Linkage Disequilibrium Patterns Research Articles

The ε4‐bearing TOMM40‐APOE‐APOC1 haplotype but not the ε4 allele confers an exceptionally high risk of Alzheimer’s disease

The APOE gene harboring the ε2 and ε4 alleles encoded by minor alleles of rs7412 and rs429358 single nucleotide polymorphisms (SNPs), respectively, is one of the most studied genes in humans. Despite that, the problem of whether the associations between these alleles and the late-onset Alzheimer's disease (referred to as AD) are due to these alleles themselves remains controversial. This problem is complicated by an inherent heterogeneity in genetic predisposition to AD, which is common for complex diseases in late life.We used a comprehensive, two-stage strategy to gain insights into a more homogeneous genetic architecture of AD in subjects of European ancestry in the TOMM40-APOE-APOC1 region, emphasizing the role of the ε4 allele. First, we examined differences in linkage disequilibrium (LD) patterns in AD-affected and unaffected subjects who carry or do not carry the ε4 or ε2 allele to prioritize SNP pairs according to the LD differences. Second, rs429358 and SNPs from the top pair were selected to construct compound genotypes and associate them with AD. The discovery analyses used a mega-sample of 2,673 AD-affected and 16,246 cognitively normal subjects from four independent studies. The landmark Alzheimer's Disease Genetics Consortium initiative cohort (3,662 AD-cases and 1,541 controls) was used for validation.The top LD difference between the most unfavorable AD-affected ε2-free sample and the most favorable AD- and ε4-free sample was for rs2075650 (TOMM40) and rs12721046 (APOC1) SNPs, Δr=77.6% (p<10-100 ). The meta-analysis results showed the exceptionally high risk of AD for carriers of the full minor allele homozygote comprised of rs429358, rs2075650, and rs12721046 SNPs, odds ratio=18.1, p=1.96×10-77 . This risk was 4.37-fold (p=1.34×10-3 ) higher than that for carriers of two copies of the ε4 alleles who do not have minor alleles of rs2075650 and rs12721046.This study provides compelling evidence that the strongest genetic predisposition to AD in the APOE region is credited to a haplotype comprised of the ε4 allele and TOMM40 (rs2075650) and APOC1 (rs12721046) minor alleles, but not to the ε4 allele itself. This more homogeneous polygenic profile is promising for assessing subjects at an exceptionally high risk of AD.

LDexpress: an online tool for integrating population-specific linkage disequilibrium patterns with tissue-specific expression data

Genome-wide association studies have identified thousands of genetic susceptibility loci associated with cancer as well as other traits and diseases. Mapping germline variation in identified genetic susceptibility regions to alterations in nearby gene expression nominates candidate genes potentially related to disease risk for further functional investigation. We developed LDexpress as an online resource that integrates population-specific linkage disequilibrium data from the 1000 Genomes (1000G) project and tissue-specific expression data from the Genotype-Tissue Expression project to better study regional germline variation impacting gene expression. LDexpress is a publicly available web tool designed to be easy to use, flexible to conduct a wide range of variant queries, and quick to efficiently investigate dozens of query variants across multiple tissue types. We demonstrate the utility of LDexpress using example genomic queries and anticipate this tool will accelerate understanding of disease etiology by uncovering associations of regional germline variation to nearby gene expression.

Insights on Genetic Diversity, Population Structure, and Linkage Disequilibrium in Globally Diverse Coconut Accessions Using Genotyping-by-Sequencing.

Genotyping-by-sequencing (GBS) has emerged as a cost-effective approach for genome-wide discovery of single-nucleotide polymorphism (SNP) markers and high-throughput genotyping. In this study, 96 coconut palms, representing 16 accessions from globally diverse origins, were genotyped using the GBS strategy. A total of 10,835 high-quality SNPs, which were identified after stringent filtering, were utilized to assess genetic diversity, population structure, and linkage disequilibrium (LD) analyses. The polymorphism information content (PIC) values of SNPs ranged from 0.1 to 0.4, with a large proportion of SNPs (8633 nos.; 79.7%) having a higher PIC in the range of 0.3-0.4. The genetic diversity analysis revealed the existence of a high level of variation in coconut accessions, with an average expected heterozygosity (He) value of 0.43. Unweighted neighbor-joining phylogenetic tree and Bayesian-based model population structure grouped coconut genotypes into four main clusters. The accessions are generally clustered based on their height (tall or dwarf), with a few accession clusterings based on geographical origins. Investigation of LD pattern in coconut indicated a relatively rapid LD decay with a short range (9 kb). The results obtained in this study will contribute to enhancing the capacity of coconut researchers to utilize genetic diversity for further genetic improvement. In addition, it would open up possibilities for performing genomic studies such as genome-wide association studies and genomic selection to accelerate the efficiency and speed of coconut genetic improvement.

An Overview of Strategies for Detecting Genotype-Phenotype Associations Across Ancestrally Diverse Populations.

Genome-wide association studies (GWAS) have been very successful at identifying genetic variants influencing a large number of traits. Although the great majority of these studies have been performed in European-descent individuals, it has been recognised that including populations with differing ancestries enhances the potential for identifying causal SNPs due to their differing patterns of linkage disequilibrium. However, when individuals from distinct ethnicities are included in a GWAS, it is necessary to implement a number of control steps to ensure that the identified associations are real genotype-phenotype relationships. In this Review, we discuss the analyses that are required when performing multi-ethnic studies, including methods for determining ancestry at the global and local level for sample exclusion, controlling for ancestry in association testing, and post-GWAS interrogation methods such as genomic control and meta-analysis. We hope that this overview provides a primer for those researchers interested in including distinct populations in their studies.

Local Ancestry Adjusted Allelic Association Analysis Robustly Captures Tuberculosis Susceptibility Loci.

Pulmonary tuberculosis (TB), caused by Mycobacterium tuberculosis, is a complex disease. The risk of developing active TB is in part determined by host genetic factors. Most genetic studies investigating TB susceptibility fail to replicate association signals particularly across diverse populations. South African populations arose because of multi-wave genetic admixture from the indigenous KhoeSan, Bantu-speaking Africans, Europeans, Southeast Asian-and East Asian populations. This has led to complex genetic admixture with heterogenous patterns of linkage disequilibrium and associated traits. As a result, precise estimation of both global and local ancestry is required to prevent both false positive and false-negative associations. Here, 820 individuals from South Africa were genotyped on the SNP-dense Illumina Multi-Ethnic Genotyping Array (∼1.7M SNPs) followed by local and global ancestry inference using RFMix. Local ancestry adjusted allelic association (LAAA) models were utilized owing to the extensive genetic heterogeneity present in this population. Hence, an interaction term, comprising the identification of the minor allele that corresponds to the ancestry present at the specific locus under investigation, was included as a covariate. One SNP (rs28647531) located on chromosome 4q22 was significantly associated with TB susceptibility and displayed a SNP minor allelic effect (G allele, frequency = 0.204) whilst correcting for local ancestry for Bantu-speaking African ancestry (p-value = 5.518 × 10−7; OR = 3.065; SE = 0.224). Although no other variants passed the significant threshold, clear differences were observed between the lead variants identified for each ancestry. Furthermore, the LAAA model robustly captured the source of association signals in multi-way admixed individuals from South Africa and allowed the identification of ancestry-specific disease risk alleles associated with TB susceptibility that have previously been missed.

PSXIV-26 Selection footprints in Russian red cattle identified by linkage disequilibrium blocks based on SNP data

Abstract Among the variety of cattle breeds in Russia, the Russian red dual-purpose cattle breeds have great importance because of their ability to produce high milk yields as well as to provide excellent milk quality. The low census size of the Russian red cattle breeds requires development of programs for conservation of their biodiversity. Our study aimed to investigate selection footprints in Russian red cattle breeds, using high values of linkage disequilibrium (LD) in SNP haplotype blocks as indicators. For finding such LD blocks, we used the genotypes (≈35K SNPs) of Red Gorbatov (RGB, n = 26), Bestuzhev (BST,n = 27), and Suksun (SKS,n = 17) breeds, as well as Red Holstein (RH,n = 16) as an outgroup. Quality control and LD calculations for different distances were performed in Plink 1.90. Top 0.01% SNP pairs by LD value (0.9≤r2&amp;lt; 1.0) were selected for further analysis. The effective population size derived from LD patterns was estimated using SNeP tool. Comparison of LD values for 70 kb interval between breeds and chromosomes by MANOVA pairwise testing significantly distinguished RH/RGB and BST/SKS breeds (P &amp;lt; 0.05-0.001). LD values among chromosomes were 0.195–0.287 for RH, 0.194–0.272 for RGB, 0.172–0.237 for BST, and 0.157–0.217 for SKS. The SKS and BST breeds had higher Ne values (84 and 113, respectively) compared to RH (63) and RGB (79). Selection footprints by LD blocks in Russian red cattle genome covered several relevant genes on BTA1 (EPHA6,DGKG), BTA2 (LRP1B,THSD7B,STAT1), BTA5 (CPM,BAIAP2L2), BTA9 (TRDN,UTRN), BTA10 (KCNN2,CAPN3), BTA11 (SH3RF3,RABGAP1,RALGPS1), BTA14 (ZNF16,ARHGAP39,TOX,DGAT1), and BTA19 (MYH10), BTA22 (FHIT). Detected genes were found to be responsible for milk fat and protein contents, fatty acid composition, somatic cells score, fertility, feet and legs, and udder conformation traits. Our results can be useful for developing the breeding and conservation programs of the Russian red cattle genetic resources. The study was funded by RFBR within project No. 20-516-00020

Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices

Genomic prediction models are often calibrated using multi-generation data. Over time, as data accumulates, training data sets become increasingly heterogeneous. Differences in allele frequency and linkage disequilibrium patterns between the training and prediction genotypes may limit prediction accuracy. This leads to the question of whether all available data or a subset of it should be used to calibrate genomic prediction models. Previous research on training set optimization has focused on identifying a subset of the available data that is optimal for a given prediction set. However, this approach does not contemplate the possibility that different training sets may be optimal for different prediction genotypes. To address this problem, we recently introduced a sparse selection index (SSI) that identifies an optimal training set for each individual in a prediction set. Using additive genomic relationships, the SSI can provide increased accuracy relative to genomic-BLUP (GBLUP). Non-parametric genomic models using Gaussian kernels (KBLUP) have, in some cases, yielded higher prediction accuracies than standard additive models. Therefore, here we studied whether combining SSIs and kernel methods could further improve prediction accuracy when training genomic models using multi-generation data. Using four years of doubled haploid maize data from the International Maize and Wheat Improvement Center (CIMMYT), we found that when predicting grain yield the KBLUP outperformed the GBLUP, and that using SSI with additive relationships (GSSI) lead to 5–17% increases in accuracy, relative to the GBLUP. However, differences in prediction accuracy between the KBLUP and the kernel-based SSI were smaller and not always significant.

Detection and Visualization of Heterozygosity-Rich Regions and Runs of Homozygosity in Worldwide Sheep Populations.

Simple SummaryHeterozygosity-rich regions (HRRs) are regions of high heterozygosity, which can harbor important genes associated with key functional traits such as immune response and disease resilience. Runs of homozygosity (ROH) are contiguous homozygous segments of the genome, which can be informative of the population’s history, structure, demography events, and overall genetic diversity. We first detected factors impacting the identification of ROH and HRR in worldwide sheep populations, which were artificially selected for specific purposes or under natural conditions. We also identified common regions of high homozygosity or heterozygosity among these populations, where a diversity of candidate genes with distinct functions might indicate differential selection pressure on these regions in breeds with different trait expression. Moreover, we evaluated a tool commonly used in the corporate environment, making use of the business intelligence (BI) concept to support managers in the decision-making process, which allowed us to combine results from multiple analyses and create visualization schemes integrating different information. Our findings and proposed tools contribute to the development of more efficient breeding strategies and conservation of genetic resources in sheep and other livestock species.In this study, we chose 17 worldwide sheep populations of eight breeds, which were intensively selected for different purposes (meat, milk, or wool), or locally-adapted breeds, in order to identify and characterize factors impacting the detection of runs of homozygosity (ROH) and heterozygosity-rich regions (HRRs) in sheep. We also applied a business intelligence (BI) tool to integrate and visualize outputs from complementary analyses. We observed a prevalence of short ROH, and a clear distinction between the ROH profiles across populations. The visualizations showed a fragmentation of medium and long ROH segments. Furthermore, we tested different scenarios for the detection of HRR and evaluated the impact of the detection parameters used. Our findings suggest that HRRs are small and frequent in the sheep genome; however, further studies with higher density SNP chips and different detection methods are suggested for future research. We also defined ROH and HRR islands and identified common regions across the populations, where genes related to a variety of traits were reported, such as body size, muscle development, and brain functions. These results indicate that such regions are associated with many traits, and thus were under selective pressure in sheep breeds raised for different purposes. Interestingly, many candidate genes detected within the HRR islands were associated with brain integrity. We also observed a strong association of high linkage disequilibrium pattern with ROH compared with HRR, despite the fact that many regions in linkage disequilibrium were not located in ROH regions.

Relationship between genetically determined telomere length and glioma risk.

BackgroundTelomere maintenance is increasingly recognized as being fundamental to glioma oncogenesis with longer leukocyte telomere length (LTL) reported to increase risk of glioma. To gain further insight into the relationship between telomere genetics and risk of glioma, we conducted several complementary analyses, using genome-wide association studies data on LTL (78 592 individuals) and glioma (12 488 cases and 18 169 controls).MethodsWe performed both classical and summary Mendelian randomization (SMR), coupled with heterogeneity in dependent instruments tests, at genome-wide significant LTL loci to examine if an association was mediated by the same causal variant in glioma. To prioritize genes underscoring glioma-LTL associations, we analyzed gene expression and DNA methylation data.ResultsGenetically increased LTL was significantly associated with increased glioma risk, random-effects inverse variance weighted ORs per 1 SD unit increase in the putative risk factor (odds ratio [OR]SD) 4.79 (95% confidence interval: 2.11-10.85; P = 1.76 × 10−4). SMR confirmed the previously reported LTL associations at 3q26.2 (TERC; PSMR = 1.33 × 10−5), 5p15.33 (TERT; PSMR = 9.80 × 10−27), 10q24.33 (STN1 alias OBFC1; PSMR = 4.31 × 10−5), and 20q13.3 (STMN3/RTEL1; PSMR = 2.47 × 10−4) glioma risk loci. Our analysis implicates variation at 1q42.12 (PSMR = 1.55 × 10−2), 6p21.3 (PSMR = 9.76 × 10−3), 6p22.2 (PSMR = 5.45 × 10−3), 7q31.33 (PSMR = 6.52 × 10−3), and 11q22.3 (PSMR = 8.89 × 10−4) as risk factors for glioma risk. While complicated by patterns of linkage disequilibrium, genetic variation involving PARP1, PRRC2A, CARMIL1, POT1, and ATM-NPAT1 was implicated in the etiology of glioma.ConclusionsThese observations extend the role of telomere-related genes in the development of glioma.

Association analysis for agronomic traits in wheat under terminal heat stress.

Terminal heat stress causes irreversible damage to wheat crop productivity. It reduces the vegetative growth and flowering period that consequently declines the efficiency to capture available stem reserves (carbohydrates) in grains. Markers associated with thermotolerant traits ease in marker assisted selection (MAS) for crop improvement. It identifies the genomic regions associated with thermotolerant traits in wheat, but the scarcity of markers is the major hindrance in crop improvement. Therefore, 158 wheat genotypes were subjected to genotyping with 165 simple sequence repeat markers dispersed on three genomes (A, B and D). Allelic frequency and polymorphic information content values were highest on genome A (5.34 (14% greater than the lowest value at genome D) and 0.715 (3% greater than the lowest value at genome D)), chromosome 4 (5.40 (16% greater than the lowest value at chromosome 2) and 0.725 (5% greater than the lowest value at chromosome 6)) and marker xgwm44 (13.0 (84% greater than the lowest value at marker xbarc148) and 0.916 (46% greater than the lowest value at marker xbarc148)). Bayesian based population structure discriminated the wheat genotypes into seven groups based on genetic similarity indicating their ancestral origin and geographical ecotype. Linkage disequilibrium pattern had highest significant (P<0.001) linked loci pairs 732 on genome A at r2>0.1 whereas, 58 on genome B at r2>0.5. Linkage disequilibrium decay (P<0.01 and r2>0.1) had larger LD block (5-10cM) on genome A. Highly significant MTAs (P<0.000061) under heat stress conditions were identified for flag leaf area (xwmc336), spikelet per spike (xwmc553), grains per spike (cxfa2147, xwmc418 and xwmc121), biomass (xbarc7) and grain yield (xcfa2147 and xwmc671). The identified markers in this study could facilitate in MAS and gene pyramiding against heat stress in wheat.

Generalizability of Polygenic Risk Scores for Breast Cancer Among Women With European, African, and Latinx Ancestry

Multiple polygenic risk scores (PRSs) for breast cancer have been developed from large research consortia; however, their generalizability to diverse clinical settings is unknown. To examine the performance of previously developed breast cancer PRSs in a clinical setting for women of European, African, and Latinx ancestry. This cohort study using the Electronic Medical Records and Genomics (eMERGE) network data set included 39 591 women from 9 contributing medical centers in the US that had electronic medical records (EMR) linked to genotype data. Breast cancer cases and controls were identified through a validated EMR phenotyping algorithm. Multivariable logistic regression was used to assess the association between breast cancer risk and 7 previously developed PRSs, adjusting for age, study site, breast cancer family history, and first 3 ancestry informative principal components. This study included 39 591 women: 33 594 with European, 3801 with African, and 2196 with Latinx ancestry. The mean (SD) age at breast cancer diagnosis was 60.7 (13.0), 58.8 (12.5), and 60.1 (13.0) years for women with European, African, and Latinx ancestry, respectively. PRSs derived from women with European ancestry were associated with breast cancer risk in women with European ancestry (highest odds ratio [OR] per 1-SD increase, 1.46; 95% CI, 1.41-1.51), women with Latinx ancestry (highest OR, 1.31; 95% CI, 1.09-1.58), and women with African ancestry (OR, 1.19; 95% CI, 1.05-1.35). For women with European ancestry, this association with breast cancer risk was largest in the extremes of the PRS distribution, with ORs ranging from 2.19 (95% CI, 1.84-2.53) to 2.48 (95% CI, 1.89-3.25) for the 3 different PRSs examined for those in the highest 1% of the PRS compared with those in the middle quantile. Among women with Latinx and African ancestries at the extremes of the PRS distribution, there were no statistically significant associations. This cohort study found that PRS models derived from women with European ancestry for breast cancer risk generalized well for women with European, Latinx, and African ancestries across different clinical settings, although the effect sizes for women with African ancestry were smaller, likely because of differences in risk allele frequencies and linkage disequilibrium patterns. These results highlight the need to improve representation of diverse population groups, particularly women with African ancestry, in genomic research cohorts.

Negative linkage disequilibrium between amino acid changing variants reveals interference among deleterious mutations in the human genome.

Evolutionary forces like Hill-Robertson interference and negative epistasis can lead to deleterious mutations being found on distinct haplotypes. However, the extent to which these forces depend on the selection and dominance coefficients of deleterious mutations and shape genome-wide patterns of linkage disequilibrium (LD) in natural populations with complex demographic histories has not been tested. In this study, we first used forward-in-time simulations to predict how negative selection impacts LD. Under models where deleterious mutations have additive effects on fitness, deleterious variants less than 10 kb apart tend to be carried on different haplotypes relative to pairs of synonymous SNPs. In contrast, for recessive mutations, there is no consistent ordering of how selection coefficients affect LD decay, due to the complex interplay of different evolutionary effects. We then examined empirical data of modern humans from the 1000 Genomes Project. LD between derived alleles at nonsynonymous SNPs is lower compared to pairs of derived synonymous variants, suggesting that nonsynonymous derived alleles tend to occur on different haplotypes more than synonymous variants. This result holds when controlling for potential confounding factors by matching SNPs for frequency in the sample (allele count), physical distance, magnitude of background selection, and genetic distance between pairs of variants. Lastly, we introduce a new statistic HR(j) which allows us to detect interference using unphased genotypes. Application of this approach to high-coverage human genome sequences confirms our finding that nonsynonymous derived alleles tend to be located on different haplotypes more often than are synonymous derived alleles. Our findings suggest that interference may play a pervasive role in shaping patterns of LD between deleterious variants in the human genome, and consequently influences genome-wide patterns of LD.

Genome-Wide Analyses Reveal the Genetic Architecture and Candidate Genes of Indicine, Taurine, Synthetic Crossbreds, and Locally Adapted Cattle in Brazil

Cattle population history, breeding systems, and geographic subdivision may be reflected in runs of homozygosity (ROH), effective population size (Ne), and linkage disequilibrium (LD) patterns. Thus, the assessment of this information has become essential to the implementation of genomic selection on purebred and crossbred cattle breeding programs. In this way, we assessed the genotype of 19 cattle breeds raised in Brazil belonging to taurine, indicine, synthetic crossbreds, and Iberian-derived locally adapted ancestries to evaluate the overall LD decay patterns, Ne, ROH, and breed composition. We were able to obtain a general overview of the genomic architecture of cattle breeds currently raised in Brazil and other tropical countries. We found that, among the evaluated breeds, different marker densities should be used to improve the genomic prediction accuracy and power of genome-wide association studies. Breeds showing low Ne values indicate a recent inbreeding, also reflected by the occurrence of longer ROH, which demand special attention in the matting schemes to avoid extensive inbreeding. Candidate genes (e.g., ABCA7, PENK, SPP1, IFNAR1, IFNAR2, SPEF2, PRLR, LRRTM1, and LRRTM4) located in the identified ROH islands were evaluated, highlighting biological processes involved with milk production, behavior, rusticity, and fertility. Furthermore, we were successful in obtaining the breed composition regarding the taurine and indicine composition using single-nucleotide polymorphism (SNP) data. Our results were able to observe in detail the genomic backgrounds that are present in each breed and allowed to better understand the various contributions of ancestor breeds to the modern breed composition to the Brazilian cattle.

Association of TNF-α polymorphisms (-857, -863 and -1031), TNF-α serum level and lipid profile with acne vulgaris.

BackgroundAcne is an inflammatory condition principally affected by genetic and dietary factors. Investigation into functional polymorphisms of TNF-α gene and their association with acne vulgaris will be helpful in exploring genetic influence on skin immune mediated inflammatory events. In the present study, we analyzed association of TNF-α gene polymorphisms, its expression levels and lipid profiles in a large cohort of acne patients and controls.MethodsWe used PCR-RFLP to study association of TNF-α polymorphisms at −857C/T, −863C/A and −1031 T/C sites with acne vulgaris. Lipid profiles were measured using enzymatic end-point method. The serum levels of TNF-α and apolipoprotein a were measured using ELISA. NIH, LDlink was used to investigate patterns of linkage disequilibrium across south Asian reference genome (Punjabi from Lahore Pakistan).ResultsWe found that TNF-α −863 polymorphism is strongly associated with acne in overall population as well as in gender and severity based groups of acne patients. Polymorphisms at −863 and −1031 position were in linkage disequilibrium. Importantly, TNF-α serum level was significantly increased in acne patients with severe disease symptoms. Furthermore, levels of total cholesterol (TC) and triglycerides (TG) were significantly increased, whereas high density lipoprotein cholesterol (HDL-C) level was significantly decreased in acne patients. The levels of apolipoprotein a varied widely in studied populations and no significant difference was found in the analyzed groups.ConclusionIn conclusion, we found that TNF-α expression increases in acne patients affected by TNF-α polymorphisms, and that the lipid profile is specifically disrupted in acne patients.

An ancestral recombination graph of human, Neanderthal, and Denisovan genomes.

Many humans carry genes from Neanderthals, a legacy of past admixture. Existing methods detect this archaic hominin ancestry within human genomes using patterns of linkage disequilibrium or direct comparison to Neanderthal genomes. Each of these methods is limited in sensitivity and scalability. We describe a new ancestral recombination graph inference algorithm that scales to large genome-wide datasets and demonstrate its accuracy on real and simulated data. We then generate a genome-wide ancestral recombination graph including human and archaic hominin genomes. From this, we generate a map within human genomes of archaic ancestry and of genomic regions not shared with archaic hominins either by admixture or incomplete lineage sorting. We find that only 1.5 to 7% of the modern human genome is uniquely human. We also find evidence of multiple bursts of adaptive changes specific to modern humans within the past 600,000 years involving genes related to brain development and function.

Evolution of Recombination Landscapes in Diverging Populations of Bread Wheat.

Reciprocal exchanges of DNA (crossovers) that occur during meiosis are mandatory to ensure the production of fertile gametes in sexually reproducing species. They also contribute to shuffle parental alleles into new combinations thereby fueling genetic variation and evolution. However, due to biological constraints, the recombination landscape is highly heterogeneous along the genome which limits the range of allelic combinations and the adaptability of populations. An approach to better understand the constraints on the recombination process is to study how it evolved in the past. In this work, we tackled this question by constructing recombination profiles in four diverging bread wheat (Triticum aestivum L.) populations established from 371 landraces genotyped at 200,062 SNPs. We used linkage disequilibrium (LD) patterns to estimate in each population the past distribution of recombination along the genome and characterize its fine-scale heterogeneity. At the megabase scale, recombination rates derived from LD patterns were consistent with family-based estimates obtained from a population of 406 recombinant inbred lines. Among the four populations, recombination landscapes were positively correlated between each other and shared a statistically significant proportion of highly recombinant intervals. However, this comparison also highlighted that the similarity in recombination landscapes between populations was significantly decreasing with their genetic differentiation in most regions of the genome. This observation was found to be robust to SNPs ascertainment and demography and suggests a relatively rapid evolution of factors determining the fine-scale localization of recombination in bread wheat.

Diversity, divergence and density: How habitat and hybrid zone dynamics maintain a genomic cline in an intertidal barnacle

AbstractAimAs within‐species genomic data have been shown useful in interpreting broader biogeographic trends, we analysed the mode of population genomic isolation involved in a well‐studied intertidal genomic cline to better understand the mechanisms maintaining it. These results were interpreted in the context of spatial variation in habitat use and availability as well as likely fitness consequences for hybridization between the two lineages.LocationPacific coast of North America.TaxonArthropods (Class Maxillopoda, Order Sessilia, Family Balanidae;Balanus glandula).MethodsGenotype‐by‐sequencing approaches were used to generate single‐nucleotide polymorphism markers across sites sampled between southern Alaska and Southern California. Inference using standard population genomic methods, including analysis of population structure, inbreeding and linkage disequilibrium, was used to identify the steepest transitions across the largest number of loci examined. These data were put in the context of observed population density and habitat availability.ResultsWe show that the majority of markers analysed show strong clinal transitions in a very narrow portion of the California coast. Patterns of linkage disequilibrium among markers, along with prior evidence of variation in reproductive potential by latitude and by mitochondrial lineage, suggest some reproductive isolation among the northern and southern lineages ofB. glandulathat are concordant with the drop in population density and habitat availability in central California.Main ConclusionsA significant clinal transition in genomic diversity is stronger and more localized than previously recognized and exhibits statistical patterns suggesting that the lineages are reproductively and phenotypically distinct in ways that may be ecologically important. As this species has been used to infer process in coastal biogeography, further study of concordant patterns will be important for advancing our understanding of this region.

The evolution of brain size among the Homininae and selection at ASPM and MCPH1 genes

We examined the evolutionary relationship of the ASPM (abnormal spindle-like microcephaly associated) and MCPH1 (microcephalin-1) genes with brain volume among humans and other primates. We obtained sequences of these genes from 14 simiiform species including hominins. Two phylogenetic analyses of ASPM exon 3 and MCPH1 exons 8 and 11 were performed to maximize taxon sampling or sequence extension to compare the nucleotide substitution and encephalization rates, and examine signals of selection. Further assessment of selection among humans was done through the analysis of non-synonymous and synonymous substitutions (dN/dS), and linkage disequilibrium (LD) patterns. We found that the accelerated evolution of brain size in hominids, is related to synchronic acceleration in the substitution rates of ASPM and MCPH1, and to signals of positive selection, especially in hominins. The dN/dS and LD analyses in Homo detected sites under positive selection and some regions with haplotype blocks at several candidate sites surrounded by blocks in LD-equilibrium. Accelerations and signals of positive selection in ASPM and MCPH1 occurred in different lineages and periods being ASPM more closely related with the brain evolution of hominins. MCPH1 evolved under positive selection in different lineages of the Catarrhini, suggesting independent evolutionary roles of this gene among primates.

245-OR: Comprehensive Genome-Wide Association Study (GWAS) Meta-analysis Using TOPMed Imputation in Latinos Identifies Rare Variation Associated with Type 2 Diabetes (T2D)

The high prevalence of T2D among Latino individuals emphasizes the importance of studying both environmental and genetic factors that influence disease in this population. However, Latino is a recently admixed population with complex linkage disequilibrium patterns that impose methodological challenges, especially when using genotype imputation to test association with rare variation. The recent release of the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) reference panel, comprising more than 100,000 samples (~10% are Latinos), represents a unique opportunity to explore rare genetic variation in this population. We performed the largest T2D GWAS meta-analysis in the Latino population imputed to the TOPMed panel. Our analysis includes 18,294 individuals (7,556 cases and 10,738 controls) with genotyping array and whole-exome sequencing data. We were able to impute 38M high-quality variants (rsq&amp;gt;0.8) with TOPMed (5M with minor allele frequency [MAF] 0.001-0.0005) compared to 16M with 1000G Phase 3 panel (1.6M with [MAF] 0.001-0.0005). This allowed us to replicate previously reported T2D-associated common variants, and to identify a novel genome-wide significant variant (MAF=0.016, OR=2.06, P=1.7×10-9) near ORC5, a gene implicated in familial hyperinsulinemic hypoglycemia 2. Two additional suggestive novel signals merit further research: a variant in ROR2 (MAF=0.066, OR=1.3, P=8.8×10-8) and a Latino specific variant in HDAC2 only identified with the use of TOPMed (MAF=0.013, OR=1.82, P=3.2×10-7) and associated with insulin secretion in individuals without diabetes in this study. Our results demonstrate the utility of the TOPMed imputation for the identification of novel and population-specific rare variation associated with T2D in understudied populations, which could lead to the identification of novel therapeutic targets and the improvement of polygenic risk scores. Disclosure A. Huerta: None. Mgb biobank: n/a. J. B. Cole: None. P. Dornbos: None. D. A. Dicorpo: None. A. Leong: None. J. C. Florez: Consultant; Self; Goldfinch Bio, Inc., Other Relationship; Self; Novo Nordisk. J. M. Mercader: None. Sigma t2d consortium: n/a. Mx biobank: n/a. Funding American Diabetes Association (1-19-ICTS-068 to J.M.M.)

SnpXplorer: a web application to explore human SNP-associations and annotate SNP-sets.

Genetic association studies are frequently used to study the genetic basis of numerous human phenotypes. However, the rapid interrogation of how well a certain genomic region associates across traits as well as the interpretation of genetic associations is often complex and requires the integration of multiple sources of annotation, which involves advanced bioinformatic skills. We developed snpXplorer, an easy-to-use web-server application for exploring Single Nucleotide Polymorphisms (SNP) association statistics and to functionally annotate sets of SNPs. snpXplorer can superimpose association statistics from multiple studies, and displays regional information including SNP associations, structural variations, recombination rates, eQTL, linkage disequilibrium patterns, genes and gene-expressions per tissue. By overlaying multiple GWAS studies, snpXplorer can be used to compare levels of association across different traits, which may help the interpretation of variant consequences. Given a list of SNPs, snpXplorer can also be used to perform variant-to-gene mapping and gene-set enrichment analysis to identify molecular pathways that are overrepresented in the list of input SNPs. snpXplorer is freely available at https://snpxplorer.net. Source code, documentation, example files and tutorial videos are available within the Help section of snpXplorer and at https://github.com/TesiNicco/snpXplorer.