening for genetic variation in porcine PPARD revealed only silent mutations. Nevertheless, significant associations between PPARD haplotypes and backfat thickness were observed in the F2 generation of the Mangalitsa × Piétrain cross as well as a commercial German Landrace population. Haplotype 5 is associated with increased backfat in F2 Mangalitsa × Piétrain pigs, whereas haplotype 4 is associated with lower backfat thickness in the German Landrace population. Haplotype 4 and 5 carry the same alleles at all but one SNP. Interestingly, the opposite effects of PPARD haplotypes 4 and 5 on backfat thickness are reflected by opposite effects of these two haplotypes on PPAR-δ mRNA levels. Haplotype 4 significantly increases PPAR-δ mRNA levels, whereas haplotype 5 decreases mRNA levels of PPAR-δ. Conclusion: This study provides evidence for an association between PPARD and backfat thickness. The association is substantiated by mRNA quantification. Further studies are required to clarify, whether the observed associations are caused by PPARD or are the result of linkage disequilibrium with a causal variant in a neighbouring gene. http://www.biomedcentral.com/1471-2156/10/76 Karina Meidtner Hermann Schwarzenbacher Maren Scharfe Simone Severitt Helmut Bloecker Ruedi Fries BMC Genetics 2009, 10:76 2009-11-30T00:00:00Z doi:10.1186/1471-2156-10-76 BMC Genetics 1471-2156 10 76 2009-11-30T00:00:00Z XML