Linkage Disequilibrium Structure Research Articles

Computing linkage disequilibrium aware genome embeddings using autoencoders.

The completion of the genome has paved the way for genome-wide association studies (GWAS), which explained certain proportions of heritability. GWAS are not optimally suited to detect non-linear effects in disease risk, possibly hidden in non-additive interactions (epistasis). Alternative methods for epistasis detection using e.g. deep neural networks (DNNs) are currently under active development. However, DNNs are constrained by finite computational resources, which can be rapidly depleted due to increasing complexity with the sheer size of the genome. Besides, the curse of dimensionality complicates the task of capturing meaningful genetic patterns for DNNs; therefore necessitates dimensionality reduction. We propose a method to compress single nucleotide polymorphism (SNP) data, while leveraging the linkage disequilibrium (LD) structure and preserving potential epistasis. This method involves clustering correlated SNPs into haplotype blocks and training per-block autoencoders to learn a compressed representation of the block's genetic content. We provide an adjustable autoencoder design to accommodate diverse blocks and bypass extensive hyperparameter tuning. We applied this method to genotyping data from Project MinE, and achieved 99% average test reconstruction accuracy-i.e. minimal information loss-while compressing the input to nearly 10% of the original size. We demonstrate that haplotype-block based autoencoders outperform linear Principal Component Analysis (PCA) by approximately 3% chromosome-wide accuracy of reconstructed variants. To the extent of our knowledge, our approach is the first to simultaneously leverage haplotype structure and DNNs for dimensionality reduction of genetic data. Data are available for academic use through Project MinE at https://www.projectmine.com/research/data-sharing/, contingent upon terms and requirements specified by the source studies. Code is available at https://github.com/gizem-tas/haploblock-autoencoders. Supplementary data are available at Bioinformatics online.

Genetic Diversity, Linkage Disequilibrium, and Population Structure in a Common Bean Reference Collection

An in-depth understanding of the extent and pattern of genetic diversity and population structure in crop populations is of paramount importance for any crop improvement program to efficiently promote the translation of genetic diversity into genetic gain. A reference collection of 150 common bean genotypes selected from the International Center for Tropical Agriculture’s global core collection was evaluated using single-nucleotide polymorphism (SNP) markers to quantify the amount of genetic diversity, linkage disequilibrium, and population structure. The cultivars and landraces of the collection were diverse and originated from 14 countries, and wild accessions were used as controls for each gene pool. The collection was genotyped using an SNP array, generating a total of 5398 locus calls distributed across the entire bean genome. The SNP data quality was checked, and two datasets were generated. The first dataset (Dataset_1) comprised a set of 5108 SNPs and 150 genotypes after filtering for 10% missing alleles and an MAF < 0.05. The second dataset (Dataset_2) comprised a set of 2300 SNPs that remained after removing any null-allele SNPs and LD pruning for a criterion of r2 < 0.2. Dataset_1 was used for a principal coordinate analysis (PCoA), phylogenetic relationship determination, an analysis of molecular variance (AMOVA), and a discriminant analysis of principal components. Dataset_2 was used for a population structure analysis using STRUCTURE software and is proposed for a genome-wide association study (GWAS). The population structure analysis split the reference collection into two subpopulations according to an Andean or Mesoamerican gene pool. The Mesoamerican populations displayed higher genetic differentiation and tended to split into more groups that were somewhat aligned with common bean races. Andean beans were characterized by a larger average LD but lower LD percentage, a small average genetic distance between members of the population, and a higher major allele frequency, which suggested narrower genetic diversity compared to the Mesoamerican gene pool. In conclusion, the results indicated the presence of high genetic diversity, which is useful for a GWAS. However, the presence of significant linkage disequilibrium requires that genetic distance be considered as a co-factor for any further genetic studies. Overall, the molecular variation observed in the genotypes shows that this reference collection is valuable as a genebank-derived diversity panel which is useful for marker trait association studies.

A powerful approach to identify replicable variants in genome-wide association studies

Replicability is the cornerstone of modern scientific research. Reliable identifications of genotype-phenotype associations that are significant in multiple genome-wide association studies (GWASs) provide stronger evidence for the findings. Current replicability analysis relies on the independence assumption among single-nucleotide polymorphisms (SNPs) and ignores the linkage disequilibrium (LD) structure. We show that such a strategy may produce either overly liberal or overly conservative results in practice. We develop an efficient method, ReAD, to detect replicable SNPs associated with the phenotype from two GWASs accounting for the LD structure. The local dependence structure of SNPs across two heterogeneous studies is captured by a four-state hidden Markov model (HMM) built on two sequences of p values. By incorporating information from adjacent locations via the HMM, our approach provides more accurate SNP significance rankings. ReAD is scalable, platform independent, and more powerful than existing replicability analysis methods with effective false discovery rate control. Through analysis of datasets from two asthma GWASs and two ulcerative colitis GWASs, we show that ReAD can identify replicable genetic loci that existing methods might otherwise miss.

Hierarchical joint analysis of marginal summary statistics-Part I: Multipopulation fine mapping and credible set construction.

Recent advancement in genome-wide association studies (GWAS) comes from not only increasingly larger sample sizes but also the shift in focus towards underrepresented populations. Multipopulation GWAS increase power to detect novel risk variants and improve fine-mapping resolution by leveraging evidence and differences in linkage disequilibrium (LD) from diverse populations. Here, we expand upon our previous approach for single-population fine-mapping through Joint Analysis of Marginal SNP Effects (JAM) to a multipopulation analysis (mJAM). Under the assumption that true causal variants are common across studies, we implement a hierarchical model framework that conditions on multiple SNPs while explicitly incorporating the different LD structures across populations. The mJAM framework can be used to first select index variants using the mJAM likelihood with different feature selection approaches. In addition, we present a novel approach leveraging the ideas of mediation to construct credible sets for these index variants. Construction of such credible sets can be performed given any existing index variants. We illustrate the implementation of the mJAM likelihood through two implementations: mJAM-SuSiE (a Bayesian approach) and mJAM-Forward selection. Through simulation studies based on realistic effect sizes and levels of LD, we demonstrated that mJAM performs well for constructing concise credible sets that include the underlying causal variants. In real data examples taken from the most recent multipopulation prostate cancer GWAS, we showed several practical advantages of mJAM over other existing multipopulation methods.

Assessment of linkage disequilibrium and haplotype block structure in indigenous cattle populations of Tamil Nadu, India usingwhole genome sequence data

Understanding the nature of linkage disequilibrium (LD) between molecular markers reflects the degree of non-random association between their alleles, which is essential for genome-wide association studies. The main objective of this study was to assess the chromosome-wise linkage disequilibrium and haplotype block structure in indigenous cattle breeds of Tamil Nadu, India. A total of 79 animals belonging to five indigenous cattle breeds were considered as single indigenous cattle population and three samples per breed were sequenced using Illumina NovaSeqTM 6000 and Illumina HiSeq 2500. The LD parameter (r2) was estimated for a total of 62,095,778 pair- wise SNPs for all autosomes. The chromosome-wise number of SNP pairs, mean r2±standard deviation and median r2 values were 2,141,234, 0.484±0.246 and 0.493, respectively. Chromosome-wise mean r2 of different distance bins were calculated and it showed the maximum r2 value of 0.839 at 31-70 kb distance for Chromosome 11 and minimum r2 value of zero for Chromosome 7, 11, 15, 20 and 29, respectively at a distance 71-100 kb. This study also revealed a total of 413, 277 haplotype blocks which covered 2.34% of the autosomal genome. There was a total of 1,589,118 SNPs distributed within the haplotype blocks, covering a total length of 53.62 mb. The results of the study suggest the need for breed-specific reference populations for indigenous cattle breeds, which have a greater density of molecular markers of economic significance and thereby to identify breed-specific haplotypes in future.

A new wild emmer wheat panel allows to map new loci associated with resistance to stem rust at seedling stage.

Wheat stem rust, caused by Puccinia graminis f. sp. tritici (Pgt), is a major wheat disease worldwide. A collection of 283 wild emmer wheat [Triticum turgidum L. subsp. dicoccoides (Körn. ex Asch. & Graebn.) Thell] accessions, representative of the entire Fertile Crescent region where wild emmer naturally occurs, was assembled, genotyped, and characterized for population structure, genetic diversity, and rate of linkage disequilibrium (LD) decay. Then, the collection was employed for mapping Pgt resistance genes, as a proof of concept of the effectiveness of genome-wide association studies in wild emmer. The collection was evaluated in controlled conditions for reaction to six common Pgt pathotypes (TPMKC, TTTTF, JRCQC, TRTTF, TTKSK/Ug99, and TKTTF). Most resistant accessions originated from the Southern Levant wild emmer lineage, with some showing a resistance reaction toward three to six tested races. Association analysis was conducted considering a 12K polymorphic single-nucleotide polymorphisms dataset, kinship relatedness between accessions, and population structure. Eleven significant marker-trait associations (MTA) were identified across the genome, which explained from 17% to up to 49% of phenotypic variance with an average 1.5 additive effect (based on the 1-9 scoring scale). The identified loci were either effective against single or multiple races. Some MTAs colocalized with known Pgt resistance genes, while others represent novel resistance loci useful for durum and bread wheat prebreeding. Candidate genes with an annotated function related to plant response to pathogens were identified at the regions linked to the resistance and defined according to the estimated small LD (about 126kb), as typical of wild species.

Genetic diversity, linkage disequilibrium, and population structure of tetraploid wheat landraces originating from Europe and Asia

BackgroundDurum wheat is one of the most important crops, especially in the Mediterranean region. Insight into the genetic diversity of germplasm can improve the breeding program management in various traits. This study was done using single nucleotide polymorphisms (SNP) markers to characterize the genetic distinctiveness and differentiation of tetraploid wheat landraces collected from nine European and Asian countries. A sum of 23,334 polymorphic SNPs was detected in 126 tetraploid wheat landraces in relation to the reference genome.ResultsThe number of identified SNPs was 11,613 and 11,721 in A and B genomes, respectively. The highest and lowest diversity was on 6B and 6 A chromosomes, respectively. Structure analysis classified the landraces into two distinct subpopulations (K = 2). Evaluating the principal coordinate analysis (PCoA) and weighted pair-group method using arithmetic averages (WPGMA) clustering results demonstrated that landraces (99.2%) are categorized into one of the two chief subpopulations. Therefore, the grouping pattern did not clearly show the presence of a clear pattern of relationships between genetic diversity and their geographical derivation. Part of this result could be due to the historical exchange between different germplasms. Although the result did not separate landraces based on their region of origin, the landraces collected from Iran were classified into the same group and cluster. Analysis of molecular variance (AMOVA) also confirmed the results of population structure. Finally, Durum wheat landraces in some countries, including Turkey, Russia, Ukraine, and Afghanistan, were highly diverse, while others, including Iran and China, were low-diversity.ConclusionThe recent study concluded that the 126 tetraploid wheat genotypes and their GBS-SNP markers are very appropriate for quantitative trait loci (QTLs) mapping and genome-wide association studies (GWAS). The core collection comprises two distinct subpopulations. Subpopulation II genotypes are the most diverse genotypes, and if they possess desired traits, they may be used in future breeding programs. The degree of diversity in the landraces of countries can provide the ground for the improvement of new cultivars with international cooperation. linkage disequilibrium (LD) hotspot distribution across the genome was investigated, which provides useful information about the genomic regions that contain intriguing genes.

Leveraging information between multiple population groups and traits improves fine-mapping resolution

Statistical fine-mapping helps to pinpoint likely causal variants underlying genetic association signals. Its resolution can be improved by (i) leveraging information between traits; and (ii) exploiting differences in linkage disequilibrium structure between diverse population groups. Using association summary statistics, MGflashfm jointly fine-maps signals from multiple traits and population groups; MGfm uses an analogous framework to analyse each trait separately. We also provide a practical approach to fine-mapping with out-of-sample reference panels. In simulation studies we show that MGflashfm and MGfm are well-calibrated and that the mean proportion of causal variants with PP > 0.80 is above 0.75 (MGflashfm) and 0.70 (MGfm). In our analysis of four lipids traits across five population groups, MGflashfm gives a median 99% credible set reduction of 10.5% over MGfm. MGflashfm and MGfm only require summary level data, making them very useful fine-mapping tools in consortia efforts where individual-level data cannot be shared.

Identification of potential genetic causal variants for obesity-related traits using statistical fine mapping.

Obesity is highly influenced by heritability and variant effects. While previous genome-wide association studies (GWASs) have successfully identified numerous genetic loci associated with obesity-related traits [body mass index (BMI) and waist-to-hip ratio (WHR)], most causal variants remain unidentified. The high degree of linkage disequilibrium (LD) throughout the genome makes it extremely difficult to distinguish the GWAS-associated SNPs that exert a true biological effect. This study was to identify the potential causal variants having a biological effect on obesity-related traits. We used Probabilistic Annotation INTegratOR, a Bayesian fine-mapping method, which incorporated genetic association data (GWAS summary statistics), LD structure, and functional annotations to calculate a posterior probability of causality for SNPs across all loci of interest. Moreover, we performed gene expression analysis using the available public transcriptomic data to validate the corresponding genes of the potential causal SNPs partially. We identified 96 SNPs for BMI and 43 SNPs for WHR with a high posterior probability of causality (> 99%), including 49 BMI SNPs and 24 WHR SNPs which did not reach genome-wide significance in the original GWAS. Finally, we partially validated some genes corresponding to the potential causal SNPs. Using a statistical fine-mapping approach, we identified a set of potential causal variants to be prioritized for future functional validation and also detected some novel trait-associated variants. These results provided novel insight into our understanding of the genetics of obesity and also demonstrated that fine mapping may improve upon the results identified by the original GWASs.

Genetic architecture of DCC and influence on psychological, psychiatric and cardiometabolic traits in multiple ancestry groups in UK Biobank

BackgroundPeople with severe mental illness have a higher risk of cardiometabolic disease than the general population. Traditionally attributed to sociodemographic, behavioural factors and medication effects, recent genetic studies have provided evidence of shared biological mechanisms underlying mental illness and cardiometabolic disease. We aimed to determine whether signals in the DCC locus, implicated in psychiatric and cardiometabolic traits, were shared or distinct. MethodsIn UK Biobank, we systematically assessed genetic variation in the DCC locus for association with metabolic, cardiovascular and psychiatric-related traits in unrelated “white British” participants (N = 402,837). Logistic or linear regression were applied assuming an additive genetic model and adjusting for age, sex, genotyping chip and population structure. Bonferroni correction for the number of independent variants was applied. Conditional analyses (including lead variants as covariates) and trans-ancestry analyses were used to investigate linkage disequilibrium between signals. ResultsSignificant associations were observed between DCC variants and smoking, anhedonia, body mass index (BMI), neuroticism and mood instability. Conditional analyses and linkage disequilibrium structure suggested signals for smoking and BMI were distinct from each other and the mood traits, whilst individual mood traits were inter-related in a complex manner. LimitationsRestricting analyses in non-“white British” individuals to the phenotypes significant in the “white British” sample is not ideal, but the smaller samples sizes restricted the phenotypes possible to analyse. ConclusionsGenetic variation in the DCC locus had distinct effects on BMI, smoking and mood traits, and therefore is unlikely to contribute to shared mechanisms underpinning mental and cardiometabolic traits.

Genomic Prediction Accuracies for Growth and Carcass Traits in a Brangus Heifer Population.

The predictive abilities and accuracies of genomic best linear unbiased prediction (GBLUP) and the Bayesian (BayesA, BayesB, BayesC and Lasso) genomic selection (GS) methods for economically important growth (birth, weaning, and yearling weights) and carcass (depth of rib fat, apercent intramuscular fat and longissimus muscle area) traits were characterized by estimating the linkage disequilibrium (LD) structure in Brangus heifers using single nucleotide polymorphisms (SNP) markers. Sharp declines in LD were observed as distance among SNP markers increased. The application of the GBLUP and the Bayesian methods to obtain the GEBV for growth and carcass traits within k-means and random clusters showed that k-means and random clustering had quite similar heritability estimates, but the Bayesian methods resulted in the lower estimates of heritability between 0.06 and 0.21 for growth and carcass traits compared with those between 0.21 and 0.35 from the GBLUP methodologies. Although the prediction ability of the GBLUP and the Bayesian methods were quite similar for growth and carcass traits, the Bayesian methods overestimated the accuracies of GEBV because of the lower estimates of heritability of growth and carcass traits. However, GBLUP resulted in accuracy of GEBV for growth and carcass traits that parallels previous reports.

Population Structure and Association Mapping for Agronomical and Biochemical Traits of a Large Spanish Apple Germplasm.

A basic knowledge of linkage disequilibrium and population structure is necessary in order to determine the genetic control and identify significant associations with agronomical and phytochemical compounds in apple (Malus × domestica Borkh). In this study, 186 apple accessions (Pop1), representing both Spanish native accessions (94) and non-Spanish cultivars (92) from the EEAD-CSIC apple core collection, were assessed using 23 SSRs markers. Four populations were considered: Pop1, Pop2, Pop3, and Pop4. The initial Pop1 was divided into 150 diploid (Pop2) and 36 triploid accessions (Pop3), while for the inter-chromosomal linkage disequilibrium and the association mapping analysis, 118 phenotype diploid accessions were considered Pop4. Thus, the average number of alleles per locus and observed heterozygosity for the overall sample set (Pop1) were 15.65 and 0.75, respectively. The population structure analysis identified two subpopulations in the diploid accessions (Pop2 and Pop4) and four in the triploids (Pop3). Regarding the Pop4, the population structure with K = 2 subpopulations segregation was in agreement with the UPGMA cluster analysis according to the genetic pairwise distances. Moreover, the accessions seemed to be segregated by their origin (Spanish/non-Spanish) in the clustering analysis. One of the two subpopulations encountered was quite-exclusively formed by non-Spanish accessions (30 out of 33). Furthermore, agronomical and basic fruit quality parameters, antioxidant traits, individual sugars, and organic acids were assessed for the association mapping analysis. A high level of biodiversity was exhibited in the phenotypic characterization of Pop4, and a total of 126 significant associations were found between the 23 SSR markers and the 21 phenotypic traits evaluated. This study also identified many new marker-locus trait associations for the first time, such as in the antioxidant traits or in sugars and organic acids, which may be useful for predictions and for a better understanding of the apple genome.

GSEL: a fast, flexible python package for detecting signatures of diverse evolutionary forces on genomic regions.

GSEL is a computational framework for calculating the enrichment of signatures of diverse evolutionary forces in a set of genomic regions. GSEL can flexibly integrate any sequence-based evolutionary metric and analyze sets of human genomic regions identified by genome-wide assays (e.g. GWAS, eQTL, *-seq). The core of GSEL's approach is the generation of empirical null distributions tailored to the allele frequency and linkage disequilibrium structure of the regions of interest. We illustrate the application of GSEL to variants identified from a GWAS of body mass index, a highly polygenic trait. GSEL is implemented as a fast, flexible and user-friendly python package. It is available with demonstration data at https://github.com/abraham-abin13/gsel_vec. Supplementary data are available at Bioinformatics online.

MBAT-combo: A more powerful test to detect gene-trait associations from GWAS data

Gene-based association tests aggregate multiple SNP-trait associations into sets defined by gene boundaries and are widely used in post-GWAS analysis. A common approach for gene-based tests is to combine SNPs associations by computing the sum of χ2 statistics. However, this strategy ignores the directions of SNP effects, which could result in a loss of power for SNPs with masking effects,e.g.,when the product of two SNP effects and the linkage disequilibrium (LD) correlation is negative. Here, we introduce "mBAT-combo," a set-based test that is better powered than other methods to detect multi-SNP associations in the context of masking effects. We validate the method through simulations and applications to real data. We find that of 35 blood and urine biomarker traits in the UK Biobank, 34 traits show evidence for masking effects in a total of 4,273 gene-trait pairs, indicating that masking effects is common in complex traits. We further validate the improved power of our method in height, body mass index, and schizophrenia with different GWAS sample sizes and show that on average 95.7% of the genes detected only by mBAT-combo with smaller sample sizes can be identified by the single-SNP approach with a 1.7-fold increase in sample sizes. Eleven genes significant only in mBAT-combo for schizophrenia are confirmed by functionally informed fine-mapping or Mendelian randomization integrating gene expression data. The framework of mBAT-combo can be applied to any set of SNPs to refine trait-association signals hidden in genomic regions with complex LD structures.

Investigation of the causal relationship between ALS and autoimmune disorders: a Mendelian randomization study

BackgroundEpidemiological studies have reported an association between amyotrophic lateral sclerosis (ALS) and different autoimmune disorders. This study aims to explore the causal relationship between autoimmune disorders and ALS using Mendelian randomization (MR).MethodsTo test the genetically predicted effects of liability towards immune-related outcomes on ALS risk, we used summary statistics from the largest European genome-wide association studies (GWAS) for these disorders in a two-sample MR setting. To do this, we extracted single nucleotide polymorphisms (SNPs) from the GWAS, which strongly associated with the 12 traits, and queried their effects in a large European ALS GWAS (27,265 cases and 110,881 controls). To avoid bias in our MR instruments related to the complex linkage disequilibrium structure of the human leukocyte antigen (HLA) region, we excluded SNPs within this region from the analyses. We computed inverse-variance weighted (IVW) MR estimates and undertook sensitivity analyses using MR methods robust to horizontal pleiotropy. We also performed a reverse MR analysis testing the causal effects of ALS on the above autoimmune traits.ResultsAfter applying Bonferroni correction for multiple testing, our MR analyses showed that the liability to autoimmune disorders does not affect ALS risk. Our reverse MR analysis also did not support the effects of liability to ALS on other autoimmune disorders. The results of the main IVW MR analyses were generally supported by our sensitivity MR analyses. The variance in the exposures explained by the sets of SNPs used as MR instruments ranged from 8.1 × 10−4 to 0.31. Our MR study was well-powered to detect effects as small as an odds ratio (OR) of 1.045 for ALS in the main MR and as small as an OR of 1.32 in the reverse MR.ConclusionOur MR study does not support a relationship between liability to autoimmune disorders and ALS risk in the European population. The associations observed in epidemiological studies could be partly attributed to shared biology or environmental confounders.

Genetic diversity, population structure, linkage disequilibrium and GWAS for resistance to WSSV in Pacific white shrimp (Litopenaeus vannamei) using a 50K SNP chip

The objectives of this research were to assess the genetic diversity, population structure, linkage disequilibrium and perform a genome-wide association study (GWAS) to search for single nucleotide polymorphisms (SNPs) that associate with the resistance of Pacific white shrimp (Litopenaeus vannamei) to White Spot Syndrome Virus (WSSV). The line here analyzed was formerly founded with animals originated from different origins (Ecuador, Panama, and United States of America), with a history of resistance to WSSV and they have undergone a selection process, currently reaching up to F6 generation. In a controlled challenge to WSSV, this line showed a binary survival (39.0% ± 1.1%) and time to death (109.7 ± 0.9 h post-infection) (n = 1944). The population was genotyped using a commercially available 50 K SNP chip and ∼32 K SNPs remained after quality control of genotypes. Population structure, extent and decay of linkage disequilibrium (LD), and genetic variability in the population were evaluated. The principal component analysis (PCA) did not show apparent genetic stratification in the population. The LD decay estimated, revealed a rapid decrease in r2 as the physical distance between markers increased. The most significant decrease was observed in the first 30Kb of distance, suggesting the need to use a higher density of informative markers to meet the requirement of a genome-wide association study in L. vannamei. The observed and expected heterozygosities were both = 0.38, which indicates that the population presents acceptable genetic variability for WSSV resistance. Two SNPs were significantly associated to WSSV resistance at a genome-wide level, which in turn together explained 0.17% of the genetic variance for the trait. One identified SNP surpassing the genome-wide significance threshold (chromosome 1 at 51207389 bp) is located near potential candidate genes, such as Arylsulfatase B-like (ARSB), and D-beta-hydroxybutyrate dehydrogenase mitochondrial-like (βhyD); which was linked to the humoral immune response to WSSV, in addition to the Putative mediator of RNA polymerase II transcription subunit 26 (PMed26); which are associated with the hepatopancreas immune response against infection of WSSV and acute hepatopancreatic necrosis disease (AHPND). Our results indicate a polygenic architecture for WSSV resistance in L. vannamei, suggesting that incorporating genome-wide SNP information, through genomic selection, might be the most appropriate approach to accelerate the genetic progress for this trait.

Extended population genetic analysis of 12 X-STRs – Exemplified using a Norwegian population sample

The use of X-chromosomal markers to resolve questions of relatedness has experienced a significant increase during the last years in forensic genetics. Perhaps primarily due to the emergence of commercial kits, but equally important due to an increased awareness of the utility of those markers. The X-chromosomal inheritance pattern entails that some cases, for instance paternal half-sisters, can potentially be resolved using a few X-chromosomal markers alone. For the statistical assessment in kinship cases it is of importance to have relevant population frequency data. In the present study 631 unrelated males from a Norwegian population sample are analyzed. The resulting haplotypes are compared to previously studied population samples and a deeper analysis of the linkage disequilibrium (LD) structure is conducted. We demonstrate that the power to detect LD will be low when few males, say below 300, are analyzed. We use entropy to describe the degree of LD between multiallelic loci and describe how this measure varies between different studied populations. Large population frequency databases have been recommended when using X-chromosomal markers, and we show that by combining reference databases from genetically similar populations, more precise haplotype frequency estimates can be obtained for rare haplotypes which improves the statistical assessment of the weight of evidence. In addition, we promote the use of simulations to assess the utility of STR markers in contrast to standard forensic parameters. Specifically we perform extensive simulations on cases where X-chromosomal markers are important and illustrate how the results can be used to infer the information gained from these markers.

Verification of Candidate SNP Effects Reveals Two QTLs on BTA7 for Beef Marbling in Two Japanese Black Cattle Populations

In our previous study, we used genome resequencing to detect all candidate polymorphisms within a quantitative trait loci (QTL) region for beef marbling reported previously at 10–30 Mbp on bovine chromosome 7, and we selected 6044 polymorphisms as candidate quantitative trait nucleotides (QTNs). In the present study, we aimed to identify quantitative trait genes (QTGs) and QTNs in this QTL region by verifying the effect of SNPs on beef marbling in two Japanese Black cattle populations using a Dynamic Array integrated fluidic circuit. In total, 96 selected SNPs were genotyped in 441 and 529 animals in Hyogo and Miyazaki cattle populations, respectively. The most significant p-values were detected in a SNP in a splice region of ALDH7A1 (SNP93_ALDH7A1; p = 3.46 × 10−5) in Hyogo cattle and a missense polymorphism of intercellular adhesion molecule-1 (ICAM1) (SNP37_ICAM1; p = 3.33 × 10−4) in Miyazaki cattle. Interestingly, SNP93_ALDH7A1 was not significant (p = 0.459) in Miyazaki cattle, and SNP37_ICAM1 showed a weakly significant association (p = 0.043) in Hyogo cattle. Thus, each population would likely have different QTGs and QTNs for beef marbling in the QTL region. In the Hyogo population, it was not possible to determine the accurate range of the linkage disequilibrium (LD) block in LD block analysis because of a strong LD structure throughout the assessed region. In Miyazaki cattle, however, an LD block containing SNP37_ICAM1 had a range of 15.8–16.1 Mbp, suggesting that QTNs would be located within this region. The functions of 19 genes in the LD block were investigated. ICAM1 is known to play an important role in adipocyte differentiation; given this function and the effect of amino acid substitution, SNP37_ICAM1 was identified as a promising candidate QTN for beef marbling. Further research on the effect of SNP37_ICAM1 on adipocyte differentiation is expected to provide insights into the mechanism underlying beef marbling formation.

PB1969: FUNCTIONAL CHARACTERIZATION OF SUSCEPTIBILITY LOCI TO MULTIPLE MYELOMA

Background: Multiple myeloma (MM) is the second most common blood malignancy, caused by an uncontrolled growth of plasma cells in the bone marrow, accounting for 20% of all newly diagnosed hematological cancers. Although the current 5-year survival rate is ranging between 40-60%, MM is still considered an incurable disease since most of the patients eventually relapse. While the causes of MM are incompletely understood, several genome-wide association studies (GWAS) have been conducted to identify germline variants that predispose to MM. Up to date, a total of 24 loci were found to be associated with MM risk, but very little information is available about their functional role. Aims: The principal goal is to explore in silico the function of the germline variants associated with MM risk. As we do not know if the GWAS-identified SNPs are the causal variants or just markers of risk, the functional characterization of the causal risk variants would lead to a better understanding of disease development. Methods: GWAS design takes advantage of the linkage disequilibrium (LD) structure of the human genome, thus the main GWAS findings are single-nucleotide polymorphisms (SNPs) that show the strongest association with MM risk (measured as the lowest p-values), but they are not necessarily the functionally causal variants. In this project, we used bioinformatics tools (GTEx, HaploReg v4.1, Roadmap, LDlinke, SNPnexus, RegulomeDB 2.0.3, SNP2TFBS, miRNASNP v3, GeneMANIA) to perform fine mapping of all GWAS-identified loci and to prioritize in each locus the polymorphism with the highest chance of being functionally relevant. In particular, we focused on the loci with the smallest number of SNPs in high LD (r2>0.8) in order to maximize the probability to capture the casual variant. Results: Four of the 24 MM risk loci had a relatively small number of SNPs in high LD and within them we found that the locus located at chromosome 16 contained the greatest number of functionally annotated SNPs. Of particular functional interest was rs3747481 (chr16:30666367 C/T) due to the following reasons: it is a missense variant (protein change: P359L), it is located in the PRR14 gene, that contributes to chromatin hierarchical organization and has a role in gene regulation, has a high CADD PHRED score (22.1). Additionally, according to GTEx portal, rs3747481 is associated with the expression level of the RNF40 gene (which plays a central role in histone code and gene regulation) in whole blood cells (p=3.02-12). It has a score of “1d” in RegulomeDB (eQTL+ TF binding + any motif + DNase peak), meaning that this variant has a high likelihood to affect binding of transcription factors. Some other SNPs (like rs35629860 and rs67128646) in the same LD block show the co-occurrence of H3K4me3 and H3K27me3 histone marks (associated with gene activation and repression, respectively) in promoters and enhancers in B lymphocytes. rs6565197 is predicted to affect the binding of the KLF4 and KLF5 transcription factors which play key roles in cell cycle regulation. Summary/Conclusion: Through a fine mapping of MM risk loci by bioinformatics tools, we found a variant in the locus 16p11.2 that shows in silico a very high probability to have biological role in the risk disease.

Combined GWAS and Transcriptome Analyses Provide New Insights Into the Response Mechanisms of Sunflower Against Drought Stress.

Sunflower is one of the most important oil crops in the world, and drought stress can severely limit its production and quality. To understand the underlying mechanism of drought tolerance, and identify candidate genes for drought tolerance breeding, we conducted a combined genome-wide association studies (GWAS) and RNA-seq analysis. A total of 226 sunflower inbred lines were collected from different regions of China and other countries. Eight phenotypic traits were evaluated under control and drought stress conditions. Genotyping was performed using a Specific-Locus Amplified Fragment Sequencing (SLAF-seq) approach. A total of 934.08 M paired-end reads were generated, with an average Q30 of 91.97%. Based on the 243,291 polymorphic SLAF tags, a total of 94,162 high-quality SNPs were identified. Subsequent analysis of linkage disequilibrium (LD) and population structure in the 226 accessions was carried out based on the 94,162 high-quality SNPs. The average LD decay across the genome was 20 kb. Admixture analysis indicated that the entire population most likely originated from 11 ancestors. GWAS was performed using three methods (MLM, FarmCPU, and BLINK) simultaneously. A total of 80 SNPs showed significant associations with the 8 traits (p < 1.062 × 10−6). Next, a total of 118 candidate genes were found. To obtain more reliable candidate genes, RNA-seq analysis was subsequently performed. An inbred line with the highest drought tolerance was selected according to phenotypic traits. RNA was extracted from leaves at 0, 7, and 14 days of drought treatment. A total of 18,922 differentially expressed genes were obtained. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis showed up-regulated genes were mainly enriched in the branched-chain amino acid catabolic process, while the down-regulated genes were mainly enriched in the photosynthesis-related process. Six DEGs were randomly selected from all DEGs for validation; these genes showed similar patterns in RNA-seq and RT-qPCR analysis, with a correlation coefficient of 0.8167. Through the integration of the genome-wide association study and the RNA-sequencing, 14 candidate genes were identified. Four of them (LOC110885273, LOC110872899, LOC110891369, LOC110920644) were abscisic acid related protein kinases and transcription factors. These genes may play an important role in sunflower drought response and will be used for further study. Our findings provide new insights into the response mechanisms of sunflowers against drought stress and contribute to further genetic breeding.