Linkage Disequilibrium Estimates Research Articles

Association between OX40L polymorphism and type 2 diabetes mellitus in Iranians

IntroductionDiabetes mellitus (DM) is one of the leading causes of morbidity and mortality worldwide. It is a multifactorial disease that genetic and environmental factors contribute to its development. The aim of the study was to investigate the association of OX40L promoter gene polymorphisms with type 2 diabetes mellitus (T2DM) in Iranians.Materials and methodsThree hundred and sixty-eight subjects including 184 healthy subjects and 184 T2DM patients were enrolled in our study. Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was applied to detect genotype and allele frequencies of rs3850641, rs1234313 and rs10912580. In addition, SNPStats web tool was applied to estimate haplotype frequency and linkage disequilibrium (LD).ResultsThe distribution of tested polymorphisms was statistically different between the T2DM patients and healthy subjects (P < 0.01). rs1234313 AG (OR = 0.375, 95% CI = 0.193–0.727, P = 0.004) and rs10912580 AG (OR = 0.351, 95% CI = 0.162–0.758, P = 0.008) genotypes were associated with the decreased risk of T2DM in Iranians. Moreover, our prediction revealed that AAG (OR = 0.46, 95% CI= (0.28–0.76), P = 0.0028) and GAG (OR = 0.24, 95% CI= (0.13–0.45), P < 0.0001) haplotypes were related to the reduced risk of the disease. However, the tested polymorphisms had no effect on biochemical parameters and body mass index (BMI) in the patient group (P > 0.05).ConclusionOur findings revealed that OX40L promoter gene polymorphisms are associated with T2DM. Moreover, genotype and allelic variations were related to the decreased risk of T2DM in Iranians. Further studies are recommended to show whether these polymorphic variations could affect OX40/OX40L interaction or OX40L phenotype.

Mining genomic regions associated with agronomic and biochemical traits in quinoa through GWAS

Quinoa (Chenopodium quinoa Willd.), an Andean crop, is a facultative halophyte food crop recognized globally for its high nutritional value and plasticity to adapt to harsh conditions. We conducted a genome-wide association study on a diverse set of quinoa germplasm accessions. These accessions were evaluated for the following agronomic and biochemical traits: days to 50% flowering (DTF), plant height (PH), panicle length (PL), stem diameter (SD), seed yield (SY), grain diameter (GD), and thousand-grain weight (TGW). These accessions underwent genotyping-by-sequencing using the DNBSeq-G400R platform. Among all evaluated traits, TGW represented maximum broad-sense heritability. Our study revealed average SNP density of ≈ 3.11 SNPs/10 kb for the whole genome, with the lowest and highest on chromosomes Cq1B and Cq9A, respectively. Principal component analysis clustered the quinoa population in three main clusters, one clearly representing lowland Chilean accessions, whereas the other two groups corresponded to germplasm from the highlands of Peru and Bolivia. In our germplasm set, we estimated linkage disequilibrium decay to be ≈ 118.5 kb. Marker-trait analyses revealed major and consistent effect associations for DTF on chromosomes 3A, 4B, 5B, 6A, 7A, 7B and 8B, with phenotypic variance explained (PVE) as high as 19.15%. Nine associations across eight chromosomes were also found for saponin content with 20% PVE by qSPN5A.1. More QTLs were identified for PL and TGW on multiple chromosomal locations. We identified putative candidate genes in the genomic regions associated with DTF and saponin content. The consistent and major-effect genomic associations can be used in fast-tracking quinoa breeding for wider adaptation across marginal environments.

GAUSS: a summary-statistics-based R package for accurate estimation of linkage disequilibrium for variants, Gaussian imputation, and TWAS analysis of cosmopolitan cohorts.

As the availability of larger and more ethnically diverse reference panels grows, there is an increase in demand for ancestry-informed imputation of genome-wide association studies (GWAS), and other downstream analyses, e.g. fine-mapping. Performing such analyses at the genotype level is computationally challenging and necessitates, at best, a laborious process to access individual-level genotype and phenotype data. Summary-statistics-based tools, not requiring individual-level data, provide an efficient alternative that streamlines computational requirements and promotes open science by simplifying the re-analysis and downstream analysis of existing GWAS summary data. However, existing tools perform only disparate parts of needed analysis, have only command-line interfaces, and are difficult to extend/link by applied researchers. To address these challenges, we present Genome Analysis Using Summary Statistics (GAUSS)-a comprehensive and user-friendly R package designed to facilitate the re-analysis/downstream analysis of GWAS summary statistics. GAUSS offers an integrated toolkit for a range of functionalities, including (i) estimating ancestry proportion of study cohorts, (ii) calculating ancestry-informed linkage disequilibrium, (iii) imputing summary statistics of unobserved variants, (iv) conducting transcriptome-wide association studies, and (v) correcting for "Winner's Curse" biases. Notably, GAUSS utilizes an expansive, multi-ethnic reference panel consisting of 32953 genomes from 29 ethnic groups. This panel enhances the range and accuracy of imputable variants, including the ability to impute summary statistics of rarer variants. As a result, GAUSS elevates the quality and applicability of existing GWAS analyses without requiring access to subject-level genotypic and phenotypic information. The GAUSS R package, complete with its source code, is readily accessible to the public via our GitHub repository at https://github.com/statsleelab/gauss. To further assist users, we provided illustrative use-case scenarios that are conveniently found at https://statsleelab.github.io/gauss/, along with a comprehensive user guide detailed in Supplementary Text S1.

Correlated Allele Frequency Changes Reveal Clonal Structure and Selection in Temporal Genetic Data.

In evolving populations where the rate of beneficial mutations is large, subpopulations of individuals with competing beneficial mutations can be maintained over long times. Evolution with this kind of clonal structure is commonly observed in a wide range of microbial and viral populations. However, it can be difficult to completely resolve clonal dynamics in data. This is due to limited read lengths in high-throughput sequencing methods, which are often insufficient to directly measure linkage disequilibrium or determine clonal structure. Here, we develop a method to infer clonal structure using correlated allele frequency changes in time-series sequence data. Simulations show that our method recovers true, underlying clonal structures when they are known and accurately estimate linkage disequilibrium. This information can then be combined with other inference methods to improve estimates of the fitness effects of individual mutations. Applications to data suggest novel clonal structures in an E. coli long-term evolution experiment, and yield improved predictions of the effects of mutations on bacterial fitness and antibiotic resistance. Moreover, our method is computationally efficient, requiring orders of magnitude less run time for large data sets than existing methods. Overall, our method provides a powerful tool to infer clonal structures from data sets where only allele frequencies are available, which can also improve downstream analyses.

Identification of Genomic Regions for Traits Associated with Flowering in Cassava (Manihot esculenta Crantz).

Flowering in cassava (Manihot esculenta Crantz) is crucial for the generation of botanical seed for breeding. However, genotypes preferred by most farmers are erect and poor at flowering or never flower. To elucidate the genetic basis of flowering, 293 diverse cassava accessions were evaluated for flowering-associated traits at two locations and seasons in Uganda. Genotyping using the Diversity Array Technology Pty Ltd. (DArTseq) platform identified 24,040 single-nucleotide polymorphisms (SNPs) distributed on the 18 cassava chromosomes. Population structure analysis using principal components (PCs) and kinships showed three clusters; the first five PCs accounted for 49.2% of the observed genetic variation. Linkage disequilibrium (LD) estimation averaged 0.32 at a distance of ~2850 kb (kilo base pairs). Polymorphism information content (PIC) and minor allele frequency (MAF) were 0.25 and 0.23, respectively. A genome-wide association study (GWAS) analysis uncovered 53 significant marker-trait associations (MTAs) with flowering-associated traits involving 27 loci. Two loci, SNPs S5_29309724 and S15_11747301, were associated with all the traits. Using five of the 27 SNPs with a Phenotype_Variance_Explained (PVE) ≥ 5%, 44 candidate genes were identified in the peak SNP sites located within 50 kb upstream or downstream, with most associated with branching traits. Eight of the genes, orthologous to Arabidopsis and other plant species, had known functional annotations related to flowering, e.g., eukaryotic translation initiation factor and myb family transcription factor. This study identified genomic regions associated with flowering-associated traits in cassava, and the identified SNPs can be useful in marker-assisted selection to overcome hybridization challenges, like unsynchronized flowering, and candidate gene validation.

Evaluating and improving health equity and fairness of polygenic scores

Polygenic scores (PGSs) are quantitative metrics for predicting phenotypic values, such as human height or disease status. Some PGS methods require only summary statistics of a relevant genome-wide association study (GWAS) for their score. One such method is Lassosum, which inherits the model selection advantages of Lasso to select a meaningful subset of the GWAS single-nucleotide polymorphisms as predictors from their association statistics. However, even efficient scores like Lassosum, when derived from European-based GWASs, are poor predictors of phenotype for subjects of non-European ancestry; that is, they have limited portability to other ancestries. To increase the portability of Lassosum, when GWAS information and estimates of linkage disequilibrium are available for both ancestries, we propose Joint-Lassosum (JLS). In the simulation settings we explore, JLS provides more accurate PGSs compared to other methods, especially when measured in terms of fairness. In analyses of UK Biobank data, JLS was computationally more efficient but slightly less accurate than a Bayesian comparator, SDPRX. Like all PGS methods, JLS requires selection of predictors, which are determined by data-driven tuning parameters. We describe a new approach to selecting tuning parameters and note its relevance for model selection for any PGS. We also draw connections to the literature on algorithmic fairness and discuss how JLS can help mitigate fairness-related harms that might result from the use of PGSs in clinical settings. While no PGS method is likely to be universally portable, due to the diversity of human populations and unequal information content of GWASs for different ancestries, JLS is an effective approach for enhancing portability and reducing predictive bias.

Joint Linkage and Association Analysis Using GENEHUNTER-MODSCORE with an Application to Familial Pancreatic Cancer

Introduction: Joint linkage and association (JLA) analysis combines two disease gene mapping strategies: linkage information contained in families and association information contained in populations. Such a JLA analysis can increase mapping power, especially when the evidence for both linkage and association is low to moderate. Similarly, an association analysis based on haplotypes instead of single markers can increase mapping power when the association pattern is complex. Methods: In this paper, we present an extension to the GENEHUNTER-MODSCORE software package that enables a JLA analysis based on haplotypes and uses information from arbitrary pedigree types and unrelated individuals. Our new JLA method is an extension of the MOD score approach for linkage analysis, which allows the estimation of trait-model and linkage disequilibrium (LD) parameters, i.e., penetrance, disease-allele frequency, and haplotype frequencies. LD is modeled between alleles at a single diallelic disease locus and up to three diallelic test markers. Linkage information is contributed by additional multi-allelic flanking markers. We investigated the statistical properties of our JLA implementation using extensive simulations, and we compared our approach to another commonly used single-marker JLA test. To demonstrate the applicability of our new method in practice, we analyzed pedigree data from the German National Case Collection for Familial Pancreatic Cancer (FaPaCa). Results: Based on the simulated data, we demonstrated the validity of our JLA-MOD score analysis implementation and identified scenarios in which haplotype-based tests outperformed the single-marker test. The estimated trait-model and LD parameters were in good accordance with the simulated values. Our method outperformed another commonly used JLA single-marker test when the LD pattern was complex. The exploratory analysis of the FaPaCa families led to the identification of a promising genetic region on chromosome 22q13.33, which can serve as a starting point for future mutation analysis and molecular research in pancreatic cancer. Conclusion: Our newly proposed JLA-MOD score method proves to be a valuable gene mapping and characterization tool, especially when either linkage or association information alone provide insufficient power to identify the disease-causing genetic variants.

Efficient estimation for large-scale linkage disequilibrium patterns of the human genome.

In this study, we proposed an efficient algorithm (X-LD) for estimating linkage disequilibrium (LD) patterns for a genomic grid, which can be of inter-chromosomal scale or of small segments. Compared with conventional methods, the proposed method was significantly faster, dropped from O(nm2) to O(n2m)-n the sample size and m the number of SNPs, and consequently we were permitted to explore in depth unknown or reveal long-anticipated LD features of the human genome. Having applied the algorithm for 1000 Genome Project (1KG), we found (1) the extended LD, driven by population structure, universally existed, and the strength of inter-chromosomal LD was about 10% of their respective intra-chromosomal LD in relatively homogeneous cohorts, such as FIN, and to nearly 56% in admixed cohort, such as ASW. (2) After splitting each chromosome into upmost of more than a half million grids, we elucidated the LD of the HLA region was nearly 42 folders higher than chromosome 6 in CEU and 11.58 in ASW; on chromosome 11, we observed that the LD of its centromere was nearly 94.05 folders higher than chromosome 11 in YRI and 42.73 in ASW. (3) We uncovered the long-anticipated inversely proportional linear relationship between the length of a chromosome and the strength of chromosomal LD, and their Pearson's correlation was on average over 0.80 for 26 1KG cohorts. However, this linear norm was so far perturbed by chromosome 11 given its more completely sequenced centromere region. Uniquely chromosome 8 of ASW was found most deviated from the linear norm than any other autosomes. The proposed algorithm has been realized in C++ (called X-LD) and is available at https://github.com/gc5k/gear2, and can be applied to explore LD features in any sequenced populations.

Efficient estimation for large-scale linkage disequilibrium patterns of the human genome

In this study, we proposed an efficient algorithm (X-LD) for estimating linkage disequilibrium (LD) patterns for a genomic grid, which can be of inter-chromosomal scale or of small segments. Compared with conventional methods, the proposed method was significantly faster, dropped from O(nm2) to O(n2m)—n the sample size and m the number of SNPs, and consequently we were permitted to explore in depth unknown or reveal long-anticipated LD features of the human genome. Having applied the algorithm for 1000 Genome Project (1KG), we found (1) the extended LD, driven by population structure, universally existed, and the strength of inter-chromosomal LD was about 10% of their respective intra-chromosomal LD in relatively homogeneous cohorts, such as FIN, and to nearly 56% in admixed cohort, such as ASW. (2) After splitting each chromosome into upmost of more than a half million grids, we elucidated the LD of the HLA region was nearly 42 folders higher than chromosome 6 in CEU and 11.58 in ASW; on chromosome 11, we observed that the LD of its centromere was nearly 94.05 folders higher than chromosome 11 in YRI and 42.73 in ASW. (3) We uncovered the long-anticipated inversely proportional linear relationship between the length of a chromosome and the strength of chromosomal LD, and their Pearson’s correlation was on average over 0.80 for 26 1KG cohorts. However, this linear norm was so far perturbed by chromosome 11 given its more completely sequenced centromere region. Uniquely chromosome 8 of ASW was found most deviated from the linear norm than any other autosomes. The proposed algorithm has been realized in C++ (called X-LD) and is available at https://github.com/gc5k/gear2, and can be applied to explore LD features in any sequenced populations.

Comparative assessment of the effective population size and linkage disequilibrium of Karan Fries cattle revealed viable population dynamics.

Karan Fries (KF), a high-producing composite cattle was developed through crossing indicine Tharparkar cows with taurine bulls (Holstein Friesian, Brown Swiss, and Jersey), to increase the milk yield across India. This composite cattle population must maintain sufficient genetic diversity for long-term development and breed improvement in the coming years. The level of linkage disequilibrium (LD) measures the influence of population genetic forces on the genomic structure and provides insights into the evolutionary history of populations, while the decay of LD is important in understanding the limits of genome-wide association studies for a population. Effective population size (Ne) which is genomically based on LD accumulated over the course of previous generations, is a valuable tool for e valuation of the genetic diversity and level of inbreeding. The present study was undertaken to understand KF population dynamics through the estimation of Ne and LD for the longterm sustainability of these breeds. The present study included 96 KF samples genotyped using Illumina HDBovine array to estimate the effective population and examine the LD pattern. The genotype data were also obtained for other crossbreds (Santa Gertrudis, Brangus, and Beefmaster) and Holstein Friesian cattle for comparison purposes. The average LD between single nucleotide polymorphisms (SNPs) was r2 = 0.13 in the present study. LD decay (r2 = 0.2) was observed at 40 kb inter-marker distance, indicating a panel with 62,765 SNPs was sufficient for genomic breeding value estimation in KF cattle. The pedigree-based Ne of KF was determined to be 78, while the Ne estimates obtained using LD-based methods were 52 (SNeP) and 219 (genetic optimization for Ne estimation), respectively. KF cattle have an Ne exceeding the FAO's minimum recommended level of 50, which was desirable. The study also revealed significant population dynamics of KF cattle and increased our understanding of devising suitable breeding strategies for longterm sustainable development.

Comparative genomic characterization of indigenous fat‐tailed Akkaraman sheep with local and transboundary sheep breeds

AbstractThe domestic sheep with over 1200 breeds descended from those early domesticated animals that are bred for a variety of resources such as meat, milk and wool. Akkaraman, a fat‐tailed indigenous sheep breed of Türkiye, is widespread throughout Central Anatolia, with the largest indigenous sheep population. Assessing the genetic diversity and genomic structure of animal breeds is among the key contributors to deciphering adaptation to environmental extremes and constructing efficient genetic improvement strategies. Therefore, this study aimed to characterize the genome of Akkaraman breed against various world‐renowned transboundary sheep and indigenous sheep with fat and thin tails. Genetic similarities and differences between those breeds have been displayed by estimating and comparing various genetic diversity indices, linkage disequilibrium (LD) estimates and fixation index (FST), runs of homozygosity (ROH) as well as PCA and neighbour‐joining tree analysis. Akkaraman sheep were observed to form a cluster alongside Moghani, Karakas, Tibetan and Cyprus Fat Tail sheep, which are primarily the sole representatives of fat‐tailed sheep in the study. This clustering was evident in both the PCA and neighbour‐joining tree analysis. The Akkaraman sheep was also observed to have the lowest genomic inbreeding and one of the lowest numbers of ROHs, which might also indicate that the breed has not been exposed to historical intensive selection pressure, inbred mating or a massive population bottleneck that might leave strong marks of genomic homozygosity. The results improve our understanding of the genetic diversity in Akkaraman sheep in comparison with certain mainstream sheep breeds as well as those indigenous breeds from around the world. Additionally, findings will also provide valuable insights to perform further GWAS effectively by considering population structure, diversity and LD patterns observed among the breeds while providing practical knowledge that will contribute to designing efficient and successful genome‐based selection programmes for worldwide sheep production systems.

Benchmarking of local genetic correlation estimation methods using summary statistics from genome-wide association studies.

Local genetic correlation evaluates the correlation of additive genetic effects between different traits across the same genetic variants at a genomic locus. It has been proven informative for understanding the genetic similarities of complex traits beyond that captured by global genetic correlation calculated across the whole genome. Several summary-statistics-based approaches have been developed for estimating local genetic correlation, including $\rho$-hess, SUPERGNOVA and LAVA. However, there has not been a comprehensive evaluation of these methods to offer practical guidelines on the choices of these methods. In this study, we conduct benchmark comparisons of the performance of these three methods through extensive simulation and real data analyses. We focus on two technical difficulties in estimating local genetic correlation: sample overlaps across traits and local linkage disequilibrium (LD) estimates when only the external reference panels are available. Our simulations suggest the likelihood of incorrectly identifying correlated regions and local correlation estimation accuracy are highly dependent on the estimation of the local LD matrix. These observations are corroborated by real data analyses of 31 complex traits. Overall, our findings illuminate the distinct results yielded by different methods applied in post-genome-wide association studies (post-GWAS) local correlation studies. We underscore the sensitivity of local genetic correlation estimates and inferences to the precision of local LD estimation. These observations accentuate the vital need for ongoing refinement in methodologies.

Genome-wide association study as a powerful tool for dissecting competitive traits in legumes.

Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.

Fine‐scale spatial genetic structure in a locally abundant native bunchgrass (Achnatherum thurberianum) including distinct lineages revealed within seed transfer zones

AbstractAnalyses of the factors shaping genetic variation in widespread plant species are important for understanding the evolutionary history and local adaptation and have applied significance for guiding conservation and restoration decisions. Thurber's needlegrass (Achnatherum thurberianum) is a widespread, locally abundant grass that inhabits heterogeneous arid environments of western North America and is of restoration significance. It is a common component of shrubland steppe communities in the Great Basin Desert, where drought, fire, and invasive grasses have degraded natural communities. Using a reduced representation sequencing approach, we generated SNP data at 5677 loci across 246 individuals from 17 A. thurberianum populations spanning five previously delineated seed zones from the western Great Basin. Analyses revealed a pronounced population genetic structure, with individuals forming consistent geographical clusters across a variety of population genetic analyses and spatial scales. Low levels of genetic diversity within populations, as well as high population estimates of linkage disequilibrium and relatedness, were consistent with self‐fertilization as a contributor to population differentiation. Variance partitioning and partial redundancy analysis (pRDA) indicated local adaptation to environment as additionally influencing the spatial distribution of genetic variation. The environmental variables driving these results were similar to those implicated in recent genecological work which inferred local adaptation for seed zone delineation. Our analyses also revealed a complex evolutionary history of A. thurberianum in the Great Basin, where previously delineated seed zones contain distantly related populations. Our results indicate evolutionary history, mating system, and differentiation across distinct geographic and environmental scales have shaped genetic variation in A. thurberianum and illustrate how numerous aspects of population genetic variation might require consideration for restoration planning.

Novel genomic markers and genes related to reproduction in prolific Chios dairy sheep: a genome-wide association study.

Genetic architecture of sheep reproduction is increasingly gaining scientific interest due to the major impact on sheep production systems. In the present study, we conducted pedigree-based analyses and genome-wide association studies using the Illumina Ovine SNP50K BeadChip to explore the genetic mechanisms underlying the reproduction of the highly prolific Chios dairy sheep. First lambing age, total prolificacy and maternal lamb survival were selected as representative reproductive traits and estimated as significantly heritable (h2 = 0.07-0.21) with no evident genetic antagonism among traits. We identified novel genome-wide and suggestive significant single-nucleotide polymorphisms (SNPs) on chromosomes 2 and 12 associated with age at first lambing. The new variants detected on chromosome 2 span a region of 357.79 kb with high pairwise linkage disequilibrium estimates (r2 = 0.8-0.9). Functional annotation analysis revealed candidate genes, such as the collagen-type genes and the Myostatin gene, that participate in osteogenesis, myogenesis, skeletal and muscle mass development resembling the functionality of major genes affecting the ovulation rate and prolificacy. Additional functional enrichment analysis associated the collagen-type genes with multiple uterine-related disfunctions, such as cervical insufficiency, uterine prolapse and abnormalities of the uterine cervix. Several genes (e.g., KAZN, PRDM2, PDPN, LRRC28) localised close to the SNP marker on chromosome 12 were grouped in annotation enrichment clusters majorly involved in developmental and biosynthetic pathways, apoptosis, and nucleic acid-templated transcription. Our findings may further contribute to unravel the genomic regions that are important for sheep reproduction and could be incorporated into future selective breeding programmes.

Estimating linkage disequilibrium and selection from allele frequency trajectories.

Genetic sequences collected over time provide an exciting opportunity to study natural selection. In such studies, it is important to account for linkage disequilibrium to accurately measure selection and to distinguish between selection and other effects that can cause changes in allele frequencies, such as genetic hitchhiking or clonal interference. However, most high-throughput sequencing methods cannot directly measure linkage due to short-read lengths. Here we develop a simple method to estimate linkage disequilibrium from time-series allele frequencies. This reconstructed linkage information can then be combined with other inference methods to infer the fitness effects of individual mutations. Simulations show that our approach reliably outperforms inference that ignores linkage disequilibrium and, with sufficient sampling, performs similarly to inference using the true linkage information. We also introduce two regularization methods derived from random matrix theory that help to preserve its performance under limited sampling effects. Overall, our method enables the use of linkage-aware inference methods even for data sets where only allele frequency time series are available.

Estimation of Linkage Disequilibrium, Effective Population Size, and Genetic Parameters of Phenotypic Traits in Dabieshan Cattle.

Dabieshan cattle (DBSC) are a valuable genetic resource for indigenous cattle breeds in China. It is a small to medium-sized breed with slower growth, but with good meat quality and fat deposition. Genetic markers could be used for the estimation of population genetic structure and genetic parameters. In this work, we genotyped the DBSC breeding population (n = 235) with the GeneSeek Genomic Profiler (GGP) 100 k density genomic chip. Genotype data of 222 individuals and 81,579 SNPs were retained after quality control. The average minor allele frequency (MAF) was 0.20 and the average linkage disequilibrium (LD) level (r2) was 0.67 at a distance of 0-50 Kb. The estimated relationship coefficient and effective population size (Ne) were 0.023 and 86 for the current generation. In addition, we used genotype data to estimate the genetic parameters of the population's phenotypic traits. Among them, height at hip cross (HHC) and shin circumference (SC) were rather high heritability traits, with heritability of 0.41 and 0.54, respectively. The results reflected the current cattle population's extent of inbreeding and history. Through the principal breeding parameters, genomic breeding would significantly improve the genetic progress of breeding.

Failing the four-gamete test enables exact phasing: the Corners’ Algorithm

BackgroundFailing the four-gamete test for two polymorphic DNA markers is an indication that two or three rather than four haplotypes segregate in the population. The objective of this paper is to show that when just three haplotypes are segregating, all three haplotypes can be fully and unambiguously phase-resolved.Theory and methodsThe Corners’ Algorithm tests the four corners in a 3 × 3 table of two-locus genotypes. If one of the four corners is filled with zeroes, then the missing haplotype is identified and the phases of all three haplotypes can be unambiguously resolved for all individuals. Three applications of this method are proposed when the four-gamete test fails: (1) direct estimation of linkage disequilibrium (LD), (2) haplotype-based genome-wide association studies (GWAS) of three haplotypes (single-marker GWAS tests for two out of three haplotypes only), and (3) haplotyping of chromosomal regions that are comprised of pairs of single nucleotide polymorphisms (SNPs) that consist of just three haplotypes. An example based on 435 sows with performance records for total number of piglets born is used to illustrate the methods.ResultsOf 20,339 SNPs, approximately 50% of the pairs of flanking SNPs failed the four-gamete test. For those, the expectation maximization (EM) algorithm gave the same results. The average of the absolute value of the difference in r2 between flanking SNPs across the genome between the two methods was 0.00082. Single-marker GWAS (using two of three haplotypes) detected significant associations for total number of piglets born on chromosomes 1, 2, 6, 9, 10, 12, 13, 14, 15, and 18. Haplotype-based GWAS using the third haplotype resolved with the Corners’ Algorithm detected additional significant associations for total number of piglets born on chromosomes 2, 5, 10, 13, 14, 15, and 18. Estimated substitution effects ranged from 0.40 to 1.35 piglets. Haplotyping of chromosomal regions that failed the four-gamete test for any pair of SNPs covered 961 Mb out of the 2249 Mb by the SNP array.ConclusionsThe Corner’s Algorithm allows to fully phase haplotypes when the four-gamete test fails. Longer haplotypes in chromosomal regions in which the four-gamete test fails for any pair of SNPs can be used as a multi-allelic marker with increased polymorphism information content.

Linkage Disequilibrium, Haplotype Block Structures, Effective Population Size and Genome-Wide Signatures of Selection of Two Conservation Herds of the South African Nguni Cattle.

Simple SummaryKnowledge of linkage disequilibrium (LD), haplotypes blocks, and selective sweeps is important for effective application of genomics in breed characterization, improvement, and conservation, amongst other uses. The South African Nguni cattle breed is a Sanga breed that is well known for its ability to adapt to various environmental conditions such as harsh pedoclimatic and socio-economic conditions which exist in semiarid areas. Nguni cattle are characterized by many eco-types and research populations have been established in an effort to conserve the diversity within the breed by sampling in animals from different ecotypes into the herds. In this study, we calculated autosomal linkage disequilibrium, haplotype block structure, and screened for selection sweeps in two Nguni conservation herds of Bartlow Combine (n = 85) and Kokstad (n = 42), whose animals were genotyped on the Illumina High-Density Bovine SNP BeadChip®. The two herds were generally similar based on a number of genetic parameters. Overall, the study implied reduced genetic diversity in the two herds, calling for corrective measures to maintain the diversity of the South African Nguni cattle.The Nguni cattle of South Africa are a Sanga breed, characterized by many eco-types and research populations that have been established in an effort to conserve the diversity within the breed. The aim of this study was to investigate the overall genetic diversity as well as similarities and differences within and between two conservation herds of the South African Nguni Cattle. Mean LD (r2) estimates were 0.413 ± 0.219 for Bartlow Combine and 0.402 ± 0.209 for Kokstad. Genome-wide average LD (r2) decreased with increasing genetic marker distance for both populations from an average of 0.76 ± 0.28 and 0.77 ± 0.27 at 0–1 kb bin to 0.31 ± 0.13 and 0.32 ± 0.13 at 900–1000 kb bin in Bartlow Combine and Kokstad populations, respectively. Variation in LD levels across autosomes was observed in both populations. The results showed higher levels of LD than previously reported in Nguni field populations and other South African breeds, especially at shorter marker distances of less than 20 kb. A total number of 77,305 and 66,237 haplotype blocks covering a total of 1570.09 Mb (61.99% genome coverage) and 1367.42 Mb (53.96% genome coverage) were detected in Bartlow Combine and Kokstad populations, respectively. A total of 18,449 haploblocks were shared between the two populations while 58,856 and 47,788 haploblocks were unique to Bartlow Combine and Kokstad populations, respectively. Effective population size (Ne) results demonstrated a rapid decrease in Ne across generations for both Bartlow Combine and Kokstad conservation herds. Two complementary methods, integrated haplotype score (iHS) and Extend Haplotype Homozygosity Test (XP-EHH), were implemented in this study to detect the selection signatures in the two herds. A total of 553 and 166 selected regions were identified in Bartlow Combine and Kokstad populations, respectively. DAVID and GO terms analysis of the regions under selection reported genes/QTLs associated with fertility, carcass weight, coat colour, immune response, and eye area pigmentation. Some genes, such as HCAR1, GNAI1, PIK3R3, WNT3, RAB5A, BOLA-N (Class IB MHC Antigen QA-2-Related), BOLA (Class IB MHC Antigen QA-2-Related), and Rab-8B, etc., were found in regions under selection in this study. Overall, the study implied reduced genetic diversity in the two herds calling for corrective measures to maintain the diversity of the South African Nguni cattle. This study presented a comprehensive analysis of the genomic architecture of South African Nguni cattle populations, providing essential genetic information of utility in the management of conservation flocks.

Fine-mapping from summary data with the "Sum of Single Effects" model.

In recent work, Wang et al introduced the "Sum of Single Effects" (SuSiE) model, and showed that it provides a simple and efficient approach to fine-mapping genetic variants from individual-level data. Here we present new methods for fitting the SuSiE model to summary data, for example to single-SNP z-scores from an association study and linkage disequilibrium (LD) values estimated from a suitable reference panel. To develop these new methods, we first describe a simple, generic strategy for extending any individual-level data method to deal with summary data. The key idea is to replace the usual regression likelihood with an analogous likelihood based on summary data. We show that existing fine-mapping methods such as FINEMAP and CAVIAR also (implicitly) use this strategy, but in different ways, and so this provides a common framework for understanding different methods for fine-mapping. We investigate other common practical issues in fine-mapping with summary data, including problems caused by inconsistencies between the z-scores and LD estimates, and we develop diagnostics to identify these inconsistencies. We also present a new refinement procedure that improves model fits in some data sets, and hence improves overall reliability of the SuSiE fine-mapping results. Detailed evaluations of fine-mapping methods in a range of simulated data sets show that SuSiE applied to summary data is competitive, in both speed and accuracy, with the best available fine-mapping methods for summary data.