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Background Rutabaga or swede (Brassica napus ssp. napobrassica (L.) Hanelt) varies in root and leaf shape and colour, flesh colour, foliage growth habits, maturity date, seed quality parameters, disease resistance and other traits. Despite these morphological differences, no in-depth molecular analyses of genetic diversity have been conducted in this crop. Understanding this diversity is important for conservation and broadening the use of this resource. Results This study investigated the genetic diversity within and among 124 rutabaga accessions from five Nordic countries (Norway, Sweden, Finland, Denmark and Iceland) using a 15 K single nucleotide polymorphism (SNP) Brassica array. After excluding markers that did not amplify genomic DNA, monomorphic and low coverage site markers, the accessions were analyzedwith 6861 SNP markers. Allelic frequency statistics, including polymorphism information content (PIC), minor allele frequency (MAF) and mean expected heterozygosity ($$ \overline{H} $$ H ¯ e) and population differentiation statistics such as Wright’s F-statistics (FST) and analysis of molecular variance (AMOVA) indicated that the rutabaga accessions from Norway, Sweden, Finland and Denmark were not genetically different from each other. In contrast, accessions from these countries were significantly different from the accessions from Iceland (P STRUCTURE placed 66.9% of the rutabaga accessions into three to four clusters, while the remaining 33.1% constituted admixtures. Three multivariate analyses: principal coordinate analysis (PCoA), the unweighted pair group method with arithmetic mean (UPGMA) and neighbour-joining (NJ) clustering methods grouped the 124 accessions into four to six subgroups. Conclusion Overall, the correlation of the accessions with their geographic origin was very low, except for the accessions from Iceland. Thus, Icelandic rutabaga accessions can offer valuable germplasm for crop improvement.

Background Structural variants (SV) are causative for some prominent phenotypic traits of livestock as different comb types in chickens or color patterns in pigs. Their effects on production traits are also increasingly studied. Nevertheless, accurately calling SV remains challenging. It is therefore of interest, whether close-by single nucleotide polymorphisms (SNPs) are in strong linkage disequilibrium (LD) with SVs and can serve as markers. Literature comes to different conclusions on whether SVs are in LD to SNPs on the same level as SNPs to other SNPs. The present study aimed to generate a precise SV callset from whole-genome short-read sequencing (WGS) data for three commercial chicken populations and to evaluate LD patterns between the called SVs and surrounding SNPs. It is thereby the first study that assessed LD between SVs and SNPs in chickens. Results The final callset consisted of 12,294,329 bivariate SNPs, 4,301 deletions (DEL), 224 duplications (DUP), 218 inversions (INV) and 117 translocation breakpoints (BND). While average LD between DELs and SNPs was at the same level as between SNPs and SNPs, LD between other SVs and SNPs was strongly reduced (DUP: 40%, INV: 27%, BND: 19% of between-SNP LD). A main factor for the reduced LD was the presence of local minor allele frequency differences, which accounted for 50% of the difference between SNP – SNP and DUP – SNP LD. This was potentially accompanied by lower genotyping accuracies for DUP, INV and BND compared with SNPs and DELs. An evaluation of the presence of tag SNPs (SNP in highest LD to the variant of interest) further revealed DELs to be slightly less tagged by WGS SNPs than WGS SNPs by other SNPs. This difference, however, was no longer present when reducing the pool of potential tag SNPs to SNPs located on four different chicken genotyping arrays. Conclusions The results implied that genomic variance due to DELs in the chicken populations studied can be captured by different SNP marker sets as good as variance from WGS SNPs, whereas separate SV calling might be advisable for DUP, INV, and BND effects.

Background In forest trees, genetic markers have been used to understand the genetic architecture of natural populations, identify quantitative trait loci, infer gene function, and enhance tree breeding. Recently, new, efficient technologies for genotyping thousands to millions of single nucleotide polymorphisms (SNPs) have finally made large-scale use of genetic markers widely available. These methods will be exceedingly valuable for improving tree breeding and understanding the ecological genetics of Douglas-fir, one of the most economically and ecologically important trees in the world. Results We designed SNP assays for 55,766 potential SNPs that were discovered from previous transcriptome sequencing projects. We tested the array on ~ 2300 related and unrelated coastal Douglas-fir trees (Pseudotsuga menziesii var. menziesii) from Oregon and Washington, and 13 trees of interior Douglas-fir (P. menziesii var. glauca). As many as ~ 28 K SNPs were reliably genotyped and polymorphic, depending on the selected SNP call rate. To increase the number of SNPs and improve genome coverage, we developed protocols to ‘rescue’ SNPs that did not pass the default Affymetrix quality control criteria (e.g., 97% SNP call rate). Lowering the SNP call rate threshold from 97 to 60% increased the number of successful SNPs from 20,669 to 28,094. We used a subset of 395 unrelated trees to calculate SNP population genetic statistics for coastal Douglas-fir. Over a range of call rate thresholds (97 to 60%), the median call rate for SNPs in Hardy-Weinberg equilibrium ranged from 99.2 to 99.7%, and the median minor allele frequency ranged from 0.198 to 0.233. The successful SNPs also worked well on interior Douglas-fir. Conclusions Based on the original transcriptome assemblies and comparisons to version 1.0 of the Douglas-fir reference genome, we conclude that these SNPs can be used to genotype about 10 K to 15 K loci. The Axiom genotyping array will serve as an excellent foundation for studying the population genomics of Douglas-fir and for implementing genomic selection. We are currently using the array to construct a linkage map and test genomic selection in a three-generation breeding program for coastal Douglas-fir.

Background As a photoperiod-sensitive and self-pollinated species, the growth periods traits play important roles in the adaptability and yield of soybean. To examine the genetic architecture of soybean growth periods, we performed a genome-wide association study (GWAS) using a panel of 278 soybean accessions and 34,710 single nucleotide polymorphisms (SNPs) with minor allele frequencies (MAF) higher than 0.04 detected by the specific-locus amplified fragment sequencing (SLAF-seq) with a 6.14-fold average sequencing depth. GWAS was conducted by a compressed mixed linear model (CMLM) involving in both relative kinship and population structure. Results GWAS revealed that 37 significant SNP peaks associated with soybean flowering time or other growth periods related traits including full bloom, beginning pod, full pod, beginning seed, and full seed in two or more environments at -log10(P) > 3.75 or -log10(P) > 4.44 were distributed on 14 chromosomes, including chromosome 1, 2, 3, 5, 6, 9, 11, 12, 13, 14, 15, 17, 18, 19. Fourteen SNPs were novel loci and 23 SNPs were located within known QTLs or 75 kb near the known SNPs. Five candidate genes (Glyma.05G101800, Glyma.11G140100, Glyma.11G142900, Glyma.19G099700, Glyma.19G100900) in a 90 kb genomic region of each side of four significant SNPs (Gm5_27111367, Gm11_10629613, Gm11_10950924, Gm19_34768458) based on the average LD decay were homologs of Arabidopsis flowering time genes of AT5G48385.1, AT3G46510.1, AT5G59780.3, AT1G28050.1, and AT3G26790.1. These genes encoding FRI (FRIGIDA), PUB13 (plant U-box 13), MYB59, CONSTANS, and FUS3 proteins respectively might play important roles in controlling soybean growth periods. Conclusions This study identified putative SNP markers associated with soybean growth period traits, which could be used for the marker-assisted selection of soybean growth period traits. Furthermore, the possible candidate genes involved in the control of soybean flowering time were predicted.

Background National genetic evaluations for disease resistance do not exist, precluding the genetic improvement of cattle for these traits. We imputed BovineHD genotypes to whole genome sequence for 2703 Holsteins that were cases or controls for Bovine Respiratory Disease and sampled from either California or New Mexico to construct and compare genomic prediction models. The sequence variation reference dataset comprised variants called for 1578 animals from Run 5 of the 1000 Bull Genomes Project, including 450 Holsteins and 29 animals sequenced from this study population. Genotypes for 9,282,726 variants with minor allele frequencies ≥5% were imputed and used to obtain genomic predictions in GEMMA using a Bayesian Sparse Linear Mixed Model. Results Variation explained by markers increased from 13.6% using BovineHD data to 14.4% using imputed whole genome sequence data and the resolution of genomic regions detected as harbouring QTL substantially increased. Explained variation in the analysis of the combined California and New Mexico data was less than when data for each state were separately analysed and the estimated genetic correlation between risk of Bovine Respiratory Disease in California and New Mexico Holsteins was − 0.36. Consequently, genomic predictions trained using the data from one state did not accurately predict disease risk in the other state. To determine if a prediction model could be developed with utility in both states, we selected variants within genomic regions harbouring: 1) genes involved in the normal immune response to infection by pathogens responsible for Bovine Respiratory Disease detected by RNA-Seq analysis, and/or 2) QTL identified in the association analysis of the imputed sequence variants. The model based on QTL selected variants is biased but when trained in one state generated BRD risk predictions with positive accuracies in the other state. Conclusions We demonstrate the utility of sequence-based and biology-driven model development for genomic selection. Disease phenotypes cannot be routinely recorded in most livestock species and the observed phenotypes may vary in their genomic architecture due to variation in the pathogen composition across environments. Elucidation of trait biology and genetic architecture may guide the development of prediction models with utility across breeds and environments. Electronic supplementary material The online version of this article (10.1186/s12864-019-5941-5) contains supplementary material, which is available to authorized users.

Background China has the richest local chicken breeding resources in the world and is the world’s second largest producer of meat-type chickens. Development of a moderate-density SNP array for genetic analysis of chickens and breeding of meat-type chickens taking utility of those resources is urgently needed for conventional farms, breeding industry, and research areas. Results Eight representative local breeds or commercial broiler lines with 3 pools of 48 individuals within each breed/line were sequenced and supplied the major SNPs resource. There were 7.09 million - 9.41 million SNPs detected in each breed/line. After filtering using multiple criteria such as preferred incorporation of trait-related SNPs and uniformity of distribution across the genome, 52.18 K SNPs were selected in the final array. It consists of: (i) 19.22 K SNPs from the genomes of yellow-feathered, cyan-shank partridge and white-feathered chickens; (ii) 5.98 K SNPs related to economic traits from the Illumina 60 K SNP Bead Chip, which were found as significant associated SNPs with 15 traits in a Beijing-You crossed Cobb F2 resource population by genome-wide association study analysis; (iii) 7.63 K SNPs from 861 candidate genes of economic traits; (iv) the 0.94 K SNPs related to residual feed intake; and (v) 18.41 K from chicken SNPdb. The polymorphisms of 9 extra local breeds and 3 commercial lines were examined with this array, and 40 K - 47 K SNPs were polymorphic (with minor allele frequency > 0.05) in those breeds. The MDS result showed that those breeds can be clearly distinguished by this newly developed genotyping array. Conclusions We successfully developed a 55K genotyping array by using SNPs segregated from typical local breeds and commercial lines. Compared to the existing Affy 600 K and Illumina 60 K arrays, there were 21,41 K new SNPs included on our Affy 55K array. The results of the 55K genotyping data can therefore be imputed to high-density SNPs genotyping data. The array offers a wide range of potential applications such as genomic selection breeding, GWAS of interested traits, and investigation of diversity of different chicken breeds. Electronic supplementary material The online version of this article (10.1186/s12864-019-5736-8) contains supplementary material, which is available to authorized users.

Background Synonymous mutations are able to change the tAI (tRNA adaptation index) of a codon and consequently affect the local translation rate. Intuitively, one may hypothesize that those synonymous mutations which increase the tAI values are favored by natural selection. Results We use the maize (Zea mays) genome to test our assumption. The first supporting evidence is that the tAI-increasing synonymous mutations have higher fixed-to-polymorphic ratios than the tAI-decreasing ones. Next, the DAF (derived allele frequency) or MAF (minor allele frequency) of the former is significantly higher than the latter. Moreover, similar results are obtained when we investigate CAI (codon adaptation index) instead of tAI. Conclusion The synonymous mutations in the maize genome are not strictly neutral. The tAI-increasing mutations are positively selected while those tAI-decreasing ones undergo purifying selection. This selection force might be weak but should not be automatically ignored.

Background Sclerotinia Stem Rot (SSR), caused by the fungal pathogen Sclerotinia sclerotiorum, is ubiquitous in cooler climates where soybean crops are grown. Breeding for resistance to SSR remains challenging in crops like soybean, where no single gene provides strong resistance, but instead, multiple genes work together to provide partial resistance. In this study, a genome-wide association study (GWAS) was performed to dissect the complex genetic architecture of soybean quantitative resistance to SSR and to provide effective molecular markers that could be used in breeding programs. A collection of 420 soybean genotypes were selected based on either reports of resistance, or from one of three different breeding programs in Brazil, two commercial, one public. Plant genotype sensitivity to SSR was evaluated by the cut stem inoculation method, and lesion lengths were measured at 4 days post inoculation. Results Genotyping-by-sequencing was conducted to genotype the 420 soybean lines. The TASSEL 5 GBSv2 pipeline was used to call SNPs under optimized parameters, and with the extra step of trimming adapter sequences. After filtering missing data, heterozygosity, and minor allele frequency, a total of 11,811 SNPs and 275 soybean genotypes were obtained for association analyses. Using a threshold of FDR-adjusted p-values

Background The cost of Whole Genome Sequencing (WGS) has decreased tremendously in recent years due to advances in next-generation sequencing technologies. Nevertheless, the cost of carrying out large-scale cohort studies using WGS is still daunting. Past simulation studies with coverage at ~2x have shown promise for using low coverage WGS in studies focused on variant discovery, association study replications, and population genomics characterization. However, the performance of low coverage WGS in populations with a complex history and no reference panel remains to be determined. Results South Indian populations are known to have a complex population structure and are an example of a major population group that lacks adequate reference panels. To test the performance of extremely low-coverage WGS (EXL-WGS) in populations with a complex history and to provide a reference resource for South Indian populations, we performed EXL-WGS on 185 South Indian individuals from eight populations to ~1.6x coverage. Using two variant discovery pipelines, SNPTools and GATK, we generated a consensus call set that has ~90% sensitivity for identifying common variants (minor allele frequency ≥ 10%). Imputation further improves the sensitivity of our call set. In addition, we obtained high-coverage for the whole mitochondrial genome to infer the maternal lineage evolutionary history of the Indian samples. Conclusions Overall, we demonstrate that EXL-WGS with imputation can be a valuable study design for variant discovery with a dramatically lower cost than standard WGS, even in populations with a complex history and without available reference data. In addition, the South Indian EXL-WGS data generated in this study will provide a valuable resource for future Indian genomic studies. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3767-6) contains supplementary material, which is available to authorized users.

Background The hundred seed weight (HSW) is one of the yield components of soybean [Glycine max (L.) Merrill] and is especially critical for various soybean food types. In this study, a representative sample consisting of 185 accessions was selected from Northeast China and analysed in three tested environments to determine the quantitative trait nucleotide (QTN) of HSW through a genome-wide association study (GWAS). Result A total of 24,180 single nucleotide polymorphisms (SNPs) with minor allele frequencies greater than 0.2 and missing data less than 3% were utilized to estimate linkage disequilibrium (LD) levels in the tested association panel. Thirty-four association signals were identified as associated with HSW via GWAS. Among them, nineteen QTNs were novel, and another fifteen QTNs were overlapped or located near the genomic regions of known HSW QTL. A total of 237 genes, derived from 31 QTNs and located near peak SNPs from the three tested environments in 2015 and 2016, were considered candidate genes, were related to plant growth regulation, hormone metabolism, cell, RNA, protein metabolism, development, starch accumulation, secondary metabolism, signalling, and the TCA cycle, some of which have been found to participate in the regulation of HSW. A total of 106 SNPs from 16 candidate genes were significantly associated with HSW in soybean. Conclusions The identified loci with beneficial alleles and candidate genes might be valuable for the molecular network and MAS of HSW. Electronic supplementary material The online version of this article (10.1186/s12864-019-6009-2) contains supplementary material, which is available to authorized users.

Background The most widely used human genome reference assembly hg19 harbors minor alleles at 2.18 million positions as revealed by 1000 Genome Phase 3 dataset. Although this is less than 2% of the 89 million variants reported, it has been shown that the minor alleles can result in 30% false positives in individual genomes, thus misleading and burdening downstream interpretation. More alarming is the fact that, significant percentage of variants that are homozygous recessive for these minor alleles, with potential disease implications, are masked from reporting. Results We have demonstrated that the false positives (FP) and false negatives (FN) can be corrected for by simply replacing nucleotides at the minor allele positions in hg19 with corresponding major allele. Here, we have effectively replaced 2.18 million minor alleles Single Nucleotide Polymorphism (SNPs), Insertion and Deletions (INDELs), Multiple Nucleotide Polymorphism (MNPs) in hg19 with the corresponding major alleles to create an ethnically normalized reference genome called hg19KIndel. In doing so, hg19KIndel has both corrected for sequencing errors acknowledged to be present in hg19 and has improved read alignment near the minor alleles in hg19. Conclusion We have created and made available a new version human reference genome called hg19KIndel. It has been shown that variant calling using hg19KIndel, significantly reduces false positives calls, which in-turn reduces the burden from downstream analysis and validation. It also improved false negative variants call, which means that the variants which were getting missed due to the presence of minor alleles in hg19, will now be called using hg19KIndel. Using hg19KIndel, one even gets a better mapping percentage when compared to currently available human reference genome. hg19KIndel reference genome and its auxiliary datasets are available at https://doi.org/10.5281/zenodo.2638113

Background Salt stress, a major plant environmental stress, is a critical constraint for rice productivity. Dissecting the genetic loci controlling salt tolerance in rice for improving productivity, especially at the flowering stage, remains challenging. Here, we conducted a genome-wide association study (GWAS) of salt tolerance based on exome sequencing of the Thai rice accessions. Results Photosynthetic parameters and cell membrane stability under salt stress at the flowering stage; and yield-related traits of 104 Thai rice (Oryza sativa L.) accessions belonging to the indica subspecies were evaluated. The rice accessions were subjected to exome sequencing, resulting in 112,565 single nucleotide polymorphisms (SNPs) called with a minor allele frequency of at least 5%. LD decay analysis of the panel indicates that the average LD for SNPs at 20 kb distance from each other was 0.34 (r2), which decayed to its half value (~ 0.17) at around 80 kb. By GWAS performed using mixed linear model, two hundred loci containing 448 SNPs on exons were identified based on the salt susceptibility index of the net photosynthetic rate at day 6 after salt stress; and the number of panicles, filled grains and unfilled grains per plant. One hundred and forty six genes, which accounted for 73% of the identified loci, co-localized with the previously reported salt quantitative trait loci (QTLs). The top four regions that contained a high number of significant SNPs were found on chromosome 8, 12, 1 and 2. While many are novel, their annotation is consistent with potential involvement in plant salt tolerance and in related agronomic traits. These significant SNPs greatly help narrow down the region within these QTLs where the likely underlying candidate genes can be identified. Conclusions Insight into the contribution of potential genes controlling salt tolerance from this GWAS provides further understanding of salt tolerance mechanisms of rice at the flowering stage, which can help improve yield productivity under salinity via gene cloning and genomic selection. Electronic supplementary material The online version of this article (10.1186/s12864-018-5317-2) contains supplementary material, which is available to authorized users.

Background Genotype imputation from single-nucleotide polymorphism (SNP) genotype data using a haplotype reference panel consisting of thousands of unrelated individuals from populations of interest can help to identify strongly associated variants in genome-wide association studies. The Tohoku Medical Megabank (TMM) project was established to support the development of precision medicine, together with the whole-genome sequencing of 1070 human genomes from individuals in the Miyagi region (Northeast Japan) and the construction of the 1070 Japanese genome reference panel (1KJPN). Here, we investigated the performance of 1KJPN for genotype imputation of Japanese samples not included in the TMM project and compared it with other population reference panels. Results We found that the 1KJPN population was more similar to other Japanese populations, Nagahama (south-central Japan) and Aki (Shikoku Island), than to East Asian populations in the 1000 Genomes Project other than JPT, suggesting that the large-scale collection (more than 1000) of Japanese genomes from the Miyagi region covered many of the genetic variations of Japanese in mainland Japan. Moreover, 1KJPN outperformed the phase 3 reference panel of the 1000 Genomes Project (1KGPp3) for Japanese samples, and IKJPN showed similar imputation rates for the TMM and other Japanese samples for SNPs with minor allele frequencies (MAFs) higher than 1%. Conclusions 1KJPN covered most of the variants found in the samples from areas of the Japanese mainland outside the Miyagi region, implying 1KJPN is representative of the Japanese population’s genomes. 1KJPN and successive reference panels are useful genome reference panels for the mainland Japanese population. Importantly, the addition of whole genome sequences not included in the 1KJPN panel improved imputation efficiencies for SNPs with MAFs under 1% for samples from most regions of the Japanese archipelago. Electronic supplementary material The online version of this article (10.1186/s12864-018-4942-0) contains supplementary material, which is available to authorized users.

Background Gene copy number variants (CNVs), which consist of deletions and amplifications of single or sets of contiguous genes, contribute to the great diversity in the Plasmodium falciparum genome. In vitro studies in the laboratory have revealed their important role in parasite fitness phenotypes such as red cell invasion, transmissibility and cytoadherence. Studies of natural parasite populations indicate that CNVs are also common in the field and thus may facilitate adaptation of the parasite to its local environment. Results In a survey of 183 fresh field isolates from three populations in Eastern Africa with different malaria transmission intensities, we identified 94 CNV loci using microarrays. All CNVs had low population frequencies (minor allele frequency 0.3) and nine exhibited significant clines in population frequency across a gradient in transmission intensity. The clearest example of this was a large deletion on chromosome 9 previously reported only in laboratory-adapted isolates. This deletion was present in 33% of isolates from a population with low and highly seasonal malaria transmission, and in

Background Higher fasting Growth Hormone (GH) has been associated with increased cardiovascular morbidity and mortality. Our objective was to find genetic determinants of fasting GH in order to facilitate future efforts of analyzing the association between fasting growth hormone and cardiovascular disease. A genome-wide association study (GWAS) was performed in a discovery cohort of 4134 persons (58% females; age 46–68 yrs), linking SNPs to fasting hs-GH. Fifteen SNPs were replicated in an independent cohort of 5262 persons (28.9% females; age 56–85 yrs). The best performing SNP was analyzed vs GH-related variables in a third independent cohort (n = 24,047; 61% females; age 44–73 yrs). A candidate gene approach searched for significant SNPs in the genes GH1 and GHR in the discovery cohort and was replicated as previously described. Results In the GWAS, the minor allele of rs7208736 was associated with lower GH in the discovery cohort (p = 5.15*10^-6) and the replication cohort (p = 0.005). The GH reducing allele was associated with lower BMI (P = 0.026) and waist (P = 0.021) in males only. In the candidate gene approach rs13153388 in the GHR-gene was associated with elevated GH-levels (P = 0.003) in the discovery cohort only and reduced height (P = 0.003). Conclusion In the first GWAS ever for GH, we identify a novel locus on chromosome 17 associated with fasting GH levels, suggesting novel biological mechanisms behind GH secretion and GH-related traits. The candidate gene approach identified a genetic variant in the GHR, which was associated with an elevation of fasting hs-GH and lower height suggesting reduced GHR ligand sensitivity. Our findings need further replication. Electronic supplementary material The online version of this article (10.1186/s12864-017-4219-z) contains supplementary material, which is available to authorized users.

Background Soybean (Glycine max L. Merr.) cyst nematode (SCN, Heterodera glycines I,) is a major pest of soybean worldwide. The most effective strategy to control this pest involves the use of resistant cultivars. The aim of the present study was to investigate the genome-wide genetic architecture of resistance to SCN HG Type 2.5.7 (race 1) in landrace and elite cultivated soybeans. Results A total of 200 diverse soybean accessions were screened for resistance to SCN HG Type 2.5.7 and genotyped through sequencing using the Specific Locus Amplified Fragment Sequencing (SLAF-seq) approach with a 6.14-fold average sequencing depth. A total of 33,194 SNPs were identified with minor allele frequencies (MAF) over 4%, covering 97% of all the genotypes. Genome-wide association mapping (GWAS) revealed thirteen SNPs associated with resistance to SCN HG Type 2.5.7. These SNPs were distributed on five chromosomes (Chr), including Chr7, 8, 14, 15 and 18. Four SNPs were novel resistance loci and nine SNPs were located near known QTL. A total of 30 genes were identified as candidate genes underlying SCN resistance. Conclusions A total of sixteen novel soybean accessions were identified with significant resistance to HG Type 2.5.7. The beneficial alleles and candidate genes identified by GWAS might be valuable for improving marker-assisted breeding efficiency and exploring the molecular mechanisms underlying SCN resistance. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3843-y) contains supplementary material, which is available to authorized users.

Background Polymorphic loci exist throughout the genomes of a population and provide the raw genetic material needed for a species to adapt to changes in the environment. The minor allele frequencies of rare Single Nucleotide Polymorphisms (SNPs) within a population have been difficult to track with Next-Generation Sequencing (NGS), due to the high error rate of standard methods such as Illumina sequencing. Results We have developed a wet-lab protocol and variant-calling method that identifies both sequencing and PCR errors, called Paired-End Low Error Sequencing (PELE-Seq). To test the specificity and sensitivity of the PELE-Seq method, we sequenced control E. coli DNA libraries containing known rare alleles present at frequencies ranging from 0.2–0.4 % of the total reads. PELE-Seq had higher specificity and sensitivity than standard libraries. We then used PELE-Seq to characterize rare alleles in a Caenorhabditis remanei nematode worm population before and after laboratory adaptation, and found that minor and rare alleles can undergo large changes in frequency during lab-adaptation. Conclusion We have developed a method of rare allele detection that mitigates both sequencing and PCR errors, called PELE-Seq. PELE-Seq was evaluated using control E. coli populations and was then used to compare a wild C. remanei population to a lab-adapted population. The PELE-Seq method is ideal for investigating the dynamics of rare alleles in a broad range of reduced-representation sequencing methods, including targeted amplicon sequencing, RAD-Seq, ddRAD, and GBS. PELE-Seq is also well-suited for whole genome sequencing of mitochondria and viruses, and for high-throughput rare mutation screens. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2669-3) contains supplementary material, which is available to authorized users.

Background Addictive disorders are a class of chronic, relapsing mental disorders that are responsible for increased risk of mental and medical disorders and represent the largest, potentially modifiable cause of death. Tobacco dependence is associated with increased risk of disease and premature death. While tobacco control efforts and therapeutic interventions have made good progress in reducing smoking prevalence, challenges remain in optimizing their effectiveness based on patient characteristics, including genetic variation. In order to maximize collaborative efforts to advance addiction research, we have developed a genotyping array called Smokescreen. This custom array builds upon previous work in the analyses of human genetic variation, the genetics of addiction, drug metabolism, and response to therapy, with an emphasis on smoking and nicotine addiction. Results The Smokescreen genotyping array includes 646,247 markers in 23 categories. The array design covers genome-wide common variation (65.67, 82.37, and 90.72 % in African (YRI), East Asian (ASN), and European (EUR) respectively); most of the variation with a minor allele frequency ≥ 0.01 in 1014 addiction genes (85.16, 89.51, and 90.49 % for YRI, ASN, and EUR respectively); and nearly all variation from the 1000 Genomes Project Phase 1, NHLBI GO Exome Sequencing Project and HapMap databases in the regions related to smoking behavior and nicotine metabolism: CHRNA5-CHRNA3-CHRNB4 and CYP2A6-CYP2B6. Of the 636 pilot DNA samples derived from blood or cell line biospecimens that were genotyped on the array, 622 (97.80 %) passed quality control. In passing samples, 90.08 % of markers passed quality control. The genotype reproducibility in 25 replicate pairs was 99.94 %. For 137 samples that overlapped with HapMap2 release 24, the genotype concordance was 99.76 %. In a genome-wide association analysis of the nicotine metabolite ratio in 315 individuals participating in nicotine metabolism laboratory studies, we identified genome-wide significant variants in the CYP2A6 region (min p = 9.10E-15). Conclusions We developed a comprehensive genotyping array for addiction research and demonstrated its analytic validity and utility through pilot genotyping of HapMap and study samples. This array allows researchers to perform genome-wide, candidate gene, and pathway-based association analyses of addiction, tobacco-use, treatment response, comorbidities, and associated diseases in a standardized, high-throughput platform. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-2495-7) contains supplementary material, which is available to authorized users.

Background Over the last few years, continuous development of high-throughput sequencing platforms and sequence analysis tools has facilitated reliable identification and characterization of genetic variants in many cattle breeds. Deep sequencing of entire genomes within a cattle breed that has not been thoroughly investigated would be imagined to discover functional variants that are underlying phenotypic differences. Here, we sequenced to a high coverage the Danish Holstein cattle breed to detect and characterize single nucleotide polymorphisms (SNPs), insertion/deletions (Indels), and loss-of-function (LoF) variants in protein-coding genes in order to provide a comprehensive resource for subsequent detection of causal variants for recessive traits. Results We sequenced four genetically unrelated Danish Holstein cows with a mean coverage of 27X using an Illumina Hiseq 2000. Multi-sample SNP calling identified 10,796,794 SNPs and 1,295,036 indels whereof 482,835 (4.5 %) SNPs and 231,359 (17.9 %) indels were novel. A comparison between sequencing-derived SNPs and genotyping from the BovineHD BeadChip revealed a concordance rate of 99.6–99.8 % for homozygous SNPs and 93.3–96.5 % for heterozygous SNPs. Annotation of the SNPs discovered 74,886 SNPs and 1937 indels affecting coding sequences with 2145 being LoF mutations. The frequency of LoF variants differed greatly across the genome, a hot spot with a strikingly high density was observed in a 6 Mb region on BTA18. LoF affected genes were enriched for functional categories related to olfactory reception and underrepresented for genes related to key cellular constituents and cellular and biological process regulation. Filtering using sequence derived genotype data for 288 Holstein animals from the 1000 bull genomes project removing variants containing homozygous individuals retained 345 of the LoF variants as putatively deleterious. A substantial number of the putative deleterious LoF variants had a minor allele frequency >0.05 in the 1000 bull genomes data set. Conclusions Deep sequencing of Danish Holstein genomes enabled us to identify 12.1 million variants. An investigation into LoF variants discovered a set of variants predicted to disrupt protein-coding genes. This catalog of variants will be a resource for future studies to understand variation underlying important phenotypes, particularly recessively inherited lethal phenotypes. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-2249-y) contains supplementary material, which is available to authorized users.

Background Multifactor dimensionality reduction (MDR) is widely used to analyze interactions of genes to determine the complex relationship between diseases and polymorphisms in humans. However, the astronomical number of high-order combinations makes MDR a highly time-consuming process which can be difficult to implement for multiple tests to identify more complex interactions between genes. This study proposes a new framework, named fast MDR (FMDR), which is a greedy search strategy based on the joint effect property. Results Six models with different minor allele frequencies (MAFs) and different sample sizes were used to generate the six simulation data sets. A real data set was obtained from the mitochondrial D-loop of chronic dialysis patients. Comparison of results from the simulation data and real data sets showed that FMDR identified significant gene–gene interaction with less computational complexity than the MDR in high-order interaction analysis. Conclusion FMDR improves the MDR difficulties associated with the computational loading of high-order SNPs and can be used to evaluate the relative effects of each individual SNP on disease susceptibility. FMDR is freely available at http://bioinfo.kmu.edu.tw/FMDR.rar. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1717-8) contains supplementary material, which is available to authorized users.

Background The power of the genome wide association studies starts to go down when the minor allele frequency (MAF) is below 0.05. Here, we proposed the use of Cohen’s h in detecting disease associated rare variants. The variance stabilizing effect based on the arcsine square root transformation of MAFs to generate Cohen’s h contributed to the statistical power for rare variants analysis. We re-analyzed published datasets, one microarray and one sequencing based, and used simulation to compare the performance of Cohen’s h with the risk difference (RD) and odds ratio (OR). Results The analysis showed that the type 1 error rate of Cohen’s h was as expected and Cohen’s h and RD were both less biased and had higher power than OR. The advantage of Cohen’s h was more obvious when MAF was less than 0.01. Conclusions Cohen’s h can increase the power to find genetic association of rare variants and diseases, especially when MAF is less than 0.01. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-875) contains supplementary material, which is available to authorized users.

Background Psoriasis is a common inflammatory skin disease with a known genetic component. Our previously published psoriasis genome-wide association study identified dozens of novel susceptibility loci in Han Chinese. However, these markers explained only a small fraction of the estimated heritable component of psoriasis. To better understand the unknown yet likely polygenic architecture in psoriasis, we applied a linear mixed model to quantify the variation in the liability to psoriasis explained by common genetic markers (minor allele frequency > 0.01) in a Han Chinese population. Results We explored the polygenic genetic architecture of psoriasis using genome-wide association data from 2,271 Han Chinese individuals. We estimated that 34.9% (s.e. = 6.0%, P = 9 × 10-9) of the variation in the liability to psoriasis is captured by common genotyped and imputed variants. We discuss these results in the context of the strong association between HLA variants and psoriasis. We also show that the variance explained by each chromosome is linearly correlated to its length (R2 = 0.27, P=0.01), and quantify the impact of a polygenic effect on the prediction and diagnosis of psoriasis. Conclusions Our results suggest that psoriasis has a substantial polygenic component, which not only has implications for the development of genetic diagnostics and prognostics for psoriasis, but also suggests that more individual variants contributing to psoriasis may be detected if sample sizes in future association studies are increased.

Background Regions of the genome that are under evolutionary constraint across multiple species have previously been used to identify functional sequences in the human genome. Furthermore, it is known that there is an inverse relationship between evolutionary constraint and the allele frequency of a mutation segregating in human populations, implying a direct relationship between interspecies divergence and fitness in humans. Here we utilise this relationship to test differences in the accumulation of putatively deleterious mutations both between populations and on the individual level. Results Using whole genome and exome sequencing data from Phase 1 of the 1000 Genome Project for 1,092 individuals from 14 worldwide populations we show that minor allele frequency (MAF) varies as a function of constraint around both coding regions and non-coding sites genome-wide, implying that negative, rather than positive, selection primarily drives the distribution of alleles among individuals via background selection. We find a strong relationship between effective population size and the depth of depression in MAF around the most conserved genes, suggesting that populations with smaller effective size are carrying more deleterious mutations, which also translates into higher genetic load when considering the number of putatively deleterious alleles segregating within each population. Finally, given the extreme richness of the data, we are now able to classify individual genomes by the accumulation of mutations at functional sites using high coverage 1000 Genomes data. Using this approach we detect differences between ‘healthy’ individuals within populations for the distributions of putatively deleterious rare alleles they are carrying. Conclusions These findings demonstrate the extent of background selection in the human genome and highlight the role of population history in shaping patterns of diversity between human individuals. Furthermore, we provide a framework for the utility of personal genomic data for the study of genetic fitness and diseases.

Submitted by Vitor Silverio Rodrigues (vitorsrodrigues@reitoria.unesp.br) on 2014-05-27T11:29:06Z No. of bitstreams: 0Bitstream added on 2014-05-27T14:32:32Z : No. of bitstreams: 1 2-s2.0-84876975374.pdf: 2094125 bytes, checksum: 6cd3b64c77b96637c2977088346cfc2e (MD5) Made available in DSpace on 2014-05-27T11:29:06Z (GMT). No. of bitstreams: 0 Previous issue date: 2013-05-05 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Background: Knowledge of the linkage disequilibrium (LD) between markers is important to establish the number of markers necessary for association studies and genomic selection. The objective of this study was to evaluate the extent of LD in Nellore cattle using a high density SNP panel and 795 genotyped steers.Results: After data editing, 446,986 SNPs were used for the estimation of LD, comprising 2508.4 Mb of the genome. The mean distance between adjacent markers was 4.90 ± 2.89 kb. The minor allele frequency (MAF) was less than 0.20 in a considerable proportion of SNPs. The overall mean LD between marker pairs measured by r2 and |D'| was 0.17 and 0.52, respectively. The LD (r2) decreased with increasing physical distance between markers from 0.34 (1 kb) to 0.11 (100 kb). In contrast to this clear decrease of LD measured by r2, the changes in |D'| indicated a less pronounced decline of LD. Chromosomes BTA1, BTA27, BTA28 and BTA29 showed lower levels of LD at any distance between markers. Except for these four chromosomes, the level of LD (r2) was higher than 0.20 for markers separated by less than 20 kb. At distances < 3 kb, the level of LD was higher than 0.30. The LD (r2) between markers was higher when the MAF threshold was high (0.15), especially when the distance between markers was short.Conclusions: The level of LD estimated for markers separated by less than 30 kb indicates that the High Density Bovine SNP BeadChip will likely be a suitable tool for prediction of genomic breeding values in Nellore cattle. © 2013 Espigolan et al.; licensee BioMed Central Ltd. Faculdade de Ciências Agrárias e Veterinárias UNESP, Jaboticabal, SP 14884-000 Instituto Nacional de Ciência e Tecnologia - Ciência Animal (INCT- CA), Viçosa, MG 36570 000 Departamento de Nutrição e Produção Animal Faculdade de Medicina Veterinária e Zootecnia USP, Pirassununga, SP Centre for Genetic Improvement of Livestock Department of Animal and Poultry Science University of Guelph, Guelph, ON CNPq Fellowship National Council of Technological and Scientific Development SHIS QI 1, Conjunto B, 71605-001, Brasília, DF GenSys Consultores Associados, Porto Alegre Faculdade de Ciências Agrárias e Veterinárias UNESP, Jaboticabal, SP 14884-000

Background Recent advances in sequencing technologies have greatly increased the identification of mutations in cancer genomes. However, it remains a significant challenge to identify cancer-driving mutations, since most observed missense changes are neutral passenger mutations. Various computational methods have been developed to predict the effects of amino acid substitutions on protein function and classify mutations as deleterious or benign. These include approaches that rely on evolutionary conservation, structural constraints, or physicochemical attributes of amino acid substitutions. Here we review existing methods and further examine eight tools: SIFT, PolyPhen2, Condel, CHASM, mCluster, logRE, SNAP, and MutationAssessor, with respect to their coverage, accuracy, availability and dependence on other tools. Results Single nucleotide polymorphisms with high minor allele frequencies were used as a negative (neutral) set for testing, and recurrent mutations from the COSMIC database as well as novel recurrent somatic mutations identified in very recent cancer studies were used as positive (non-neutral) sets. Conservation-based methods generally had moderately high accuracy in distinguishing neutral from deleterious mutations, whereas the performance of machine learning based predictors with comprehensive feature spaces varied between assessments using different positive sets. MutationAssessor consistently provided the highest accuracies. For certain combinations metapredictors slightly improved the performance of included individual methods, but did not outperform MutationAssessor as stand-alone tool. Conclusions Our independent assessment of existing tools reveals various performance disparities. Cancer-trained methods did not improve upon more general predictors. No method or combination of methods exceeds 81% accuracy, indicating there is still significant room for improvement for driver mutation prediction, and perhaps more sophisticated feature integration is needed to develop a more robust tool.

Background New sequencing technologies have tremendously increased the number of known molecular markers (single nucleotide polymorphisms; SNPs) in a variety of species. Concurrently, improvements to genotyping technology have now made it possible to efficiently genotype large numbers of genome-wide distributed SNPs enabling genome wide association studies (GWAS). However, genotyping significant numbers of individuals with large number of SNPs remains prohibitively expensive for many research groups. A possible solution to this problem is to determine allele frequencies from pooled DNA samples, such ‘allelotyping’ has been presented as a cost-effective alternative to individual genotyping and has become popular in human GWAS. In this article we have tested the effectiveness of DNA pooling to obtain accurate allele frequency estimates for Atlantic salmon (Salmo salar L.) populations using an Illumina SNP-chip. Results In total, 56 Atlantic salmon DNA pools from 14 populations were analyzed on an Atlantic salmon SNP-chip containing probes for 5568 SNP markers, 3928 of which were bi-allelic. We developed an efficient quality control filter which enables exclusion of loci showing high error rate and minor allele frequency (MAF) close to zero. After applying multiple quality control filters we obtained allele frequency estimates for 3631 bi-allelic loci. We observed high concordance (r > 0.99) between allele frequency estimates derived from individual genotyping and DNA pools. Our results also indicate that even relatively small DNA pools (35 individuals) can provide accurate allele frequency estimates for a given sample. Conclusions Despite of higher level of variation associated with array replicates compared to pool construction, we suggest that both sources of variation should be taken into account. This study demonstrates that DNA pooling allows fast and high-throughput determination of allele frequencies in Atlantic salmon enabling cost-efficient identification of informative markers for discrimination of populations at various geographical scales, as well as identification of loci controlling ecologically and economically important traits.

Background: In livestock species like the chicken, high throughput single nucleotide polymorphism (SNP) genotyping assays are increasingly being used for whole genome association studies and as a tool in breeding (referred to as genomic selection). To be of value in a wide variety of breeds and populations, the success rate of the SNP genotyping assay, the distribution of the SNP across the genome and the minor allele frequencies (MAF) of the SNPs used are extremely important. Results: We describe the design of a moderate density (60k) Illumina SNP BeadChip in chicken consisting of SNPs known to be segregating at high to medium minor allele frequencies (MAF) in the two major types of commercial chicken (broilers and layers). This was achieved by the identification of 352,303 SNPs with moderate to high MAF in 2 broilers and 2 layer lines using Illumina sequencing on reduced representation libraries. To further increase the utility of the chip, we also identified SNPs on sequences currently not covered by the chicken genome assembly (Gallus_gallus-2.1). This was achieved by 454 sequencing of the chicken genome at a depth of 12x and the identification of SNPs on 454-derived contigs not covered by the current chicken genome assembly. In total we added 790 SNPs that mapped to 454-derived contigs as well as 421 SNPs with a position on Chr_random of the current assembly. The SNP chip contains 57,636 SNPs of which 54,293 could be genotyped and were shown to be segregating in chicken populations. Our SNP identification procedure appeared to be highly reliable and the overall validation rate of the SNPs on the chip was 94%. We were able to map 328 SNPs derived from the 454 sequence contigs on the chicken genome. The majority of these SNPs map to chromosomes that are already represented in genome build Gallus_gallus-2.1.0. Twenty-eight SNPs were used to construct two new linkage groups most likely representing two micro-chromosomes not covered by the current genome assembly. Conclusions: The high success rate of the SNPs on the Illumina chicken 60K Beadchip emphasizes the power of Next generation sequence (NGS) technology for the SNP identification and selection step. The identification of SNPs from sequence contigs derived from NGS sequencing resulted in improved coverage of the chicken genome and the construction of two new linkage groups most likely representing two chicken micro-chromosomes. Background The development of high throughput SNP genotyping assays has radically changed the genetic dissection of complex traits in human, model organisms, and agricultural species. In farm animals, for many years, such studies were focused on linkage mapping in experimental crosses with the highly variable microsatellites as the markers of choice. More recently with the development

Background Next-generation sequencing technologies provide exciting avenues for studies of transcriptomics and population genomics. There is an increasing need to conduct spliced and unspliced alignments of short transcript reads onto a reference genome and estimate minor allele frequency from sequences of population samples. Results We have designed and implemented MapNext, a software tool for both spliced and unspliced alignments of short sequence reads onto reference sequences, and automated SNP detection using neighbourhood quality standards. MapNext provides four main analyses: (i) unspliced alignment and clustering of reads, (ii) spliced alignment of transcript reads over intron boundaries, (iii) SNP detection and estimation of minor allele frequency from population sequences, and (iv) storage of result data in a database to make it available for more flexible queries and for further analyses. The software tool has been tested using both simulated and real data. Conclusion MapNext is a comprehensive and powerful tool for both spliced and unspliced alignments of short reads and automated SNP detection from population sequences. The simplicity, flexibility and efficiency of MapNext makes it a valuable tool for transcriptomic and population genomic research.

Background Association mapping approaches are dependent upon discovery and validation of single nucleotide polymorphisms (SNPs). To further association studies in Anopheles gambiae we conducted a major resequencing programme, primarily targeting regions within or close to candidate genes for insecticide resistance. Results Using two pools of mosquito template DNA we sequenced over 300 kbp across 660 distinct amplicons of the An. gambiae genome. Comparison of SNPs identified from pooled templates with those from individual sequences revealed a very low false positive rate. False negative rates were much higher and mostly resulted from SNPs with a low minor allele frequency. Pooled-template sequencing also provided good estimates of SNP allele frequencies. Allele frequency estimation success, along with false positive and negative call rates, improved significantly when using a qualitative measure of SNP call quality. We identified a total of 7062 polymorphic features comprising 6995 SNPs and 67 indels, with, on average, a SNP every 34 bp; a high rate of polymorphism that is comparable to other studies of mosquitoes. SNPs were significantly more frequent in members of the cytochrome p450 mono-oxygenases and carboxy/cholinesterase gene-families than in glutathione-S-transferases, other detoxification genes, and control genomic regions. Polymorphic sites showed a significantly clustered distribution, but the degree of SNP clustering (independent of SNP frequency) did not vary among gene families, suggesting that clustering of polymorphisms is a general property of the An. gambiae genome. Conclusion The high frequency and clustering of SNPs has important ramifications for the design of high-throughput genotyping assays based on allele specific primer extension or probe hybridisation. We illustrate these issues in the context of the design of Illumina GoldenGate assays.

Background Genetic influences underpinning complex traits are thought to involve multiple quantitative trait loci (QTLs) of small effect size. Detection of such QTL associations requires systematic screening of large numbers of DNA markers within large sample populations. Using pooled DNA on SNP microarrays to screen for allelic frequency differences between groups such as cases and controls (called SNP Microarray and Pooling, or SNP-MaP) has been validated as an efficient solution on both 10 k and 100 k platforms. We demonstrate that this approach can be effectively applied to the truly genomewide Affymetrix GeneChip® Mapping 500 K Array. Results In comparisons between five independent DNA pools (N ~200 per pool) on separate Affymetrix GeneChip® Mapping 500 K Array sets, we show that, for SNPs with minor allele frequencies > 0.05, the reliability of the rank order of estimated allele frequencies, assessed as the average correlation between allele frequency estimates across the DNA pools, was 0.948 (average mean difference across the five pools = 0.069). Similarly, validity of the SNP-MaP approach was demonstrated by a rank-order correlation of 0.937 (average mean difference = 0.095) between the average DNA pool allele frequency estimates and the allele frequencies of an independent (CEPH) sample of 60 unrelated individually genotyped subjects. Conclusion We conclude that SNP-MaP can be extended for use on the Affymetrix GeneChip® Mapping 500 K Array, providing a cost-effective, reliable and valid initial screen of 500 K SNP microarrays in genomewide association scans.

Background Single nucleotide polymorphisms (SNPs) are an abundant form of genetic variation in the genome of every species and are useful for gene mapping and association studies. Of particular interest are non-synonymous SNPs, which may alter protein function and phenotype. We therefore examined bovine expressed sequences for non-synonymous SNPs and validated and tested selected SNPs for their association with measured traits. Results Over 500,000 public bovine expressed sequence tagged (EST) sequences were used to search for coding SNPs (cSNPs). A total of 15,353 SNPs were detected in the transcribed sequences studied, of which 6,325 were predicted to be coding SNPs with the remaining 9,028 SNPs presumed to be in untranslated regions. Of the cSNPs detected, 2,868 were predicted to result in a change in the amino acid encoded. In order to determine the actual number of non-synonymous polymorphic SNPs we designed assays for 920 of the putative SNPs. These SNPs were then genotyped through a panel of cattle DNA pools using chip-based MALDI-TOF mass spectrometry. Of the SNPs tested, 29% were found to be polymorphic with a minor allele frequency >10%. A subset of the SNPs was genotyped through animal resources in order to look for association with age of puberty, facial eczema resistance or meat yield. Three SNPs were nominally associated with resistance to the disease facial eczema (P < 0.01). Conclusion We have identified 15,353 putative SNPs in or close to bovine genes and 2,868 of these SNPs were predicted to be non-synonymous. Approximately 29% of the non-synonymous SNPs were polymorphic and common with a minor allele frequency >10%. Of the SNPs detected in this study, 99% have not been previously reported. These novel SNPs will be useful for association studies or gene mapping.

Background Genome-wide association studies (GWAS) have identified many common polymorphisms associated with complex traits. However, these associated common variants explain only a small fraction of the phenotypic variances, leaving a substantial portion of genetic heritability unexplained. As a result, searches for "missing" heritability are drawing increasing attention, particularly for rare variant studies that often require a large sample size and, thus, extensive sequencing effort. Although the development of next generation sequencing (NGS) technologies has made it possible to sequence a large number of reads economically and efficiently, it is still often cost prohibitive to sequence thousands of individuals that are generally required for association studies. A more efficient and cost-effective design would involve pooling the genetic materials of multiple individuals together and then sequencing the pools, instead of the individuals. This pooled sequencing approach has improved the plausibility of association studies for rare variants, while, at the same time, posed a great challenge to the pooled sequencing data analysis, essentially because individual sample identity is lost, and NGS sequencing errors could be hard to distinguish from low frequency alleles. Results A unified approach for estimating minor allele frequency, SNP calling and association studies based on pooled sequencing data using an expectation maximization (EM) algorithm is developed in this paper. This approach makes it possible to study the effects of minor allele frequency, sequencing error rate, number of pools, number of individuals in each pool, and the sequencing depth on the estimation accuracy of minor allele frequencies. We show that the naive method of estimating minor allele frequencies by taking the fraction of observed minor alleles can be significantly biased, especially for rare variants. In contrast, our EM approach can give an unbiased estimate of the minor allele frequency under all scenarios studied in this paper. A SNP calling approach, EM-SNP, for pooled sequencing data based on the EM algorithm is then developed and compared with another recent SNP calling method, SNVer. We show that EM-SNP outperforms SNVer in terms of the fraction of db-SNPs among the called SNPs, as well as transition/transversion (Ti/Tv) ratio. Finally, the EM approach is used to study the association between variants and type I diabetes. Conclusions The EM-based approach for the analysis of pooled sequencing data can accurately estimate minor allele frequencies, call SNPs, and find associations between variants and complex traits. This approach is especially useful for studies involving rare variants.

Background Rhesus macaques (Macaca mulatta) are the primate most used for biomedical research, but phenotypic differences between Indian-origin and Chinese rhesus macaques have encouraged genetic methods for identifying genetic differences between these two populations. The completion of the rhesus genome has led to the identification of many single nucleotide polymorphisms (SNPs) in this species. These single nucleotide polymorphisms have many advantages over the short tandem repeat (STR) loci currently used to assay genetic variation. However, the number of currently identified polymorphisms is too small for whole genome analysis or studies of quantitative trait loci. To that end, we tested a combination of methods to identify large numbers of high-confidence SNPs, and screen those with high minor allele frequencies (MAF). Results By testing our previously reported single nucleotide polymorphisms, we identified a subset of high-confidence, high-MAF polymorphisms. Resequencing revealed a large number of regionally specific SNPs not identified through a single pyrosequencing run. By resequencing a pooled sample of four individuals, we reliably identified loci with a MAF of at least 12.5%. Finally, we found that when applied to a larger, geographically variable sample of rhesus, a large proportion of our loci were variable in both populations, and very few loci were ancestry informative. Despite this fact, the SNP loci were more effective at discriminating Indian and Chinese rhesus than STR loci. Conclusion Pyrosequencing and pooled resequencing are viable methods for the identification of high-MAF SNP loci in rhesus macaques. These SNP loci are appropriate for screening both the inter- and intra-population genetic variation.

Genotypes not directly measured in genetic studies are often imputed to improve statistical power and to increase mapping resolution. The accuracy of standard imputation techniques strongly depends on the similarity of linkage disequilibrium (LD) patterns in the study and reference populations. Here we develop a novel approach for genotype imputation in low-recombination regions that relies on the coalescent and permits to explicitly account for population demographic factors. To test the new method, study and reference haplotypes were simulated and gene trees were inferred under the basic coalescent and also considering population growth and structure. The reference haplotypes that first coalesced with study haplotypes were used as templates for genotype imputation. Computer simulations were complemented with the analysis of real data. Genotype concordance rates were used to compare the accuracies of coalescent-based and standard (IMPUTE2) imputation. Simulations revealed that, in LD-blocks, imputation accuracy relying on the basic coalescent was higher and less variable than with IMPUTE2. Explicit consideration of population growth and structure, even if present, did not practically improve accuracy. The advantage of coalescent-based over standard imputation increased with the minor allele frequency and it decreased with population stratification. Results based on real data indicated that, even in low-recombination regions, further research is needed to incorporate recombination in coalescence inference, in particular for studies with genetically diverse and admixed individuals. To exploit the full potential of coalescent-based methods for the imputation of missing genotypes in genetic studies, further methodological research is needed to reduce computer time, to take into account recombination, and to implement these methods in user-friendly computer programs. Here we provide reproducible code which takes advantage of publicly available software to facilitate further developments in the field.

Background The variation in serum uric acid concentrations is under significant genetic influence. Elevated SUA concentrations have been linked to increased risk for gout, kidney stones, chronic kidney disease, and cardiovascular disease whereas reduced serum uric acid concentrations have been linked to multiple sclerosis, Parkinson’s disease and Alzheimer’s disease. Previously, we identified a novel locus on chromosome 3p26 affecting serum uric acid concentrations in Mexican Americans from San Antonio Family Heart Study. As a follow up, we examined genome-wide single nucleotide polymorphism data in an extended cohort of 1281 Mexican Americans from multigenerational families of the San Antonio Family Heart Study and the San Antonio Family Diabetes/Gallbladder Study. We used a linear regression-based joint linkage/association test under an additive model of allelic effect, while accounting for non-independence among family members via a kinship variance component. Results Univariate genetic analysis indicated serum uric acid concentrations to be significant heritable (h2 = 0.50 ± 0.05, p

Minor allele detection in very high coverage sequence data (>1000X) has many applications such as detecting mtDNA heteroplasmy, somatic mutations in cancer or tumors, SNP calling in pool sequencing, etc., where reads with low frequency are not necessarily sequence error but may instead convey biological information. However, the suitability of common base quality recalibration tools for such applications has not been investigated in detail. We show that the widely used tool GATK BaseRecalibration has several limitations in minor allele detection. First, GATK IndelRealignment fails to work if the sequence coverage is above a certain level since it then becomes computationally infeasible. Second, the accuracy of the base quality largely depends on the database of known SNPs as the control, which limits the ability of de novo minor allele detection. Third, GATK reduces the base quality of sequence errors at the cost of reducing scores for true minor alleles. To overcome these limitations, we present a novel approach called SEGREG, which applies segmented regression to control sequences (e.g. phiX174 DNA) spiked into a sequencing run. Based on simulations SEGREG improves both the accuracy of base quality scores and the detection of minor alleles. We further investigate sequence error and recalibration parameters by applying a Logarithm Likelihood Ratio (LLR) approach to SEGREG recalibrated base quality scores for phiX174 DNA sequenced to very high coverage, and for mtDNA genome sequences previously analyzed for heteroplasmic variants. Our results suggest that SEGREG improves base recalibration without suffering the limitations discussed above, and the LLR approach benefits from SEGREG in identifying more true minor alleles, while avoiding false positives from sequencing error.

Single nucleotide polymorphism (SNP) panels have been widely used to study genomic variations within and between populations. Methods of SNP discovery have been a matter of debate for their potential of introducing ascertainment bias, and genetic diversity results obtained from the SNP genotype data can be misleading. We used a total of 42 chicken populations where both individual genotyped array data and pool whole genome resequencing (WGS) data were available. We compared allele frequency distributions and genetic diversity measures (expected heterozygosity (H e ), fixation index (F ST ) values, genetic distances and principal components analysis (PCA)) between the two data types. With the array data, we applied different filtering options (SNPs polymorphic in samples of two Gallus gallus wild populations, linkage disequilibrium (LD) based pruning and minor allele frequency (MAF) filtering, and combinations thereof) to assess their potential to mitigate the ascertainment bias. Rare SNPs were underrepresented in the array data. Array data consistently overestimated H e compared to WGS data, however, with a similar ranking of the breeds, as demonstrated by Spearman’s rank correlations ranging between 0.956 and 0.985. LD based pruning resulted in a reduced overestimation of H e compared to the other filters and slightly improved the relationship with the WGS results. The raw array data and those with polymorphic SNPs in the wild samples underestimated pairwise F ST values between breeds which had low F ST (0.15). LD based pruned data underestimated F ST in a consistent manner. The genetic distance matrix from LD pruned data was more closely related to that of WGS than the other array versions. PCA was rather robust in all array versions, since the population structure on the PCA plot was generally well captured in comparison to the WGS data. Among the tested filtering strategies, LD based pruning was found to account for the effects of ascertainment bias in the relatively best way, producing results which are most comparable to those obtained from WGS data and therefore is recommended for practical use.

Background Imputation accuracy among other things depends on the size of the reference panel, the marker’s minor allele frequency (MAF), and the correct placement of single nucleotide polymorphism (SNP) on the reference genome assembly. Using high-density genotypes of 3938 Nellore cattle from Brazil, we investigated the accuracy of imputation from 50 K to 777 K SNP density using Minimac3, when map positions were determined according to the bovine genome assemblies UMD3.1 and ARS-UCD1.2. We assessed the effect of reference and target panel sizes on the pre-phasing based imputation quality using ten-fold cross-validation. Further, we compared the reliability of the model-based imputation quality score (Rsq) from Minimac3 to the empirical imputation accuracy. Results The overall accuracy of imputation measured as the squared correlation between true and imputed allele dosages (R2dose) was almost identical using either the UMD3.1 or ARS-UCD1.2 genome assembly. When the size of the reference panel increased from 250 to 2000, R2dose increased from 0.845 to 0.917, and the number of polymorphic markers in the imputed data set increased from 586,701 to 618,660. Advantages in both accuracy and marker density were also observed when larger target panels were imputed, likely resulting from more accurate haplotype inference. Imputation accuracy increased from 0.903 to 0.913, and the marker density in the imputed data increased from 593,239 to 595,570 when haplotypes were inferred in 500 and 2900 target animals. The model-based imputation quality scores from Minimac3 (Rsq) were systematically higher than empirically estimated accuracies. However, both metrics were positively correlated and the correlation increased with the size of the reference panel and MAF of imputed variants. Conclusions Accurate imputation of BovineHD BeadChip markers is possible in Nellore cattle using the new bovine reference genome assembly ARS-UCD1.2. The use of large reference and target panels improves the accuracy of the imputed genotypes and provides genotypes for more markers segregating at low frequency for downstream genomic analyses. The model-based imputation quality score from Minimac3 (Rsq) can be used to detect poorly imputed variants but its reliability depends on the size of the reference panel and MAF of the imputed variants.

The technical progress in the last decade has made it possible to sequence millions of DNA reads in a relatively short time frame. Several variant callers based on different algorithms have emerged and have made it possible to extract single nucleotide polymorphisms (SNPs) out of the whole-genome sequence. Often, only a few individuals of a population are sequenced completely and imputation is used to obtain genotypes for all sequence-based SNP loci for other individuals, which have been genotyped for a subset of SNPs using a genotyping array. First, we compared the sets of variants detected with different variant callers, namely GATK, freebayes and SAMtools, and checked the quality of genotypes of the called variants in a set of 50 fully sequenced white and brown layers. Second, we assessed the imputation accuracy (measured as the correlation between imputed and true genotype per SNP and per individual, and genotype conflict between father-progeny pairs) when imputing from high density SNP array data to whole-genome sequence using data from around 1000 individuals from six different generations. Three different imputation programs (Minimac, FImpute and IMPUTE2) were checked in different validation scenarios. There were 1,741,573 SNPs detected by all three callers on the studied chromosomes 3, 6, and 28, which was 71.6 % (81.6 %, 88.0 %) of SNPs detected by GATK (SAMtools, freebayes) in total. Genotype concordance (GC) defined as the proportion of individuals whose array-derived genotypes are the same as the sequence-derived genotypes over all non-missing SNPs on the array were 0.98 (GATK), 0.97 (freebayes) and 0.98 (SAMtools). Furthermore, the percentage of variants that had high values (>0.9) for another three measures (non-reference sensitivity, non-reference genotype concordance and precision) were 90 (88, 75) for GATK (SAMtools, freebayes). With all imputation programs, correlation between original and imputed genotypes was >0.95 on average with randomly masked 1000 SNPs from the SNP array and >0.85 for a leave-one-out cross-validation within sequenced individuals. Performance of all variant callers studied was very good in general, particularly for GATK and SAMtools. FImpute performed slightly worse than Minimac and IMPUTE2 in terms of genotype correlation, especially for SNPs with low minor allele frequency, while it had lowest numbers in Mendelian conflicts in available father-progeny pairs. Correlations of real and imputed genotypes remained constantly high even if individuals to be imputed were several generations away from the sequenced individuals.

Background Knowledge of germplasm diversity and relationships among elite breeding materials is fundamentally important in crop improvement. We genotyped 450 maize inbred lines developed and/or widely used by CIMMYT breeding programs in both Kenya and Zimbabwe using 1065 SNP markers to (i) investigate population structure and patterns of relationship of the germplasm for better exploitation in breeding programs; (ii) assess the usefulness of SNPs for identifying heterotic groups commonly used by CIMMYT breeding programs; and (iii) identify a subset of highly informative SNP markers for routine and low cost genotyping of CIMMYT germplasm in the region using uniplex assays. Results Genetic distance for about 94% of the pairs of lines fell between 0.300 and 0.400. Eighty four percent of the pairs of lines also showed relative kinship values ≤ 0.500. Model-based population structure analysis, principal component analysis, neighbor-joining cluster analysis and discriminant analysis revealed the presence of 3 major groups and generally agree with pedigree information. The SNP markers did not show clear separation of heterotic groups A and B that were established based on combining ability tests through diallel and line x tester analyses. Our results demonstrated large differences among the SNP markers in terms of reproducibility, ease of scoring, polymorphism, minor allele frequency and polymorphic information content. About 40% of the SNPs in the multiplexed chip-based GoldenGate assays were found to be uninformative in this study and we recommend 644 of the 1065 for low to medium density genotyping in tropical maize germplasm using uniplex assays. Conclusions There were high genetic distance and low kinship coefficients among most pairs of lines, clearly indicating the uniqueness of the majority of the inbred lines in these maize breeding programs. The results from this study will be useful to breeders in selecting best parental combinations for new breeding crosses, mapping population development and marker assisted breeding.

Background With improvements in genotyping technologies, genome-wide association studies with hundreds of thousands of SNPs allow the identification of candidate genetic loci for multifactorial diseases in different populations. However, genotyping errors caused by genotyping platforms or genotype calling algorithms may lead to inflation of false associations between markers and phenotypes. In addition, the number of SNPs available for genome-wide association studies in the Japanese population has been investigated using only 45 samples in the HapMap project, which could lead to an inaccurate estimation of the number of SNPs with low minor allele frequencies. We genotyped 400 Japanese samples in order to estimate the number of SNPs available for genome-wide association studies in the Japanese population and to examine the performance of the current SNP Array 6.0 platform and the genotype calling algorithm "Birdseed". Results About 20% of the 909,622 SNP markers on the array were revealed to be monomorphic in the Japanese population. Consequently, 661,599 SNPs were available for genome-wide association studies in the Japanese population, after excluding the poorly behaving SNPs. The Birdseed algorithm accurately determined the genotype calls of each sample with a high overall call rate of over 99.5% and a high concordance rate of over 99.8% using more than 48 samples after removing low-quality samples by adjusting QC criteria. Conclusion Our results confirmed that the SNP Array 6.0 platform reached the level reported by the manufacturer, and thus genome-wide association studies using the SNP Array 6.0 platform have considerable potential to identify candidate susceptibility or resistance genetic factors for multifactorial diseases in the Japanese population, as well as in other populations.

Background Many hypothesis-driven genetic studies require the ability to comprehensively and efficiently target specific regions of the genome to detect sequence variations. Often, sample availability is limited requiring the use of whole genome amplification (WGA). We evaluated a high-throughput microdroplet-based PCR approach in combination with next generation sequencing (NGS) to target 384 discrete exons from 373 genes involved in cancer. In our evaluation, we compared the performance of six non-amplified gDNA samples from two HapMap family trios. Three of these samples were also preamplified by WGA and evaluated. We tested sample pooling or multiplexing strategies at different stages of the tested targeted NGS (T-NGS) workflow. Results The results demonstrated comparable sequence performance between non-amplified and preamplified samples and between different indexing strategies [sequence specificity of 66.0% ± 3.4%, uniformity (coverage at 0.2× of the mean) of 85.6% ± 0.6%]. The average genotype concordance maintained across all the samples was 99.5% ± 0.4%, regardless of sample type or pooling strategy. We did not detect any errors in the Mendelian patterns of inheritance of genotypes between the parents and offspring within each trio. We also demonstrated the ability to detect minor allele frequencies within the pooled samples that conform to predicted models. Conclusion Our described PCR-based sample multiplex approach and the ability to use WGA material for NGS may enable researchers to perform deep resequencing studies and explore variants at very low frequencies and cost.

Background Serum alkaline phosphatase (ALP) is a complex phenotype influenced by both genetic and environmental factors. Recent Genome-Wide Association Studies (GWAS) have identified several loci affecting ALP levels; however, such studies in Chinese populations are limited. We performed a GWAS analyzing the association between 658,288 autosomal SNPs and serum ALP in 1,461 subjects, and replicated the top SNPs in an additional 8,830 healthy Chinese Han individuals. The interactions between significant locus and environmental factors on serum ALP levels were further investigated. Results The association between ABO locus and serum ALP levels was replicated (P = 2.50 × 10-21, 1.12 × 10-56 and 2.82 × 10-27 for SNP rs8176720, rs651007 and rs7025162 on ABO locus, respectively). SNP rs651007 accounted for 2.15% of the total variance of serum ALP levels independently of the other 2 SNPs. When comparing our findings with previously published studies, ethnic differences were observed across populations. A significant interaction between ABO rs651007 and overweight and obesity was observed (FDR for interaction was 0.036); for individuals with GG genotype, those with normal weight and those who were overweight or obese have similar serum ALP concentrations; minor allele A of rs651007 remarkably reduced serum ALP levels, but this effect was attenuated in overweight and obese individuals. Conclusions Our findings indicate that ABO locus is a major determinant for serum ALP levels in Chinese Han population. Overweight and obesity modifies the effect of ABO locus on serum ALP concentrations.

Background Atlantic cod (Gadus morhua) is a species with increasing economic significance for the aquaculture industry. The genetic improvement of cod will play a critical role in achieving successful large-scale aquaculture. While many microsatellite markers have been developed in cod, the number of single nucleotide polymorphisms (SNPs) is currently limited. Here we report the identification of SNPs from sequence data generated by a large-scale expressed sequence tag (EST) program, focusing on fish originating from Canadian waters. Results A total of 97976 ESTs were assembled to generate 13448 contigs. We detected 4753 SNPs that met our selection criteria (depth of coverage ≥ 4 reads; minor allele frequency > 25%). 3072 SNPs were selected for testing. The percentage of successful assays was 75%, with 2291 SNPs amplifying correctly. Of these, 607 (26%) SNPs were monomorphic for all populations tested. In total, 64 (4%) of SNPs are likely to represent duplicated genes or highly similar members of gene families, rather than alternative alleles of the same gene, since they showed a high frequency of heterozygosity. The remaining polymorphic SNPs (1620) were categorised as validated SNPs. The mean minor allele frequency of the validated loci was 0.258 (± 0.141). Of the 1514 contigs from which validated SNPs were selected, 31% have a significant blast hit. For the SNPs predicted to occur in coding regions (141), we determined that 36% (51) are non-synonymous. Many loci (1033 SNPs; 64%) are polymorphic in all populations tested. However a small number of SNPs (184) that are polymorphic in the Western Atlantic were monomorphic in fish tested from three European populations. A preliminary linkage map has been constructed with 23 major linkage groups and 924 mapped SNPs. Conclusions These SNPs represent powerful tools to accelerate the genetic improvement of cod aquaculture. They have been used to build a genetic linkage map that can be applied to quantitative trait locus (QTL) discovery. Since these SNPs were generated from ESTs, they are linked to specific genes. Genes that map within QTL intervals can be prioritized for testing to determine whether they contribute to observed phenotypes.

Background Association analysis is an alternative to conventional family-based methods to detect the location of gene(s) or quantitative trait loci (QTL) and provides relatively high resolution in terms of defining the genome position of a gene or QTL. Seed protein and oil concentration are quantitative traits which are determined by the interaction among many genes with small to moderate genetic effects and their interaction with the environment. In this study, a genome-wide association study (GWAS) was performed to identify quantitative trait loci (QTL) controlling seed protein and oil concentration in 298 soybean germplasm accessions exhibiting a wide range of seed protein and oil content. Results A total of 55,159 single nucleotide polymorphisms (SNPs) were genotyped using various methods including Illumina Infinium and GoldenGate assays and 31,954 markers with minor allele frequency >0.10 were used to estimate linkage disequilibrium (LD) in heterochromatic and euchromatic regions. In euchromatic regions, the mean LD (r 2 ) rapidly declined to 0.2 within 360 Kbp, whereas the mean LD declined to 0.2 at 9,600 Kbp in heterochromatic regions. The GWAS results identified 40 SNPs in 17 different genomic regions significantly associated with seed protein. Of these, the five SNPs with the highest associations and seven adjacent SNPs were located in the 27.6-30.0 Mbp region of Gm20. A major seed protein QTL has been previously mapped to the same location and potential candidate genes have recently been identified in this region. The GWAS results also detected 25 SNPs in 13 different genomic regions associated with seed oil. Of these markers, seven SNPs had a significant association with both protein and oil. Conclusions This research indicated that GWAS not only identified most of the previously reported QTL controlling seed protein and oil, but also resulted in narrower genomic regions than the regions reported as containing these QTL. The narrower GWAS-defined genome regions will allow more precise marker-assisted allele selection and will expedite positional cloning of the causal gene(s).

Background Genome-wide association studies (GWAS) using array-based genotyping technology are widely used to identify genetic loci associated with complex diseases or other phenotypes. The costs of GWAS projects based on individual genotyping are still comparatively high and increase with the size of study populations. Genotyping using pooled DNA samples, as also being referred as to allelotyping approach, offers an alternative at affordable costs. In the present study, data from 100 DNA samples individually genotyped with the Affymetrix Genome-Wide Human SNP Array 6.0 were used to estimate the error of the pooling approach by comparing the results with those obtained using the same array type but DNA pools each composed of 50 of the same samples. Newly developed and established methods for signal intensity correction were applied. Furthermore, the relative allele intensity signals (RAS) obtained by allelotyping were compared to the corresponding values derived from individual genotyping. Similarly, differences in RAS values between pools were determined and compared. Results Regardless of the intensity correction method applied, the pooling-specific error of the pool intensity values was larger for single pools than for the comparison of the intensity values of two pools, which reflects the scenario of a case–control study. Using 50 pooled samples and analyzing 10,000 SNPs with a minor allele frequency of >1% and applying the best correction method for the corresponding type of comparison, the 90% quantile (median) of the pooling-specific absolute error of the RAS values for single sub-pools and the SNP-specific difference in allele frequency comparing two pools was 0.064 (0.026) and 0.056 (0.021), respectively. Conclusions Correction of the RAS values reduced the error of the RAS values when analyzing single pool intensities. We developed a new correction method with high accuracy but low computational costs. Correction of RAS, however, only marginally reduced the error of true differences between two sample groups and those obtained by allelotyping. Exclusion of SNPs with a minor allele frequency of ≤1% notably reduced the pooling-specific error. Our findings allow for improving the estimation of the pooling-specific error and may help in designing allelotyping studies using the Affymetrix Genome-Wide Human SNP Array 6.0.