Large SNP Research Articles

Insights into the structural and functional analysis of impact of the missense mutations on α-synuclein: an in silico study

BackgroundAlpha synuclein (α-synuclein) is coded by SNCA gene and found in a helical form with phospholipids or in an unfolded arrangement in the cytosol and belongs to the synuclein family other than beta synuclein and gamma synuclein. It is a short protein made of 140 amino acids with three domains: an N-terminal lipid binding helix, a non-amyloid-ß component (NAC), and an acidic tail at the C-terminus. α-Synuclein is present in aggregated and fibrillar form in Lewy bodies and its association has been related to multiple system atrophy (MSA), Parkinson’s disease (PD), and Dementia with Lewy bodies (DLB). Our objective is to investigate and prioritise the possible nsSNPs in the α-synuclein protein that have been potentially connected to human neurodegenerative diseases.ResultsWe used the series of computational tools to predict the mutation's harmful effect on three-dimensional structure of α-synuclein based on consensus approach. Our findings pointed to a significant computational blueprint for discovering nsSNPs connected to neurodegenerative illnesses from a large SNP data set while also minimising the expenses of experimentally showing harmful nsSNPs.ConclusionsThe prioritised G25S (rs1433622151), V66E (rs1261243630), and V77D (rs745815563) mutations can be employed in additional experimental studies to assess the α-synuclein protein mutation in relation to neurodegenerative illnesses and develop a therapeutics against them.Graphical

Population Genomics of Commercial Fish Sebastes schlegelii of the Bohai and Yellow Seas (China) Using a Large SNP Panel from GBS.

Sebastes schlegelii is one of the most commercially important marine fish in the northwestern Pacific. However, little information about the genome-wide genetic characteristics is available for S. schlegelii individuals from the Bohai and Yellow Seas. In this study, a total of 157,778, 174,480, and 188,756 single-nucleotide polymorphisms from Dalian (DL), Yantai (YT), and Qingdao (QD) coastal waters of China were, respectively, identified. Sixty samples (twenty samples per population) were clustered together, indicating shallow structures and close relationships with each other. The observed heterozygosity, expected heterozygosity, polymorphism information content, and nucleotide diversity ranged from 0.14316 to 0.17684, from 0.14035 to 0.17145, from 0.20672 to 0.24678, and from 7.63 × 10-6 to 8.77 × 10-6, respectively, indicating the slight difference in genetic diversity among S. schlegelii populations, and their general genetic diversity was lower compared to other marine fishes. The population divergence showed relatively low levels (from 0.01356 to 0.01678) between S. schlegelii populations. Dispersing along drifting seaweeds, as well as the ocean current that flows along the western and northern coasts of the Yellow Sea and southward along the eastern coast of China might be the major reasons for the weak genetic differentiation. These results form the basis of the population genetic characteristics of S. schlegelii based on GBS (Genotyping by Sequencing). In addition to basic population genetic information, our results provid a theoretical basis for further studies aimed at protecting and utilizing S. schlegelii resources.

An ensemble penalized regression method for multi-ancestry polygenic risk prediction

Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination of L1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{{{{\\mathscr{L}}}}}}}_{1}$$\\end{document} (lasso) and L2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{{{{\\mathscr{L}}}}}}}_{2}$$\\end{document} (ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R2 for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations.

Whole-genome resequencing uncovers diversity and selective sweep in Kazakh cattle.

The Kazakh cattle in the Xinjiang Uygur Autonomous Region of China are highly adaptable and have multiple uses, including milk and meat production, and use as draft animals. They are an excellent original breed that could be enhanced by breeding and hybrid improvement. However, the genomic diversity and signature of selection underlying the germplasm characteristics require further elucidation. Herein, we evaluated 26 Kazakh cattle genomes in comparison with 103 genomes of seven other cattle breeds from regions around the world to assess the Kazakh cattle genetic variability. We revealed that the relatively low linkage disequilibrium at large SNP distances was strongly correlated with the largest effective population size among Kazakh cattle. Using population structural analysis, we next demonstrated a taurine lineage with restricted Bos indicus introgression among Kazakh cattle. Notably, we identified putative selected genes associated with resistance to disease and body size within Kazakh cattle. Together, our findings shed light on the evolutionary history and breeding profile of Kazakh cattle, as well as offering indispensable resources for germplasm resource conservation and crossbreeding program implementation.

Genotype imputation in F2 crosses of inbred lines.

Crosses among inbred lines are a fundamental tool for the discovery of genetic loci associated with phenotypes of interest. In organisms for which large reference panels or SNP chips are not available, imputation from low-pass whole-genome sequencing is an effective method for obtaining genotype data from a large number of individuals. To date, a structured analysis of the conditions required for optimal genotype imputation has not been performed. We report a systematic exploration of the effect of several design variables on imputation performance in F2 crosses of inbred medaka lines using the imputation software STITCH. We determined that, depending on the number of samples, imputation performance reaches a plateau when increasing the per-sample sequencing coverage. We also systematically explored the trade-offs between cost, imputation accuracy, and sample numbers. We developed a computational pipeline to streamline the process, enabling other researchers to perform a similar cost-benefit analysis on their population of interest. The source code for the pipeline is available at https://github.com/birneylab/stitchimpute. While our pipeline has been developed and tested for an F2 population, the software can also be used to analyse populations with a different structure.

Unmasking microsatellite deceptiveness and debunking hybridization with SNPs in four marine copepod species of Calanus.

Interspecific hybridization events are on the rise in natural systems due to climate change disrupting species barriers. Across taxa, microsatellites have long been the molecular markers of choice to identify admixed individuals. However, with the advent of high-throughput sequencing easing the generation of genome-wide datasets, incorrect reports of hybridization resulting from microsatellite technical artefacts have been uncovered in a growing number of taxa. In the marine zooplankton genus Calanus (Copepoda), whose species are used as climate change indicators, microsatellite markers have suggested hybridization between C. finmarchicus and C. glacialis, while other nuclear markers (InDels) never detected any admixed individuals, leaving the scientific community divided. Here, for the first time, we investigated the potential for hybridization among C. finmarchicus, C. glacialis, C. helgolandicus and C. hyperboreus using two large and independent SNP datasets. These were derived firstly from a protocol of target-capture applied to 179 individuals collected from 17 sites across the North Atlantic and Arctic Oceans, including sympatric areas, and second from published RNA sequences. All SNP-based analyses were congruent in showing that Calanus species are distinct and do not appear to hybridize. We then thoroughly re-assessed the microsatellites showing hybrids, with the support of published transcriptomes, and identified technical issues plaguing eight out of 10 microsatellites, including size homoplasy, paralogy, potential for null alleles and even two primer pairs targeting the same locus. Our study illustrates how deceptive microsatellites can be when applied to the investigation of hybridization.

The value of a large Pisum SNP dataset

The value of a large Pisum SNP dataset

A cost–benefit analysis for use of large SNP panels and high throughput typing for forensic investigative genetic genealogy

Next-generation sequencing (NGS), also known as massively sequencing, enables large dense SNP panel analyses which generate the genetic component of forensic investigative genetic genealogy (FIGG). While the costs of implementing large SNP panel analyses into the laboratory system may seem high and daunting, the benefits of the technology may more than justify the investment. To determine if an infrastructural investment in public laboratories and using large SNP panel analyses would reap substantial benefits to society, a cost–benefit analysis (CBA) was performed. This CBA applied the logic that an increase of DNA profile uploads to a DNA database due to a sheer increase in number of markers and a greater sensitivity of detection afforded with NGS and a higher hit/association rate due to large SNP/kinship resolution and genealogy will increase investigative leads, will be more effective for identifying recidivists which in turn reduces future victims of crime, and will bring greater safety and security to communities. Analyses were performed for worst case/best case scenarios as well as by simulation sampling the range spaces with multiple input values simultaneously to generate best estimate summary statistics. This study shows that the benefits, both tangible and intangible, over the lifetime of an advanced database system would be huge and can be projected to be for less than $1 billion per year (over a 10-year period) investment can reap on average > $4.8 billion in tangible and intangible cost-benefits per year. More importantly, on average > 50,000 individuals need not become victims if FIGG were employed, assuming investigative associations generated were acted upon. The benefit to society is immense making the laboratory investment a nominal cost. The benefits likely are underestimated herein. There is latitude in the estimated costs, and even if they were doubled or tripled, there would still be substantial benefits gained with a FIGG-based approach. While the data used in this CBA are US centric (primarily because data were readily accessible), the model is generalizable and could be used by other jurisdictions to perform relevant and representative CBAs.

Preselection of QTL markers enhances accuracy of genomic selection in Norway spruce

Genomic prediction (GP) or genomic selection is a method to predict the accumulative effect of all quantitative trait loci (QTLs) in a population by estimating the realized genomic relationships between the individuals and by capturing the linkage disequilibrium between markers and QTLs. Thus, marker preselection is considered a promising method to capture Mendelian segregation effects. Using QTLs detected in a genome-wide association study (GWAS) may improve GP. Here, we performed GWAS and GP in a population with 904 clones from 32 full-sib families using a newly developed 50 k SNP Norway spruce array. Through GWAS we identified 41 SNPs associated with budburst stage (BB) and the largest effect association explained 5.1% of the phenotypic variation (PVE). For the other five traits such as growth and wood quality traits, only 2 – 13 associations were observed and the PVE of the strongest effects ranged from 1.2% to 2.0%. GP using approximately 100 preselected SNPs, based on the smallest p-values from GWAS showed the greatest predictive ability (PA) for the trait BB. For the other traits, a preselection of 2000–4000 SNPs, was found to offer the best model fit according to the Akaike information criterion being minimized. But PA-magnitudes from GP using such selections were still similar to that of GP using all markers. Analyses on both real-life and simulated data also showed that the inclusion of a large QTL SNP in the model as a fixed effect could improve PA and accuracy of GP provided that the PVE of the QTL was ≥ 2.5%.

Accurate and Effective Detection of Recurrent Copy Number Variants in Large SNP Genotype Datasets.

Structural variations, including recurrent Copy Number Variants (CNVs) at specific genomic loci, have been found to be associated with increased risk of several diseases and syndromes. CNV carrier status can be determined in large collections of samples using SNP arrays and, more recently, sequencing data. Although there is some consensus among researchers about the essential steps required in such analysis (i.e., CNV calling, filtering of putative carriers, and visual validation using intensity data plots of the genomic region), standard methodologies and processes to control the quality and consistency of the results are lacking. Here, we present a comprehensive and user-friendly protocol that we have refined from our extensive research experience in the field. We cover every aspect of the analysis, from input data curation to final results. For each step, we highlight which parameters affect the analysis the most and how different settings may lead to different results. We provide a pipeline to run the complete analysis with effective (but customizable) pre-sets. We present software that we developed to better handle and filter putative CNV carriers and perform visual inspection to validate selected candidates. Finally, we describe methods to evaluate the critical sections and actions to counterbalance potential problems. The current implementation is focused on Illumina SNP array data. All the presented software is freely available and provided in a ready-to-use docker container. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: From raw intensity data files to CNV calls Basic Protocol 2: From CNV calls to validated CNV carriers. Basic Protocol 3: Quality control and quality assessment Basic Protocol 4: Install the necessary software.

Identification of copy number variations and candidate genes for reproduction traits in Finnish pig populations

Animal breeding programs can be improved by genetic markers associated with production and reproduction traits.Reproduction traits are important for economic success of pig production and therefore development of genetictools for selection is of high interest to pig breeding. In this study our objective was to identify genomic regions associatedwith fertility traits in two Finnish pig breeds using large scale SNP genotyping and genome wide associationanalysis and characterization of copy number variations (CNV). Since CNVs are structural variations of the genomethey potentially have a large effect on gene expression and protein function. We analyzed 1265 genotyped boarsfor nine different reproduction traits and identified 46 CNV regions encompassing 13 genes. 11 of the CNV regionswere shared between the breeds, 20 were unique to the Finnish Yorkshire and 15 to the Finnish Landrace. The genomewide association (GWAS) analysis identified zero to five reproduction associated genomic regions per trait.Furthermore, we identified 23 genomic regions with 20 candidate genes associated with fertility traits using GWASanalysis. The identified CNV regions were compared against GWAS regions to detect candidate regions with an effecton reproduction traits. This study reports candidate genes and genomic regions within two Finnish pig breedsfor reproduction traits, which can be utilized in breeding programs.

Development and inter-laboratory evaluation of the VISAGE Enhanced Tool for Appearance and Ancestry inference from DNA

Responding to the growing scientific and practical interest in forensic DNA phenotyping, the VISible Attributes through GEnomics (VISAGE) Consortium was founded in 2017 with the main goal of developing and validating new and reliable molecular and statistical tools to predict appearance, ancestry and age from DNA. Here, we describe the development and inter-laboratory evaluation and validation of the VISAGE Enhanced Tool for Appearance and Ancestry inference from DNA. The VISAGE Enhanced Tool for Appearance and Ancestry is the first forensic-driven genetic laboratory tool that comprises well-established markers for eye, hair and skin color with more recently discovered DNA markers for eyebrow color, freckling, hair shape and male pattern baldness and bio-geographic ancestry informative DNA markers. The bio-geographic ancestry markers include autosomal SNPs (bi- and tri-allelic SNPs), X-SNPs, Y-SNPs and autosomal Microhaplotypes. In total, primers targeting 524 SNPs (representing a 97.6% assay conversion rate) were successfully designed using AmpliSeq into a single primer pool (i.e., one multiplex assay) and sequenced with the Ion S5. In a collaborative framework, five VISAGE laboratories tested the VISAGE Enhanced Tool for Appearance and Ancestry on reproducibility, sensitivity, genotyping concordance, mixtures, species specificity and performance in relevant forensic conditions, including inhibitor-spiked, mock casework and artificially degraded samples. Based on our results, the VISAGE Enhanced Tool for Appearance and Ancestry is a robust, reproducible, and – for the large SNP number - fairly sensitive MPS assay with high concordance rates. With the VISAGE Enhanced Tool for Appearance and Ancestry introduced here, the VISAGE Consortium delivers the first single DNA-test for combined appearance prediction based on seven traits together with bio-geographic ancestry inference based on major continental regions for separated bi-parental and paternal ancestry, which represents the most comprehensive validated laboratory tool currently available for Forensic DNA Phenotyping.

SNPPar: identifying convergent evolution and other homoplasies from microbial whole-genome alignments.

Homoplasic SNPs are considered important signatures of strong (positive) selective pressure, and hence of adaptive evolution for clinically relevant traits such as antibiotic resistance and virulence. Here we present a new tool, SNPPar, for efficient detection and analysis of homoplasic SNPs from large whole genome sequencing datasets (>1000 isolates and/or >100 000 SNPs). SNPPar takes as input an SNP alignment, tree and annotated reference genome, and uses a combination of simple monophyly tests and ancestral state reconstruction (ASR, via TreeTime) to assign mutation events to branches and identify homoplasies. Mutations are annotated at the level of codon and gene, to facilitate analysis of convergent evolution. Testing on simulated data (120 Mycobacterium tuberculosis alignments representing local and global samples) showed SNPPar can detect homoplasic SNPs with very high specificity (zero false-positives in all tests) and high sensitivity (zero false-negatives in 89 % of tests). SNPPar analysis of three empirically sampled datasets ( Elizabethkingia anophelis , Burkholderia dolosa and M. tuberculosis ) produced results that were in concordance with previous studies, in terms of both individual homoplasies and evidence of convergence at the codon and gene levels. SNPPar analysis of a simulated alignment of ~64 000 genome-wide SNPs from 2000 M. tuberculosis genomes took ~23 min and ~2.6 GB of RAM to generate complete annotated results on a laptop. This analysis required ASR be conducted for only 1.25 % of SNPs, and the ASR step took ~23 s and 0.4 GB of RAM. SNPPar automates the detection and annotation of homoplasic SNPs efficiently and accurately from large SNP alignments. As demonstrated by the examples included here, this information can be readily used to explore the role of homoplasy in parallel and/or convergent evolution at the level of nucleotide, codon and/or gene.

The use of shared haplotype length information for pedigree reconstruction in asexually propagated outbreeding crops, demonstrated for apple and sweet cherry

Pedigree information is of fundamental importance in breeding programs and related genetics efforts. However, many individuals have unknown pedigrees. While methods to identify and confirm direct parent–offspring relationships are routine, those for other types of close relationships have yet to be effectively and widely implemented with plants, due to complications such as asexual propagation and extensive inbreeding. The objective of this study was to develop and demonstrate methods that support complex pedigree reconstruction via the total length of identical by state haplotypes (referred to in this study as “summed potential lengths of shared haplotypes”, SPLoSH). A custom Python script, HapShared, was developed to generate SPLoSH data in apple and sweet cherry. HapShared was used to establish empirical distributions of SPLoSH data for known relationships in these crops. These distributions were then used to estimate previously unknown relationships. Case studies in each crop demonstrated various pedigree reconstruction scenarios using SPLoSH data. For cherry, a full-sib relationship was deduced for ‘Emperor Francis, and ‘Schmidt’, a half-sib relationship for ‘Van’ and ‘Windsor’, and the paternal grandparents of ‘Stella’ were confirmed. For apple, 29 cultivars were found to share an unknown parent, the pedigree of the unknown parent of ‘Cox’s Pomona’ was reconstructed, and ‘Fameuse’ was deduced to be a likely grandparent of ‘McIntosh’. Key genetic resources that enabled this empirical study were large genome-wide SNP array datasets, integrated genetic maps, and previously identified pedigree relationships. Crops with similar resources are also expected to benefit from using HapShared for empowering pedigree reconstruction.

The effects of climate and demographic history in shaping genomic variation across populations of the Desert Horned Lizard (Phrynosoma platyrhinos).

Species often experience spatial environmental heterogeneity across their range, and populations may exhibit signatures of adaptation to local environmental characteristics. Other population genetic processes, such as migration and genetic drift, can impede the effects of local adaptation. Genetic drift in particular can have a pronounced effect on population genetic structure during large-scale geographic expansions, where a series of founder effects leads to decreases in genetic variation in the direction of the expansion. Here, we explore the genetic diversity of a desert lizard that occupies a wide range of environmental conditions and that has experienced post-glacial expansion northwards along two colonization routes. Based on our analyses of a large SNP data set, we find evidence that both climate and demographic history have shaped the genetic structure of populations. Pronounced genetic differentiation was evident between populations occupying cold versus hot deserts, and we detected numerous loci with significant associations with climate. The genetic signal of founder effects, however, is still present in the genomes of the recently expanded populations, which comprise subsets of genetic variation found in the southern populations.

Extending approximate Bayesian computation with supervised machine learning to infer demographic history from genetic polymorphisms using DIYABC Random Forest.

Simulation‐based methods such as approximate Bayesian computation (ABC) are well‐adapted to the analysis of complex scenarios of populations and species genetic history. In this context, supervised machine learning (SML) methods provide attractive statistical solutions to conduct efficient inferences about scenario choice and parameter estimation. The Random Forest methodology (RF) is a powerful ensemble of SML algorithms used for classification or regression problems. Random Forest allows conducting inferences at a low computational cost, without preliminary selection of the relevant components of the ABC summary statistics, and bypassing the derivation of ABC tolerance levels. We have implemented a set of RF algorithms to process inferences using simulated data sets generated from an extended version of the population genetic simulator implemented in DIYABC v2.1.0. The resulting computer package, named DIYABC Random Forest v1.0, integrates two functionalities into a user‐friendly interface: the simulation under custom evolutionary scenarios of different types of molecular data (microsatellites, DNA sequences or SNPs) and RF treatments including statistical tools to evaluate the power and accuracy of inferences. We illustrate the functionalities of DIYABC Random Forest v1.0 for both scenario choice and parameter estimation through the analysis of pseudo‐observed and real data sets corresponding to pool‐sequencing and individual‐sequencing SNP data sets. Because of the properties inherent to the implemented RF methods and the large feature vector (including various summary statistics and their linear combinations) available for SNP data, DIYABC Random Forest v1.0 can efficiently contribute to the analysis of large SNP data sets to make inferences about complex population genetic histories.

A simple sexing test for elephant species and its application to faecal DNA.

We developed a novel real-time PCR assay for rapid sexing in all three elephant species, which amplifies small fragments of the orthologous sexual chromosome zinc finger protein genes ZFX/ZFY (65 bp). This assay is a simple, inexpensive and reliable tool that is suitable for non-invasive DNA samples and can be incorporated into larger SNP panels for individual identification and population genetic studies.

A multi-breed GWAS for morphometric traits in four Beninese indigenous cattle breeds reveals loci associated with conformation, carcass and adaptive traits

BackgroundSpecific adaptive features including disease resistance and growth abilities in harsh environments are attributed to indigenous cattle breeds of Benin, but these breeds are endangered due to crossbreeding. So far, there is a lack of systematic trait recording, being the basis for breed characterizations, and for structured breeding program designs aiming on conservation. Bridging this gap, own phenotyping for morphological traits considered measurements for height at withers (HAW), sacrum height (SH), heart girth (HG), hip width (HW), body length (BL) and ear length (EL), including 449 cattle from the four indigenous Benin breeds Lagune, Somba, Borgou and Pabli. In order to utilize recent genomic tools for breed characterizations and genetic evaluations, phenotypes for novel traits were merged with high-density SNP marker data. Multi-breed genetic parameter estimations and genome-wide association studies (GWAS) for the six morphometric traits were carried out. Continuatively, we aimed on inferring genomic regions and functional loci potentially associated with conformation, carcass and adaptive traits.ResultsSNP-based heritability estimates for the morphometric traits ranged between 0.46 ± 0.14 (HG) and 0.74 ± 0.13 (HW). Phenotypic and genetic correlations ranged from 0.25 ± 0.05 (HW-BL) to 0.89 ± 0.01 (HAW-SH), and from 0.14 ± 0.10 (HW-BL) to 0.85 ± 0.02 (HAW-SH), respectively. Three genome-wide and 25 chromosome-wide significant SNP positioned on different chromosomes were detected, located in very close chromosomal distance (±25 kb) to 15 genes (or located within the genes). The genes PIK3R6 and PIK3R1 showed direct functional associations with height and body size. We inferred the potential candidate genes VEPH1, CNTNAP5, GYPC for conformation, growth and carcass traits including body weight and body fat deposition. According to their functional annotations, detected potential candidate genes were associated with stress or immune response (genes PTAFR, PBRM1, ADAMTS12) and with feed efficiency (genes MEGF11 SLC16A4, CCDC117).ConclusionsAccurate measurements contributed to large SNP heritabilities for some morphological traits, even for a small mixed-breed sample size. Multi-breed GWAS detected different loci associated with conformation or carcass traits. The identified potential candidate genes for immune response or feed efficiency indicators reflect the evolutionary development and adaptability features of the breeds.

The role of gene discovery, QTL analyses and gene expression in reproductive traits in the pig

The reproductive performance of the sow is one of the key factors affecting production profitability of the pig industry. Reproductive traits are in general, lowly heritable, and with reliable markers, they can be used to enhance current selection procedures for improvement of these traits. To find potential markers, large scale quantitative trait loci (QTL) and candidate gene studies have been conducted for reproductive traits. The present review discusses QTL and candidate gene discovery, large scale SNP association studies, gene expression profiling and discovery of miRNA regulation of pig reproductive tissues. Many QTL have been found for reproduction traits and a limited number of useful genes (e.g.: ESR1, PRLR, FSHB, EPOR and RBP4) have been found to have significant associations with reproductive traits. Expression studies with reproductive tissues have revealed differential expression within a few gene networks which need further mapping and association analyses to select prospective gene markers. The near completion of the pig genome sequence and the development of high density SNP chips will allow for large scale SNP association studies for pig reproductive traits in the future. Collection of appropriate phenotypes in large numbers and in broad populations representative of the swine industry are required if such genomic studies will ultimately be successful.

Large scale SNP unearthing and genetic architecture analysis in sea-captured and cultured populations of Cynoglossus semilaevis

Knowledge on population structure and genetic diversity is a focal point for association mapping studies and genomic selection. Genotyping by sequencing (GBS) represents an innovative method for large scale SNP detection and genotyping of genetic resources. Here we used the GBS approach for the genome-wide identification of SNPs in a collection of Cynoglossus semilaevis and for the assessment of the level of genetic diversity in C. semilaevis genotypes. GBS analysis generated a total of 55.12 Gb high-quality sequence data, with an average of 0.63 Gb per sample. The total number of SNP markers was 563, 109. In order to explore the genetic diversity of C. semilaevis and to select a minimal core set representing most of the total genetic variation with minimum redundancy, C. semilaevis sequences were analyzed using high quality SNPs. Based on hierarchical clustering, it was possible to divide the collection into 2 clusters. The marine fishing populations were clustered and clearly separated from the cultured populations, and the cultured populations from Hebei was also distinct from the other two local populations. These analyses showed that genotypes were clustered based on species-related features. Differential significant SNPs were also captured and validated by GBS and SNaPshot, with linkage disequilibrium and haplotype analysis, seven SNPs have been confirmed to have obvious differentiation in two populations, which may be used as the characteristic evaluation sites of sea-captured and cultured Cynoglossus semilaevis populations. And SNP markers and information on population structure developed in this study will undoubtedly support genome-wide association mapping studies and marker-assisted selection programs. These differential SNPs could be also employed as the characteristic evaluation sites of sea-captured and cultured Cynoglossus semilaevis populations in future.