Linkage Disequilibrium Structure Research Articles

THE APOLIPOPROTEIN E2 CODING POLYMORPHISM AND LINKAGE DISEQUILIBRIUM STRUCTURE IN ALZHEIMER’S DISEASE IN THE APOE REGION

Elucidating genetic predisposition to Alzheimer's diseases (AD) is complicated by AD etiologic complexity, polygenicity, and the lack of apparent evolutionary forces in establishing their molecular mechanisms. These factors are inevitable sources genetic heterogeneity in predisposition to AD, which contribute to significant differences in linkage disequilibrium (LD) structures in the ApoE region in AD-affected and unaffected subjects (Kulminski et al., Aging Cell, e12779, PMID: 29797398). Here, we examined LD (r, %) between rs7412, whose minor allele codes the AD-protective ApoE2 allele, and 31 single nucleotide polymorphisms (SNPs) representing BCAM-NECTIN2-TOMM40-ApoE-ApoC1 locus in: (i) 2,673 AD-affected and 16,246 unaffected Whites, who were mainly older than 55 years, from four independent studies, and (ii) 16,246 older unaffected subjects and 5,303 younger subjects aged 20–55 years from two additional studies. Analysis of the mega sample of all studies combined found that LD between AD-affected and unaffected subjects was significantly different for 23 of 31 SNP pairs, attaining nominal (0.005<p≤0.05), suggestive (10−4<p≤0.005), and locus-wide (p≤10−4) significances for six, eight, and nine SNP pairs, respectively (Figure 1). LD of rs7412 consistently and significantly decreased in AD-cases with 21 of these 23 SNPs, except the ApoE4-coding rs429358 and its ‘proxy’ rs2075650. The ApoE2-related LD difference was observed for SNPs from all genes in the locus and it was not due to LD between those SNPs. Cohort-specific analysis of SNP pairs with locus-wide significance showed that LD consistently decreased in independent studies (Figure 2). No significant differences in LD was observed between older AD-unaffected and younger subjects.

A statistical approach to fine-mapping for the identification of potential causal variants related to human intelligence.

Genome-wide association studies (GWASs) have identified >20 genetic loci associated with human intelligence. However, due to correlations between the trait-associated SNPs, only a few of the loci are confirmed to have a true biological effect. In order to distinguish the SNPs that have a causal effect on human intelligence, we must eliminate the noise from the high degree of linkage disequilibrium that persists throughout the genome. In this study, we apply a novel PAINTOR fine-mapping method, which uses a Bayesian approach to determine the SNPs with the highest probability of causality. This technique incorporates the GWAS summary statistics, linkage disequilibrium structure, and functional annotations to compute the posterior probability of causality for all SNPs in the GWAS-associated regions. We found five SNPs (rs6002620, rs41352752, rs6568547, rs138592330, and rs28371699) with a high probability of causality, three of which have posterior probabilities >0.60. The SNP rs6002620 (NDUFA6), which is involved in mitochondrial function, has the highest likelihood of causality. These findings provide important insight into the genetic determinants contributing to human intelligence.

241-LB: Identification of Novel and Population-Specific Signals for Type 2 Diabetes in Large-Scale Study of Hispanic/Latino Individuals

Hispanic/Latino populations are both disproportionately burdened by type 2 diabetes (T2D) and drastically less studied compared to non-Hispanic whites. We present results of the final data freeze of the DIAMANTE Hispanic/Latino Consortium, comprising the largest genome-wide meta-analysis and transcriptome-wide association study of T2D in a Hispanic/Latino population and including a total of 13,151 T2D cases and 21,511 controls. We identified a novel locus in an intronic region of BACE1 for T2D adjusted for body mass index, which has previously been linked to gestational diabetes susceptibility, but never before to T2D. We also identified two variants in known loci with evidence (p &amp;lt;1x10-5) of a residual effect after adjusting for the closest known variant, suggesting that these may be population-specific effects due to H/L linkage disequilibrium structure. We used S-PrediXcan to compute the association of tissue-specific imputed expression for 48 tissues with T2D, revealing novel associations with ten genes and replicating six known genes. These novel genes include three genes previously associated with blood pressure traits: MOV10, ST7L, and WNT2B, as well as two genes previously associated with adiposity, measured by waist and hip circumference: MOV10 and WNT2B. Three novel genes have compelling evidence due to known roles of their biological pathways, including PTCH1, in the hedgehog pathway, which is known to play a role in pancreas development, ST7L, which is a miR-24 target and previous studies have indicated that miR-24 is elevated in diabetes, and WNT2B, part of the WNT signaling pathway, which is known to be involved in glucose homeostasis. These results demonstrate that genetic study of T2D in H/L populations can both help prevent a major health disparity through inclusion of this henceforth overlooked population and can also enable further discovery. Disclosure L.E. Petty: None. J.E. Below: None.

Weighting sequence variants based on their annotation increases the power of genome-wide association studies in dairy cattle

BackgroundGenome-wide association studies (GWAS) are widely used to identify regions of the genome that harbor genetic determinants of quantitative traits. However, the multiple-testing burden from scanning tens of millions of whole-genome sequence variants reduces the power to identify associated variants, especially if sample size is limited. In addition, factors such as inaccuracy of imputation, complex linkage disequilibrium structures, and multiple closely-located causal variants may result in an identified causative mutation not being the most significant single nucleotide polymorphism in a particular genomic region. Therefore, the use of information from different sources, particularly variant annotations, was proposed to enhance the fine-mapping of causal variants. Here, we tested whether applying significance thresholds based on variant annotation categories increases the power of GWAS compared with a flat Bonferroni multiple-testing correction.ResultsWhole-genome sequence variants in dairy cattle were categorized according to type and predicted impact. Then, GWAS between markers and 17 quantitative traits were analyzed for enrichment for association of each annotation category. By using annotation categories that were determined with the variants effect predictor software and datasets indicating regions of open chromatin, “low impact” variants were found to be highly enriched. Moreover, when the variants annotated as “modifier” and not located at open chromatin regions were further classified into different types of potential regulatory elements, the high impact variants, moderate impact variants, variants located in the 3′ and 5′ untranslated regions, and variants located in potential non-coding RNA regions exhibited relatively more enrichment. In contrast, a similar study on human GWAS data reported that enrichment of association signals was highest with high impact variants. We observed an increase in power when these variant category-based significance thresholds were applied for GWAS results on stature in Nordic Holstein cattle, as more candidate genes from previous large GWAS meta-analysis for cattle stature were confirmed.ConclusionsUse of variant category-based genome-wide significance thresholds can marginally increase the power to detect the candidate genes in cattle. With the continued improvements in annotation of the bovine genome, we anticipate that the growing usefulness of variant category-based significance thresholds will be demonstrated.

Gpart: human genome partitioning and visualization of high-density SNP data by identifying haplotype blocks.

SummaryFor the analysis of high-throughput genomic data produced by next-generation sequencing (NGS) technologies, researchers need to identify linkage disequilibrium (LD) structure in the genome. In this work, we developed an R package gpart which provides clustering algorithms to define LD blocks or analysis units consisting of SNPs. The visualization tool in gpart can display the LD structure and gene positions for up to 20 000 SNPs in one image. The gpart functions facilitate construction of LD blocks and SNP partitions for vast amounts of genome sequencing data within reasonable time and memory limits in personal computing environments.Availability and implementationThe R package is available at https://bioconductor.org/packages/gpart.Supplementary information Supplementary data are available at Bioinformatics online.

Heterogeneity in the extent of linkage disequilibrium among exonic, intronic, non-coding RNA and intergenic chromosome regions.

Whole-genome sequence data enable construction of high-resolution linkage disequilibrium (LD) maps revealing the LD structure of functional elements within genic and subgenic sequences. The Malecot-Morton model defines LD map distances in linkage disequilibrium units (LDUs), analogous to the centimorgan scale of linkage maps. For whole-genome sequence-derived LD maps, we introduce the ratio of corresponding map lengths kilobases/LDU to describe the extent of LD within genome components. The extent of LD is highly variable across the genome ranging from ~38 kb for intergenic sequences to ~858 kb for centromeric regions. LD is ~16% more extensive in genic, compared with intergenic sequences, reflecting relatively increased selection and/or reduced recombination in genes. The LD profile across 18,268 autosomal genes reveals reduced extent of LD, consistent with elevated recombination, in exonic regions near the 5' end of genes but more extensive LD, compared with intronic sequences, across more centrally located exons. Genes classified as essential and genes linked to Mendelian phenotypes show more extensive LD compared with genes associated with complex traits, perhaps reflecting differences in selective pressure. Significant differences between exonic, intronic and intergenic components demonstrate that fine-scale LD structure provides important insights into genome function, which cannot be revealed by LD analysis of much lower resolution array-based genotyping and conventional linkage maps.

Genetic diversity and population structure of Plasmodium falciparum in Kenyan-Ugandan border areas.

Kenya has, in the last decade, made tremendous progress in the fight against malaria. Nevertheless, continued surveillance of the genetic diversity and population structure of Plasmodium falciparum is required to refine malaria control and to adapt and improve elimination strategies. Twelve neutral microsatellite loci were genotyped in 201 P.falciparum isolates obtained from the Kenyan-Ugandan border (Busia) and from two inland malaria-endemic sites situated in western (Nyando) and coastal (Msambweni) Kenya. Analyses were done to assess the genetic diversity (allelic richness and expected heterozygosity, [He ]), multilocus linkage disequilibrium ( ) and population structure. A similarly high degree of genetic diversity was observed among the three parasite populations surveyed (mean He =0.76; P>0.05). Except in Msambweni, random association of microsatellite loci was observed, indicating high parasite out-breeding. Low to moderate genetic structure (FST =0.022-0.076; P<0.0001) was observed with only 5% variance in allele frequencies observed among the populations. This study shows that the genetic diversity of P.falciparum populations at the Kenyan-Ugandan border is comparable to the parasite populations from inland Kenya. In addition, high genetic diversity, panmixia and weak population structure in this study highlight the fitness of Kenyan P.falciparum populations to successfully withstand malaria control interventions.

Genetic and phenotypic landscape of the major histocompatibilty complex region in the Japanese population.

To perform detailed fine-mapping of the major-histocompatibility-complex region, we conducted next-generation sequencing (NGS)-based typing of the 33 human leukocyte antigen (HLA) genes in 1,120 individuals of Japanese ancestry, providing a high-resolution allele catalog and linkage-disequilibrium structure of both classical and nonclassical HLA genes. Together with population-specific deep-whole-genome-sequencing data (n = 1,276), we conducted NGS-based HLA, single-nucleotide-variant and indel imputation of large-scale genome-wide-association-study data from 166,190 Japanese individuals. A phenome-wide association study assessing 106 clinical phenotypes identified abundant, significant genotype-phenotype associations across 52 phenotypes. Fine-mapping highlighted multiple association patterns conferring independent risks from classical HLA genes. Region-wide heritability estimates and genetic-correlation network analysis elucidated the polygenic architecture shared across the phenotypes.

Sim1000G: a user-friendly genetic variant simulator in R for unrelated individuals and family-based designs

BackgroundSimulation of genetic variants data is frequently required for the evaluation of statistical methods in the fields of human and animal genetics. Although a number of high-quality genetic simulators have been developed, many of them require advanced knowledge in population genetics or in computation to be used effectively. In addition, generating simulated data in the context of family-based studies demands sophisticated methods and advanced computer programming.ResultsTo address these issues, we propose a new user-friendly and integrated R package, sim1000G, which simulates variants in genomic regions among unrelated individuals or among families. The only input needed is a raw phased Variant Call Format (VCF) file. Haplotypes are extracted to compute linkage disequilibrium (LD) in the simulated genomic regions and for the generation of new genotype data among unrelated individuals. The covariance across variants is used to preserve the LD structure of the original population. Pedigrees of arbitrary sizes are generated by modeling recombination events with sim1000G. To illustrate the application of sim1000G, various scenarios are presented assuming unrelated individuals from a single population or two distinct populations, or alternatively for three-generation pedigree data. Sim1000G can capture allele frequency diversity, short and long-range linkage disequilibrium (LD) patterns and subtle population differences in LD structure without the need of any tuning parameters.ConclusionSim1000G fills a gap in the vast area of genetic variants simulators by its simplicity and independence from external tools. Currently, it is one of the few simulation packages completely integrated into R and able to simulate multiple genetic variants among unrelated individuals and within families. Its implementation will facilitate the application and development of computational methods for association studies with both rare and common variants.

SU22 - BIVARIATE GAUSSIAN MIXTURE MODEL FOR GWAS SUMMARY STATISTICS

Background Identifying shared genetics is important as it uncovers hidden relationship between complex traits and improves our understanding of disease etiology. Today genetic correlation is commonly used as the principal measure that quantifies genetic overlap. Available methods can calculate genetic correlation from raw genotypes (restricted maximum likelihood, polygenic risk scores), from a set of Single-Nucleotide Polymorphisms (SNPs) that pass genome-wide significance threshold (Mendelian Randomization), or utilizing all data from Genome Wide Association Studies (GWASes) including SNPs that do not reach genome-wide significance (Cross-Trait Linkage Disequilibrium (LD) Score Regression). These methods for evaluating genetic overlap are unable to capture a mixture of effect directions across shared genetic variants (i.e., only reporting overall positive, negative or no genetic correlations), while recent analyses suggest that correlation across polygenic traits in the effect size and directionality of a SNP is not the same across all SNPs. Methods Bivariate Gaussian Mixture Model provides a novel way to detect and quantify shared genetics independently from genetic correlation. In the absence of genetic correlation we use another measure of polygenic overlap, expressed as the proportion of SNPs associated with both traits. To estimate this quantity, we model true per-SNP effect sizes as a mixture of four bivariate normal distributions: two causal components specific to each trait, one causal component of SNPs affecting both traits, and a null component of SNPs with no effect on either trait. We interpret the weight of each component in the mixture as the proportion of SNPs in that component. The parameters of the mixture model are estimated from the summary statistics data by direct optimization of the maximum likelihood. Our statistical model relates observed signed test statistics (GWAS z-scores) to the underlying per-SNP effect sizes, incorporating effects of LD structure, minor allele frequency, sample size, cryptic relationships / sample stratification, and sample overlap, to capture all these effects on GWAS z-scores. Results We show in simulations that our model differentiates cases with no polygenic overlap versus cases with significant overlap in the absence of genetic correlation. We show genetic overlap between schizophrenia, waist-hip-ratio and triglycerides, despite the fact that genetic correlation is close to zero. We apply our model to schizophrenia, bipolar disorder and IQ data, and show that in the presence of genetic correlation our model reports consistent results with those from cross-trait LD score regression. Discussion It appears to be a common practice to interpret the lack of genetic correlation as no relation between traits. However, lack of correlation does not necessarily imply independence - a fact well known from statistics. Thus, we advocate that certain important cases of genetic overlap are not captured by genetic correlation. Our model quantifies genetic overlap between traits both with and without genetic correlation. It controls for the fact that if both traits are highly polygenic then some proportion of SNPs is associated with both traits by chance or due to LD structure. In addition to the insights into genetic architecture our model can improve SNP discovery, by estimating posterior effect size of one trait given observed GWAS z-score in another trait. Further, more accurately estimated effect sizes can be used for improving polygenic risk scores.

Phenotypic expression and founder effect of PANK2 c.1583C&gt;T (p.T528M) mutation in Serbian pantothenate kinase-associated neurodegeneration patients

Pantothenate kinase-associated neurodegeneration (PKAN) is an autosomal recessive disorder characterized by dystonia, parkinsonism, cognitive and visual impairment, and iron accumulation in the brain. Many cases of PKAN result from mutations in the PANK2 gene that encodes pantothenate kinase 2, a key regulatory enzyme in the biosynthesis of coenzyme A. We previously detected six Serbian patients with clinically suggestive PKAN, all of whom had PANK2 c.1583C&gt;T (p.T528M) mutation either in the homozygous or in the heterozygous state. In this study we explored the phenotypic expression and a possible founder effect of this substitution. We performed the analysis of linkage disequilibrium (LD) and organization in haplotypes of 23 single nucleotide polymorphisms (SNPs) adjacent to the PANK2 gene in all of the six patients and their parents, as well as in control healthy child-parents trios. The age of PANK2 c.1583C&gt;T mutation was determined using the r2 degeneration method. Clinical findings in our patients were markedly similar. Different LD structures between patients and controls is revealed, and PANK2 c.1583T allele was significantly associated with a particular haplotype. The age of PANK2 c.1583C&gt;T mutation was estimated to be about 15 generations. Our results suggest that PANK2 c.1583C&gt;T in Serbian PKAN patients represents a founder mutation descended from one common ancestor.

17 - FUMA: FUNCTIONAL MAPPING AND ANNOTATION OF GENETIC ASSOCIATIONS

Background A main challenge in genome-wide association studies (GWAS) is to prioritize genetic variants and identify potential causal mechanisms of human diseases. Although a variety of bioinformatics tools are now available for downstream analyses of GWAS results, a standard, integrative approach is lacking. We developed FUMA: a web-based platform to facilitate functional annotation of GWAS results, prioritization of potential causal genetic variants and genes, and interactive visualization by incorporating 14 biological data repositories and tools. FUMA is available as an online tool at http://fuma.ctglab.nl. Methods FUMA contains two core functions to annotate input summary statistics to prioritize potential causal genetic variants and genes; SNP2GENE and GENE2FUNC. In SNP2GENE, SNPs are annotated with their biological functionality and mapped to genes based on positional and functional information of SNPs. In this process, significantly associated SNPs from GWAS are characterized as genomic risk loci by incorporating the linkage disequilibrium structure. Functionally annotated SNPs are mapped to genes based on functional consequences on genes (positional mapping), expression quantitative trait loci (eQTLs) and chromatin interactions of phenotype relevant tissue types (eQTL and chromatin interaction mappings). By combining these three mapping strategies, FUMA enables to prioritize genes that are highly likely involved in the trait of interest. To obtain insight into putative causal mechanisms, the GENE2FUNC process annotates the prioritized genes in biological context, such as tissue specific gene expression pattern, enrichment of gene sets and direct links to well defined external biological databases such as disease associated genes and drug targets. Results We have applied FUMA to the most recent Body Mass Index (BMI)[1]. We successfully prioritized previously reported genes from the original GWAS study, and also novel candidates by combining positional mapping of deleterious coding SNPs and eQTL mapping. For example, IRX3 was prioritized by eQTLs from FTO locus which is the most significantly associated locus with BMI. Although, FTO is the only gene reported in the original study since the genomic risk locus is located within the FTO gene and not overlapped with any other genes, IRX3 has been recently validated as whose expression is affected by variants in the FTO locus[2]. Thus, by incorporating multiple biological information, we could prioritise genes that are located outside of genomic risk loci. Chromatin interaction mapping identified additional novel candidates, including genes located outside of the risk loci, which showed share biological functions with the previously reported genes. Discussion In summary, FUMA provides an easy-to-use tool to interpret and functionally annotate results from genetic association studies. It allows to quickly gain insight into the directional biological implications of significant genetic associations. FUMA combines information of state-of-the-art biological data sources in a single platform to facilitate the generation of hypotheses for functional follow-up analysis aimed at proving causal relations between genetic variants and diseases.

Interpreting Non-coding Genetic Variation in Multiple Sclerosis Genome-Wide Associated Regions.

Multiple sclerosis (MS) is the most common neurological disorder in young adults. Despite extensive studies, only a fraction of MS heritability has been explained, with association studies focusing primarily on protein-coding genes, essentially for the difficulty of interpreting non-coding features. However, non-coding RNAs (ncRNAs) and functional elements, such as super-enhancers (SE), are crucial regulators of many pathways and cellular mechanisms, and they have been implicated in a growing number of diseases. In this work, we searched for possible enrichments in non-coding elements at MS genome-wide associated loci, with the aim to highlight their possible involvement in the susceptibility to the disease. We first reconstructed the linkage disequilibrium (LD) structure of the Italian population using data of 727,478 single-nucleotide polymorphisms (SNPs) from 1,668 healthy individuals. The genomic coordinates of the obtained LD blocks were intersected with those of the top hits identified in previously published MS genome-wide association studies (GWAS). By a bootstrapping approach, we hence demonstrated a striking enrichment of non-coding elements, especially of circular RNAs (circRNAs) mapping in the 73 LD blocks harboring MS-associated SNPs. In particular, we found a total of 482 circRNAs (annotated in publicly available databases) vs. a mean of 194 ± 65 in the random sets of LD blocks, using 1,000 iterations. As a proof of concept of a possible functional relevance of this observation, we experimentally verified that the expression levels of a circRNA derived from an MS-associated locus, i.e., hsa_circ_0043813 from the STAT3 gene, can be modulated by the three genotypes at the disease-associated SNP. Finally, by evaluating RNA-seq data of two cell lines, SH-SY5Y and Jurkat cells, representing tissues relevant for MS, we identified 18 (two novel) circRNAs derived from MS-associated genes. In conclusion, this work showed for the first time that MS-GWAS top hits map in LD blocks enriched in circRNAs, suggesting circRNAs as possible novel contributors to the disease pathogenesis.

Linkage disequilibrium and population structure characterization in the cutting and racing lines of Quarter Horses bred in Brazil

Different selection goals in the Quarter Horse breed have led to the formation of lines. These lines have different skills such as working with cattle or running and differ in morphology, physiology and inbreeding. The aim of this study was to characterize the linkage disequilibrium (LD, r2) in the cutting and racing lines of Quarter Horses bred in Brazil using large-scale SNP genotyping. Additionally, the effective population size (Ne) of the two populations (lines) was determined, as well as their structure and relationships. A total of 428 Quarter Horses (both sexes) of the two lines (cutting: 68; racing: 360), registered in the Stud book of the Brazilian Association of Quarter Horse Breeders (ABQM), were used. The animals had blood collected in the Sorocaba Jockey Club (Sorocaba/SP) and in dozens rural properties across the state of São Paulo, Brazil. In 2011, 188 horses (cutting: 68; racing: 120) were genotyped using a 54k SNP chip. The remaining animals (n = 240) were genotyped in 2015 using a 65k SNP chip. The average genomic r2 between marker pairs was 0.22 for the cutting line and 0.27 for the racing line. In the cutting line, the r2 was less than 0.20 between 100 and 150 kb, while the same threshold (0.20) was found between 300 and 350 kb in the racing line. The Ne was 60 and 50 effective animals in the last generation of the cutting and racing line, respectively. The greater extent of LD and smaller Ne in the racing line may be explained by the structure of the population, which is more closed because of the more rigorous registration process and greater influence of the Thoroughbred breed on the formation of this line. The results point to marked differences between the lines of this breed. In addition, subpopulations were found in the racing line, which may be related to the formation of families descending from important sires.

Combined genetic influence of the nicotinic receptor gene cluster CHRNA5/A3/B4 on nicotine dependence

BackgroundThe CHRNA5/A3/B4 gene locus is associated with nicotine dependence and other smoking related disorders. While the non-synonymous CHRNA5 variant rs16969968 appears to be the main risk factor, linkage disequilibrium (LD) bins in the gene cluster carry frequent variants that regulate expression. Pairwise LD and haplotype analyses had identified at least three haplotype tagging SNPs including rs16969968 as main genetic risk factors. Searching for variants with evidence of regulatory functions, we have reported interactions between CHRNA5 and CHRNA3 enhancer variants (tagged by rs880395 and rs1948, respectively) and rs16969968, forming 3-SNP haplotypes and diplotypes that may more accurately reflect the cluster’s combined effects on nicotine dependence (Barrie et al., Hum Mutat 38:112–9, 2017). Here we address further contributions by variants affecting CHRNB4, a possibly limiting component of nicotinic receptors.ResultsWe identify an LD bin (tagged by rs4887074) associated with expression of CHRNB4. Additive logistic regression models indicate that rs4887074 is associated with nicotine dependence and modulates the effect of rs16969968 in GWAS datasets (COGEND, UW-TTURC, SAGE). 4-SNP haplotype and diplotype analyses (rs880395-rs16969968-rs1948 -rs4887074) yield nicotine dependence risk values that further differentiate those obtained with the 3-SNP model. Moreover, both the main G allele of rs16969968 and the minor G allele of rs4887074 (associated with reduced expression of CHRNB4), residing predominantly on common haplotypes that are protective, represent significant allele-specific variance QTLs, indicating that they interact with each other.ConclusionsThese results indicate rs4887074 is associated with CHRNB4 expression, and along with two regulatory variants of CHRNA3 and CHRNA5, modulates the effect of rs16969968 on nicotine dependence risk. Assignable to individuals because of strong LD structures, 4-SNP haplotypes and diplotypes serve to assess the combined genetic influence of this multi-gene cluster on complex traits, accounting for complex LD relationships and tissue-specific genetic effects (CHRNA5/3) relevant to the traits analyzed. The 4-SNP haplotypes account at least in part for previous tagging SNPs, including the highly GWAS-significant rs6495308, located in a distinct pair-wise LD bin but included in protective 4-SNP haplotypes. Our approach refines and integrates the cluster’s overall genetic influence, an important variable when integrating the genetics of multiple genomic loci.

Genetic diversity, linkage disequilibrium, and population structure in a panel of Brazilian rice accessions.

Narrowing of genetic diversity and the quantitative nature of most agronomic traits is a challenge for rice breeding. Genome-wide association studies have a great potential to identify important variation in loci underlying quantitative and complex traits; however, before performing the analysis, it is important to assess parameters of the genotypic data and population under study, to improve the accuracy of the genotype-phenotype associations. The aim of this study was to access the genetic diversity, linkage disequilibrium, and population structure of a working panel of Brazilian and several introduced rice accessions, which are currently being phenotyped for a vast number of traits to undergo association mapping. Ninety-four accessions were genotyped with 7098 SNPs, and after filtering for higher call rates and removing rare variants, 93 accessions and 4973 high-quality SNPs remained for subsequent analyses and association studies. The overall mean of the polymorphic information content, heterozygosity, and gene diversity of the SNPs was comparable to other rice panels. The r2 measure of linkage disequilibrium decayed to 0.25 in approximately 150kb, a slow decay, explained by the autogamous nature of rice and the small size of the panel. Regarding population structure, eight groups were formed according to Bayesian clustering. Principle components and neighbor-joining analyses were able to distinguish part of the groups formed, mainly regarding the sub-species indica and japonica. Our results demonstrate that the population and SNPs are of high quality for association mapping.

Potential of Genome-Wide Studies in Unrelated Plus Trees of a Coniferous Species, Cryptomeria japonica (Japanese Cedar).

A genome-wide association study (GWAS) was conducted on more than 30,000 single nucleotide polymorphisms (SNPs) in unrelated first-generation plus tree genotypes from three populations of Japanese cedar Cryptomeria japonica D. Don with genomic prediction for traits of growth, wood properties and male fecundity. Among the assessed populations, genetic characteristics including the extent of linkage disequilibrium (LD) and genetic structure differed and these differences are considered to be due to differences in genetic background. Through population-independent GWAS, several significant SNPs found close to the regions associated with each of these traits and shared in common across the populations were identified. The accuracies of genomic predictions were dependent on the traits and populations and reflected the genetic architecture of traits and genetic characteristics. Prediction accuracies using SNPs selected based on GWAS results were similar to those using all SNPs for several combinations of traits and populations. We discussed the application of genome-wide studies for C. japonica improvement.

Association between genetic risk variants and glucose intolerance during pregnancy in north Indian women

BackgroundGestational diabetes (GDM) is a more common problem in India than in many other parts of the world but it is not known whether this is due to unique environmental factors or a unique genetic background. To address this question we examined whether the same genetic variants associated with GDM and Type 2 Diabetes (T2D) in Caucasians also were associated with GDM in North Indian women.MethodsFive thousand one hundred pregnant women of gestational age 24–28 weeks from Punjab were studied by a 75 g oral glucose tolerance test (OGTT). GDM was diagnosed by both WHO1999 and 2013 criteria. 79 single nucleotide polymorphisms (SNPs) previously associated with T2D and glycemic traits (12 of them also with GDM) and 6 SNPs from previous T2D associations based on Indian population (some also with European) were genotyped on a Sequenom platform or using Taqman assays in DNA from 4018 women.ResultsIn support of previous findings in Caucasian GDM, SNPs at KCJN11 and GRB14 loci were nominally associated with GDM1999 risk in Indian women (both p = 0.02). Notably, T2D risk alleles of the variant rs1552224 near CENTD2, rs11708067 in ADCY5 and rs11605924 in CRY2 genes associated with protection from GDM regardless of criteria applied (p < 0.025). SNPs rs7607980 near COBLL1 (p = 0.0001), rs13389219 near GRB14 (p = 0.026) and rs10423928 in the GIPR gene (p = 0.012) as well as the genetic risk score (GRS) for these previously shown insulin resistance loci here associated with insulin resistance defined by HOMA2-IR and showed a trend towards GDM. GRS comprised of 3 insulin secretion loci here associated with insulin secretion but not GDM.ConclusionsGDM in women from Punjab in Northern India shows a genetic component, seemingly driven by insulin resistance and secretion and partly shared with GDM in other parts of the world. Most previous T2D loci discovered in European studies did not associate with GDM in North India, indicative of different genetic etiology or alternately, differences in the linkage disequilibrium (LD) structure between populations in which the associated SNPs were identified and Northern Indian women. Interestingly some T2D risk variants were in fact indicative of being protective for GDM in these Indian women.

Linkage disequilibrium pattern and genome-wide association mapping for meat traits in multiple porcine F2 crosses.

In the present study, data from four F2 crosses were analysed and used to study the linkage disequilibrium (LD) structure within and across the crosses. Genome-wide association analyses (GWASes) for conductivity and dressing out meat traits were conducted using single-marker and Bayesian multi-marker models using the pooled data from all F2 crosses. Porcine F2 crosses generated from the distantly related founder breeds Wild Boar, Piétrain and Meishan, as well as from a porcine F2 cross from the closely related founder breed Piétrain and an F1 Large White×Landrace cross were pooled. A total of 2572 F2 animals were genotyped using a 62K SNP chip. The positions of the SNPs were based on genome assembly Sscrofa11.1. After post-alignment and genotype filtering, approximately 50K SNPs were usable for LD studies and GWASes. The main findings of the present study are that the breakdown of LD was faster in crosses from closely related founder breeds compared to crosses from distantly related founders. The fastest breakdown of LD was observed by pooling the data. Based on the single-marker results and LD structure, clusters and windows were built for 1-Mb intervals. For conductivity and dressing out, 183 and 191 nominal significant associations respectively and six and five clusters respectively were found. Dominance was important for conductivity, and considering dominance in GWASes improved the mapping signals. Most clear signals were found for conductivity on SSC6, 8 and 15 and for dressing out on SSC2 and 7. Considering dominance might contribute to the accuracy of genomic selection and serve as a guide for choosing mating pairs with good combining abilities. However, further research is needed to investigate if dominance is also important in crossbreed pig breeding schemes.

Linkage Disequilibrium and Population Structure in Wild and Cultivated Populations of Rubber Tree (Hevea brasiliensis).

Among rubber tree species, which belong to the Hevea genus of the Euphorbiaceae family, Hevea brasiliensis (Willd. ex Adr.de Juss.) Muell. Arg. is the main commercial source of natural rubber production worldwide. Knowledge of the population structure and linkage disequilibrium (LD) of this species is essential for the efficient organization and exploitation of genetic resources. Here, we obtained single-nucleotide polymorphisms (SNPs) using a genotyping-by-sequencing (GBS) approach and then employed the SNPs for the following objectives: (i) to identify the positions of SNPs on a genetic map of a segregating mapping population, (ii) to evaluate the population structure of a germplasm collection, and (iii) to detect patterns of LD decay among chromosomes for future genetic association studies in rubber tree. A total of 626 genotypes, including both germplasm accessions (368) and individuals from a genetic mapping population (254), were genotyped. A total of 77,660 and 21,283 SNPs were detected by GBS in the germplasm and mapping populations, respectively. The mapping population, which was previously mapped, was constructed with 1,062 markers, among which only 576 SNPs came from GBS, reducing the average interval between two adjacent markers to 4.4 cM. SNPs from GBS genotyping were used for the analysis of genetic structure and LD estimation in the germplasm accessions. Two groups, which largely corresponded to the cultivated and wild populations, were detected using STRUCTURE and via principal coordinate analysis. LD analysis, also using the mapped SNPs, revealed that non-random associations varied along chromosomes, with regions of high LD interspersed with regions of low LD. Considering the length of the genetic map (4,693 cM) and the mean LD (0.49 for cultivated and 0.02 for wild populations), a large number of evenly spaced SNPs would be needed to perform genome-wide association studies in rubber tree, and the wilder the genotypes used, the more difficult the mapping saturation.