GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19.

Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte-macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).

The effect of family structure on the still-missing heritability and genomic prediction accuracy of type 2 diabetes.

This study aims to assess the effect of familial structures on the still-missing heritability estimate and prediction accuracy of Type 2 Diabetes (T2D) using pedigree estimated risk values (ERV) and genomic ERV. We used 11,818 individuals (T2D cases: 2,210) with genotype (649,932 SNPs) and pedigree information from the ongoing periodic cohort study of the Iranian population project. We considered three different familial structure scenarios, including (i) all families, (ii) all families with ≥ 1 generation, and (iii) families with ≥ 1 generation in which both case and control individuals are presented. Comprehensive simulation strategies were implemented to quantify the difference between estimates of [Formula: see text] and [Formula: see text]. A proportion of still-missing heritability in T2D could be explained by overestimation of pedigree-based heritability due to the presence of families with individuals having only one of the two disease statuses. Our research findings underscore the significance of including families with only case/control individuals in cohort studies. The presence of such family structures (as observed in scenarios i and ii) contributes to a more accurate estimation of disease heritability, addressing the underestimation that was previously overlooked in prior research. However, when predicting disease risk, the absence of these families (as seen in scenario iii) can yield the highest prediction accuracy and the strongest correlation with Polygenic Risk Scores. Our findings represent the first evidence of the important contribution of familial structure for heritability estimations and genomic prediction studies in T2D.

Estimating cancer risk in carriers of Lynch syndrome variants in UK Biobank.

BackgroundLynch syndrome (LS) is an inherited cancer predisposition syndrome caused by genetic variants affecting DNA mismatch repair (MMR) genes MLH1, MSH2, MSH6 and PMS2 Cancer risk in LS is estimated from cohorts of individuals ascertained by individual or family history of cancer, which may upwardly bias estimates. METHODS: 830 carriers of pathogenic or likely pathogenic (path_MMR) MMR gene variants classified by InSiGHT were identified in 454 756 UK Biobank (UKB) participants using whole-exome sequence. Nelson-Aalen survival analysis was used to estimate cumulative incidence of colorectal, endometrial and breast cancer (BC). RESULTS: Cumulative incidence of colorectal and endometrial cancer (EC) by age 70 years was elevated in path_MMR carriers compared with non-carriers (colorectal: 11.8% (95% confidence interval (CI): 9.5% to 14.6%) vs 1.7% (95% CI: 1.6% to 1.7%), endometrial: 13.4% (95% CI: 10.2% to 17.6%) vs 1.0% (95% CI: 0.9% to 1.0%)), but the magnitude of this increase differed between genes. Cumulative BC incidence by age 70 years was not elevated in path_MMR carriers compared with non-carriers (8.9% (95% CI: 6.3% to 12.4%) vs 7.5% (95% CI: 7.4% to 7.6%)). Cumulative cancer incidence estimates in UKB were similar to estimates from the Prospective Lynch Syndrome Database for all genes and cancers, except there was no evidence for elevated EC risk in carriers of pathogenic PMS2 variants in UKB. CONCLUSION: These results support offering incidentally identified carriers of any path_MMR surveillance to manage colorectal cancer risk. Incidentally identified carriers of pathogenic variants in MLH1, MSH2 and MSH6 would also benefit from interventions to reduce EC risk. The results suggest that BC is not an LS-related cancer.

Comprehensive analyses of 723 transcriptomes enhance genetic and biological interpretations for complex traits in cattle.

By uniformly analyzing 723 RNA-seq data from 91 tissues and cell types, we built a comprehensive gene atlas and studied tissue specificity of genes in cattle. We demonstrated that tissue-specific genes significantly reflected the tissue-relevant biology, showing distinct promoter methylation and evolution patterns (e.g., brain-specific genes evolve slowest, whereas testis-specific genes evolve fastest). Through integrative analyses of those tissue-specific genes with large-scale genome-wide association studies, we detected relevant tissues/cell types and candidate genes for 45 economically important traits in cattle, including blood/immune system (e.g., CCDC88C) for male fertility, brain (e.g., TRIM46 and RAB6A) for milk production, and multiple growth-related tissues (e.g., FGF6 and CCND2) for body conformation. We validated these findings by using epigenomic data across major somatic tissues and sperm. Collectively, our findings provided novel insights into the genetic and biological mechanisms underlying complex traits in cattle, and our transcriptome atlas can serve as a primary source for biological interpretation, functional validation, studies of adaptive evolution, and genomic improvement in livestock.

Linking protein to phenotype with Mendelian Randomization detects 38 proteins with causal roles in human diseases and traits.

To efficiently transform genetic associations into drug targets requires evidence that a particular gene, and its encoded protein, contribute causally to a disease. To achieve this, we employ a three-step proteome-by-phenome Mendelian Randomization (MR) approach. In step one, 154 protein quantitative trait loci (pQTLs) were identified and independently replicated. From these pQTLs, 64 replicated locally-acting variants were used as instrumental variables for proteome-by-phenome MR across 846 traits (step two). When its assumptions are met, proteome-by-phenome MR, is equivalent to simultaneously running many randomized controlled trials. Step 2 yielded 38 proteins that significantly predicted variation in traits and diseases in 509 instances. Step 3 revealed that amongst the 271 instances from GeneAtlas (UK Biobank), 77 showed little evidence of pleiotropy (HEIDI), and 92 evidence of colocalization (eCAVIAR). Results were wide ranging: including, for example, new evidence for a causal role of tyrosine-protein phosphatase non-receptor type substrate 1 (SHPS1; SIRPA) in schizophrenia, and a new finding that intestinal fatty acid binding protein (FABP2) abundance contributes to the pathogenesis of cardiovascular disease. We also demonstrated confirmatory evidence for the causal role of four further proteins (FGF5, IL6R, LPL, LTA) in cardiovascular disease risk.

Epigenomics and genotype-phenotype association analyses reveal conserved genetic architecture of complex traits in cattle and human.

BACKGROUND: Lack of comprehensive functional annotations across a wide range of tissues and cell types severely hinders the biological interpretations of phenotypic variation, adaptive evolution, and domestication in livestock. Here we used a combination of comparative epigenomics, genome-wide association study (GWAS), and selection signature analysis, to shed light on potential adaptive evolution in cattle. RESULTS: We cross-mapped 8 histone marks of 1300 samples from human to cattle, covering 178 unique tissues/cell types. By uniformly analyzing 723 RNA-seq and 40 whole genome bisulfite sequencing (WGBS) datasets in cattle, we validated that cross-mapped histone marks captured tissue-specific expression and methylation, reflecting tissue-relevant biology. Through integrating cross-mapped tissue-specific histone marks with large-scale GWAS and selection signature results, we for the first time detected relevant tissues and cell types for 45 economically important traits and artificial selection in cattle. For instance, immune tissues are significantly associated with health and reproduction traits, multiple tissues for milk production and body conformation traits (reflecting their highly polygenic architecture), and thyroid for the different selection between beef and dairy cattle. Similarly, we detected relevant tissues for 58 complex traits and diseases in humans and observed that immune and fertility traits in humans significantly correlated with those in cattle in terms of relevant tissues, which facilitated the identification of causal genes for such traits. For instance, PIK3CG, a gene highly specifically expressed in mononuclear cells, was significantly associated with both age-at-menopause in human and daughter-still-birth in cattle. ICAM, a T cell-specific gene, was significantly associated with both allergic diseases in human and metritis in cattle. CONCLUSION: Collectively, our results highlighted that comparative epigenomics in conjunction with GWAS and selection signature analyses could provide biological insights into the phenotypic variation and adaptive evolution. Cattle may serve as a model for human complex traits, by providing additional information beyond laboratory model organisms, particularly when more novel phenotypes become available in the near future.

Functional annotation of the cattle genome through systematic discovery and characterization of chromatin states and butyrate-induced variations.

BACKGROUND: The functional annotation of genomes, including chromatin accessibility and modifications, is important for understanding and effectively utilizing the increased amount of genome sequences reported. However, while such annotation has been well explored in a diverse set of tissues and cell types in human and model organisms, relatively little data are available for livestock genomes, hindering our understanding of complex trait variation, domestication, and adaptive evolution. Here, we present the first complete global landscape of regulatory elements in cattle and explore the dynamics of chromatin states in rumen epithelial cells induced by the rumen developmental regulator-butyrate. RESULTS: We established the first global map of regulatory elements (15 chromatin states) and defined their coordinated activities in cattle, through genome-wide profiling for six histone modifications, RNA polymerase II, CTCF-binding sites, DNA accessibility, DNA methylation, and transcriptome in rumen epithelial primary cells (REPC), rumen tissues, and Madin-Darby bovine kidney epithelial cells (MDBK). We demonstrated that each chromatin state exhibited specific enrichment for sequence ontology, transcription, methylation, trait-associated variants, gene expression-associated variants, selection signatures, and evolutionarily conserved elements, implying distinct biological functions. After butyrate treatments, we observed that the weak enhancers and flanking active transcriptional start sites (TSS) were the most dynamic chromatin states, occurred concomitantly with significant alterations in gene expression and DNA methylation, which was significantly associated with heifer conception rate and stature economic traits. CONCLUSION: Our results demonstrate the crucial role of functional genome annotation for understanding genome regulation, complex trait variation, and adaptive evolution in livestock. Using butyrate to induce the dynamics of the epigenomic landscape, we were able to establish the correlation among nutritional elements, chromatin states, gene activities, and phenotypic outcomes.

Evidence of epigenetic admixture in the Colombian population.

DNA methylation (DNAm) measured in lymphoblastoid cell lines has been repeatedly demonstrated to differ between various human populations. Due to the role that DNAm plays in controlling gene expression, these differences could significantly contribute to ethnic phenotypic differences. However, because previous studies have compared distinct ethnic groups where genetic and environmental context are confounded, their relative contribution to phenotypic differences between ethnicities remains unclear. Using DNAm assayed in whole blood and colorectal tissue of 132 admixed individuals from Colombia, we identified sites where differential DNAm levels were associated with the local ancestral genetic context. Our results are consistent with population specific DNAm being primarily driven by between population genetic differences in cis, with little environmental contribution, and with consistent effects across tissues. The findings offer new insights into a possible mechanism driving phenotypic differences among different ethnic groups, and could help explain ethnic differences in colorectal cancer incidence.

Indirect assortative mating for human disease and longevity.

Phenotypic correlations among partners for traits such as longevity or late-onset disease have been found to be comparable to phenotypic correlations in first-degree relatives. How these correlations arise in late life is poorly understood. Here we introduce a novel paradigm to establish the presence of indirect assortment on factors correlated across generations, by examining correlations between parents of couples, i.e., in-laws. Using correlations in additive genetic values we further corroborate the presence of indirect assortment on heritable factors. Specifically, using couples from the UK Biobank cohort, we show that longevity and disease history of the parents of White British couples are correlated, with correlations of up to 0.09. The correlations in parental longevity are replicated in the FamiLinx cohort, a larger and geographically more diverse historical ancestry dataset spanning a broader time frame. These correlations in parental longevity significantly (pval < 0.0093 for all pairs of parents) exceed what would be expected due to variations in lifespan based on year and location of birth. For cardiovascular diseases, in particular hypertension, we find significant correlations (r = 0.028, pval = 0.005) in genetic values among partners, supporting a model where partners assort for risk factors to some extent genetically correlated with cardiovascular disease. Partitioning the relative importance of indirect assortative mating and shared common environment will require large, well-characterized longitudinal cohorts aimed at understanding phenotypic correlations among none-blood relatives. Identifying the factors that mediate indirect assortment on longevity and human disease risk will help to unravel factors affecting human disease and ultimately longevity.

Shared activity patterns arising at genetic susceptibility loci reveal underlying genomic and cellular architecture of human disease.

Genetic variants underlying complex traits, including disease susceptibility, are enriched within the transcriptional regulatory elements, promoters and enhancers. There is emerging evidence that regulatory elements associated with particular traits or diseases share similar patterns of transcriptional activity. Accordingly, shared transcriptional activity (coexpression) may help prioritise loci associated with a given trait, and help to identify underlying biological processes. Using cap analysis of gene expression (CAGE) profiles of promoter- and enhancer-derived RNAs across 1824 human samples, we have analysed coexpression of RNAs originating from trait-associated regulatory regions using a novel quantitative method (network density analysis; NDA). For most traits studied, phenotype-associated variants in regulatory regions were linked to tightly-coexpressed networks that are likely to share important functional characteristics. Coexpression provides a new signal, independent of phenotype association, to enable fine mapping of causative variants. The NDA coexpression approach identifies new genetic variants associated with specific traits, including an association between the regulation of the OCT1 cation transporter and genetic variants underlying circulating cholesterol levels. NDA strongly implicates particular cell types and tissues in disease pathogenesis. For example, distinct groupings of disease-associated regulatory regions implicate two distinct biological processes in the pathogenesis of ulcerative colitis; a further two separate processes are implicated in Crohn's disease. Thus, our functional analysis of genetic predisposition to disease defines new distinct disease endotypes. We predict that patients with a preponderance of susceptibility variants in each group are likely to respond differently to pharmacological therapy. Together, these findings enable a deeper biological understanding of the causal basis of complex traits.

The heritability of human disease: estimation, uses and abuses.

Relatives provide the basic material for the study of inheritance of human disease. However, the methodologies for the estimation of heritability and the interpretation of the results have been controversial. The debate arises from the plethora of methods used, the validity of the methodological assumptions and the inconsistent and sometimes erroneous genetic interpretations made. We will discuss how to estimate disease heritability, how to interpret it, how biases in heritability estimates arise and how heritability relates to other measures of familial disease aggregation.

Genome-wide association study and meta-analysis in Northern European populations replicate multiple colorectal cancer risk loci.

Genome-wide association studies have been successful in elucidating the genetic basis of colorectal cancer (CRC), but there remains unexplained variability in genetic risk. To identify new risk variants and to confirm reported associations, we conducted a genome-wide association study in 1,701 CRC cases and 14,082 cancer-free controls from the Finnish population. A total of 9,068,015 genetic variants were imputed and tested, and 30 promising variants were studied in additional 11,647 cases and 12,356 controls of European ancestry. The previously reported association between the single-nucleotide polymorphism (SNP) rs992157 (2q35) and CRC was independently replicated (p = 2.08 × 10-4 ; OR, 1.14; 95% CI, 1.06-1.23), and it was genome-wide significant in combined analysis (p = 1.50 × 10-9 ; OR, 1.12; 95% CI, 1.08-1.16). Variants at 2q35, 6p21.2, 8q23.3, 8q24.21, 10q22.3, 10q24.2, 11q13.4, 11q23.1, 14q22.2, 15q13.3, 18q21.1, 20p12.3 and 20q13.33 were associated with CRC in the Finnish population (false discovery rate < 0.1), but new risk loci were not found. These results replicate the effects of multiple loci on the risk of CRC and identify shared risk alleles between the Finnish population isolate and outbred populations.

The heritability and patterns of DNA methylation in normal human colorectum.

DNA methylation (DNAm) has been linked to changes in chromatin structure, gene expression and disease. The DNAm level can be affected by genetic variation; although, how this differs by CpG dinucleotide density and genic location of the DNAm site is not well understood. Moreover, the effect of disease causing variants on the DNAm level in a tissue relevant to disease has yet to be fully elucidated. To this end, we investigated the phenotypic profiles, genetic effects and regional genomic heritability for 196080 DNAm sites in healthy colorectum tissue from 132 unrelated Colombian individuals. DNAm sites in regions of low-CpG density were more variable, on average more methylated and were more likely to be significantly heritable when compared with DNAm sites in regions of high-CpG density. DNAm sites located in intergenic regions had a higher mean DNAm level and were more likely to be heritable when compared with DNAm sites in the transcription start site (TSS) of a gene expressed in colon tissue. Within CpG-dense regions, the propensity of the DNAm level to be heritable was lower in the TSS of genes expressed in colon tissue than in the TSS of genes not expressed in colon tissue. In addition, regional genetic variation was associated with variation in local DNAm level no more frequently for DNAm sites within colorectal cancer risk regions than it was for DNAm sites outside such regions. Overall, DNAm sites located in different genomic contexts exhibited distinguishable profiles and may have a different biological function.

Variation at 2q35 (PNKD and TMBIM1) influences colorectal cancer risk and identifies a pleiotropic effect with inflammatory bowel disease.

To identify new risk loci for colorectal cancer (CRC), we conducted a meta-analysis of seven genome-wide association studies (GWAS) with independent replication, totalling 13 656 CRC cases and 21 667 controls of European ancestry. The combined analysis identified a new risk association for CRC at 2q35 marked by rs992157 (P = 3.15 × 10-8, odds ratio = 1.10, 95% confidence interval = 1.06-1.13), which is intronic to PNKD (paroxysmal non-kinesigenic dyskinesia) and TMBIM1 (transmembrane BAX inhibitor motif containing 1). Intriguingly this susceptibility single-nucleotide polymorphism (SNP) is in strong linkage disequilibrium (r2 = 0.90, D' = 0.96) with the previously discovered GWAS SNP rs2382817 for inflammatory bowel disease (IBD). Following on from this observation we examined for pleiotropy, or shared genetic susceptibility, between CRC and the 200 established IBD risk loci, identifying an additional 11 significant associations (false discovery rate [FDR]) < 0.05). Our findings provide further insight into the biological basis of inherited genetic susceptibility to CRC, and identify risk factors that may influence the development of both CRC and IBD.

Genome-wide scan of the effect of common nsSNPs on colorectal cancer survival outcome.

BACKGROUND: We conducted a genome-wide scan to identify non-synonymous SNPs (nsSNPs) that might influence survival after a diagnosis of colorectal cancer (CRC). METHODS: We genotyped 7679 nsSNPs in 1939 Scottish patients from the Scottish Colorectal Cancer Study recruited soon after a CRC diagnosis and prospectively followed for survival outcomes. All-cause and CRC-specific survival analyses were conducted using Cox proportional hazard models adjusted for stage, age and sex for all cancer cases, after cancer type stratification and assuming additive and recessive models of inheritance. For all the SNPs that had a p-value < 0.10 a meta-analysis was performed combining the results of the discovery set and a replication set of 899 Scottish CRC patients. The p-value threshold of significance was set as at p < 10-8. RESULTS: 897 and 894 nsSNPs were associated with all-cause and CRC-specific mortality, respectively, at a p-value level < 0.10 in the discovery set. Meta-analysis of the results from the discovery and replication sets was performed overall and for cancers of colon and rectum separately and none of the variants reached a p-value < 10-8. CONCLUSIONS: This large scale well-powered analysis demonstrates that common nsSNPs are not associated with CRC prognosis overall.

Genetic contributions to stability and change in intelligence from childhood to old age.

Understanding the determinants of healthy mental ageing is a priority for society today. So far, we know that intelligence differences show high stability from childhood to old age and there are estimates of the genetic contribution to intelligence at different ages. However, attempts to discover whether genetic causes contribute to differences in cognitive ageing have been relatively uninformative. Here we provide an estimate of the genetic and environmental contributions to stability and change in intelligence across most of the human lifetime. We used genome-wide single nucleotide polymorphism (SNP) data from 1,940 unrelated individuals whose intelligence was measured in childhood (age 11 years) and again in old age (age 65, 70 or 79 years). We use a statistical method that allows genetic (co)variance to be estimated from SNP data on unrelated individuals. We estimate that causal genetic variants in linkage disequilibrium with common SNPs account for 0.24 of the variation in cognitive ability change from childhood to old age. Using bivariate analysis, we estimate a genetic correlation between intelligence at age 11 years and in old age of 0.62. These estimates, derived from rarely available data on lifetime cognitive measures, warrant the search for genetic causes of cognitive stability and change.

Identification of susceptibility loci for colorectal cancer in a genome-wide meta-analysis.

To identify common variants influencing colorectal cancer (CRC) risk, we performed a meta-analysis of five genome-wide association studies, comprising 5626 cases and 7817 controls of European descent. We conducted replication of top ranked single nucleotide polymorphisms (SNPs) in additional series totalling 14 037 cases and 15 937 controls, identifying a new CRC risk locus at 10q24.2 [rs1035209; odds ratio (OR) = 1.13, P = 4.54 × 10(-11)]. We also performed meta-analysis of our studies, with previously published data, of several recently purported CRC risk loci. We failed to find convincing evidence for a previously reported genome-wide association at rs11903757 (2q32.3). Of the three additional loci for which evidence of an association in Europeans has been previously described we failed to show an association between rs59336 (12q24.21) and CRC risk. However, for the other two SNPs, our analyses demonstrated new, formally significant associations with CRC. These are rs3217810 intronic in CCND2 (12p13.32; OR = 1.19, P = 2.16 × 10(-10)) and rs10911251 near LAMC1 (1q25.3; OR = 1.09, P = 1.75 × 10(-8)). Additionally, we found some evidence to support a relationship between, rs647161, rs2423297 and rs10774214 and CRC risk originally identified in East Asians in our European datasets. Our findings provide further insights into the genetic and biological basis of inherited genetic susceptibility to CRC.

Genome-wide association scan identifies a colorectal cancer susceptibility locus on chromosome 8q24.

Using a multistage genetic association approach comprising 7,480 affected individuals and 7,779 controls, we identified markers in chromosomal region 8q24 associated with colorectal cancer. In stage 1, we genotyped 99,632 SNPs in 1,257 affected individuals and 1,336 controls from Ontario. In stages 2-4, we performed serial replication studies using 4,024 affected individuals and 4,042 controls from Seattle, Newfoundland and Scotland. We identified one locus on chromosome 8q24 and another on 9p24 having combined odds ratios (OR) for stages 1-4 of 1.18 (trend; P = 1.41 x 10(-8)) and 1.14 (trend; P = 1.32 x 10(-5)), respectively. Additional analyses in 2,199 affected individuals and 2,401 controls from France and Europe supported the association at the 8q24 locus (OR = 1.16, trend; 95% confidence interval (c.i.): 1.07-1.26; P = 5.05 x 10(-4)). A summary across all seven studies at the 8q24 locus was highly significant (OR = 1.17, c.i.: 1.12-1.23; P = 3.16 x 10(-11)). This locus has also been implicated in prostate cancer.

Evaluation of the linkage-disequilibrium method for the estimation of effective population size when generations overlap: an empirical case.

BACKGROUND: Within the genetic methods for estimating effective population size (N e ), the method based on linkage disequilibrium (LD) has advantages over other methods, although its accuracy when applied to populations with overlapping generations is a matter of controversy. It is also unclear the best way to account for mutation and sample size when this method is implemented. Here we have addressed the applicability of this method using genome-wide information when generations overlap by profiting from having available a complete and accurate pedigree from an experimental population of Iberian pigs. Precise pedigree-based estimates of N e were considered as a baseline against which to compare LD-based estimates. METHODS: We assumed six different statistical models that varied in the adjustments made for mutation and sample size. The approach allowed us to determine the most suitable statistical model of adjustment when the LD method is used for species with overlapping generations. A novel approach used here was to treat different generations as replicates of the same population in order to assess the error of the LD-based N e estimates. RESULTS: LD-based N e estimates obtained by estimating the mutation parameter from the data and by correcting sample size using the 1/2n term were the closest to pedigree-based estimates. The N e at the time of the foundation of the herd (26 generations ago) was 20.8 ± 3.7 (average and SD across replicates), while the pedigree-based estimate was 21. From that time on, this trend was in good agreement with that followed by pedigree-based N e. CONCLUSIONS: Our results showed that when using genome-wide information, the LD method is accurate and broadly applicable to small populations even when generations overlap. This supports the use of the method for estimating N e when pedigree information is unavailable in order to effectively monitor and manage populations and to early detect population declines. To our knowledge this is the first study using replicates of empirical data to evaluate the applicability of the LD method by comparing results with accurate pedigree-based estimates.