Large Genomic Region Free of GWAS-Based Common Variants Contains Fertility-Related Genes

DNA variants, such as single nucleotide polymorphisms (SNPs) and copy number variants (CNVs), are unevenly distributed across the human genome. Currently, dbSNP contains more than 6 million human SNPs, and whole-genome genotyping arrays can assay more than 4 million of them simultaneously. In our study, we first questioned whether published genome-wide association studies (GWASs) assays cover all regions well in the genome. Using dbSNP build 135 data, we identified 50 genomic regions longer than 100 Kb that do not contain any common SNPs, i.e., those with minor allele frequency (MAF)≥1%. Secondly, because conserved regions are generally of functional importance, we tested genes in those large genomic regions without common SNPs. We found 97 genes and were enriched for reproduction function. In addition, we further filtered out regions with CNVs listed in the Database of Genomic Variants (DGV), segmental duplications from Human Genome Project and common variants identified by personal genome sequencing (UCSC). No region survived after those filtering. Our analysis suggests that, while there may not be many large genomic regions free of common variants, there are still some “holes” in the current human genomic map for common SNPs. Because GWAS only focused on common SNPs, interpretation of GWAS results should take this limitation into account. Particularly, two recent GWAS of fertility may be incomplete due to the map deficit. Additional SNP discovery efforts should pay close attention to these regions.     

DeNovoCNN: a deep learning approach to de novo variant calling in next generation sequencing data

De novo mutations (DNMs) are an important cause of genetic disorders. The accurate identification of DNMs from sequencing data is therefore fundamental to rare disease research and diagnostics. Unfortunately, identifying reliable DNMs remains a major challenge due to sequence errors, uneven coverage, and mapping artifacts. Here, we developed a deep convolutional neural network (CNN) DNM caller (DeNovoCNN), that encodes the alignment of sequence reads for a trio as 160164 resolution images. DeNovoCNN was trained on DNMs of 5616 whole exome sequencing (WES) trios achieving total 96.74% recall and 96.55% precision on the test dataset. We find that DeNovoCNN has increased recall/sensitivity and precision compared to existing DNM calling approaches (GATK, DeNovoGear, DeepTrio, Samtools) based on the Genome in a Bottle reference dataset and independent WES and WGS trios. Validations of DNMs based on Sanger and PacBio HiFi sequencing confirm that DeNovoCNN outperforms existing methods. Most importantly, our results suggest that DeNovoCNN is likely robust against different exome sequencing and analyses approaches, thereby allowing the application on other datasets. DeNovoCNN is freely available as a Docker container and can be run on existing alignment (BAM/CRAM) and variant calling (VCF) files from WES and WGS without a need for variant recalling.     

Comparison of whole‐genome (13X) and capture (87X) resequencing methods for SNP and genotype callings

The number of polymorphisms identified with next‐generation sequencing approaches depends directly on the sequencing depth and therefore on the experimental cost. Although higher levels of depth ensure more sensitive and more specific SNP calls, economic constraints limit the increase of depth for whole‐genome resequencing (WGS). For this reason, capture resequencing is used for studies focusing on only some specific regions of the genome. However, several biases in capture resequencing are known to have a negative impact on the sensitivity of SNP detection. Within this framework, the aim of this study was to compare the accuracy of WGS and capture resequencing on SNP detection and genotype calling, which differ in terms of both sequencing depth and biases. Indeed, we have evaluated the SNP calling and genotyping accuracy in a WGS dataset (13X) and in a capture resequencing dataset (87X) performed on 11 individuals. The percentage of SNPs not identified due to a sevenfold sequencing depth decrease was estimated at 7.8% using a down‐sampling procedure on the capture sequencing dataset. A comparison of the 87X capture sequencing dataset with the WGS dataset revealed that capture‐related biases were leading with the loss of 5.2% of SNPs detected with WGS. Nevertheless, when considering the SNPs detected by both approaches, capture sequencing appears to achieve far better SNP genotyping, with about 4.4% of the WGS genotypes that can be considered as erroneous and even 10% focusing on heterozygous genotypes. In conclusion, WGS and capture deep sequencing can be considered equivalent strategies for SNP detection, as the rate of SNPs not identified because of a low sequencing depth in the former is quite similar to SNPs missed because of method biases of the latter. On the other hand, capture deep sequencing clearly appears more adapted for studies requiring great accuracy in genotyping.     

An Integrated Approach for Analyzing Clinical Genomic Variant Data from Next-Generation Sequencing

Next-generation sequencing (NGS) technologies provide the potential for developing high-throughput and low-cost platforms for clinical diagnostics. A limiting factor to clinical applications of genomic NGS is downstream bioinformatics analysis for data interpretation. We have developed an integrated approach for end-to-end clinical NGS data analysis from variant detection to functional profiling. Robust bioinformatics pipelines were implemented for genome alignment, single nucleotide polymorphism (SNP), small insertion/deletion (InDel), and copy number variation (CNV) detection of whole exome sequencing (WES) data from the Illumina platform. Quality-control metrics were analyzed at each step of the pipeline by use of a validated training dataset to ensure data integrity for clinical applications. We annotate the variants with data regarding the disease population and variant impact. Custom algorithms were developed to filter variants based on criteria, such as quality of variant, inheritance pattern, and impact of variant on protein function. The developed clinical variant pipeline links the identified rare variants to Integrated Genome Viewer for visualization in a genomic context and to the Protein Information Resource’s iProXpress for rich protein and disease information. With the application of our system of annotations, prioritizations, inheritance filters, and functional profiling and analysis, we have created a unique methodology for downstream variant filtering that empowers clinicians and researchers to interpret more effectively the relevance of genomic alterations within a rare genetic disease.     

Integrating GWAS and Expression Data for Functional Characterization of Disease-Associated SNPs: An Application to Follicular Lymphoma

Development of post-GWAS (genome-wide association study) methods are greatly needed for characterizing the function of trait-associated SNPs. Strategies integrating various biological data sets with GWAS results will provide insights into the mechanistic role of associated SNPs. Here, we present a method that integrates RNA sequencing (RNA-seq) and allele-specific expression data with GWAS data to further characterize SNPs associated with follicular lymphoma (FL). We investigated the influence on gene expression of three established FL-associated loci—rs10484561, rs2647012, and rs6457327—by measuring their correlation with human-leukocyte-antigen (HLA) expression levels obtained from publicly available RNA-seq expression data sets from lymphoblastoid cell lines. Our results suggest that SNPs linked to the protective variant rs2647012 exert their effect by a cis-regulatory mechanism involving modulation of HLA-DQB1 expression. In contrast, no effect on HLA expression was observed for the colocalized risk variant rs10484561. The application of integrative methods, such as those presented here, to other post-GWAS investigations will help identify causal disease variants and enhance our understanding of biological disease mechanisms.     

One Size Doesn't Fit All - RefEditor: Building Personalized Diploid Reference Genome to Improve Read Mapping and Genotype Calling in Next Generation Sequencing Studies

With rapid decline of the sequencing cost, researchers today rush to embrace whole genome sequencing (WGS), or whole exome sequencing (WES) approach as the next powerful tool for relating genetic variants to human diseases and phenotypes. A fundamental step in analyzing WGS and WES data is mapping short sequencing reads back to the reference genome. This is an important issue because incorrectly mapped reads affect the downstream variant discovery, genotype calling and association analysis. Although many read mapping algorithms have been developed, the majority of them uses the universal reference genome and do not take sequence variants into consideration. Given that genetic variants are ubiquitous, it is highly desirable if they can be factored into the read mapping procedure. In this work, we developed a novel strategy that utilizes genotypes obtained a priori to customize the universal haploid reference genome into a personalized diploid reference genome. The new strategy is implemented in a program named RefEditor. When applying RefEditor to real data, we achieved encouraging improvements in read mapping, variant discovery and genotype calling. Compared to standard approaches, RefEditor can significantly increase genotype calling consistency (from 43% to 61% at 4X coverage; from 82% to 92% at 20X coverage) and reduce Mendelian inconsistency across various sequencing depths. Because many WGS and WES studies are conducted on cohorts that have been genotyped using array-based genotyping platforms previously or concurrently, we believe the proposed strategy will be of high value in practice, which can also be applied to the scenario where multiple NGS experiments are conducted on the same cohort. The RefEditor sources are available at https://github.com/superyuan/refeditor.

This is a PLOS Computational Biology Software Article.

     

Pre-selecting markers based on fixation index scores improved the power of genomic evaluations in a combined Yorkshire pig population

Combining different swine populations in genomic prediction can be an important tool, leading to an increased accuracy of genomic prediction using single nucleotide polymorphism (SNP) chip data compared with within-population genomic. However, the expected higher accuracy of multi-population genomic prediction has not been realized. This may be due to an inconsistent linkage disequilibrium (LD) between SNPs and quantitative trait loci (QTL) across populations, and the weak genetic relationships across populations. In this study, we determined the impact of different genomic relationship matrices, SNP density and pre-selected variants on prediction accuracy using a combined Yorkshire pig population. Our objective was to provide useful strategies for improving the accuracy of genomic prediction within a combined population. Results showed that the accuracy of genomic best linear unbiased prediction (GBLUP) using imputed whole-genome sequencing (WGS) data in the combined population was always higher than that within populations. Furthermore, the use of imputed WGS data always resulted in a higher accuracy of GBLUP than the use of 80K chip data for the combined population. Additionally, the accuracy of GBLUP with a non-linear genomic relationship matrix was markedly increased (0.87% to 15.17% for 80K chip data, and 0.43% to 4.01% for imputed WGS data) compared with that obtained with a linear genomic relationship matrix, except for the prediction of XD population in the combined population using imputed WGS data. More importantly, the application of pre-selected variants based on fixation index (Fst) scores improved the accuracy of multi-population genomic prediction, especially for 80K chip data. For BLUP|GA (BLUP approach given the genetic architecture), the use of a linear method with an appropriate weight to build a weight-relatedness matrix led to a higher prediction accuracy compared with the use of only pre-selected SNPs for genomic evaluations, especially for the total number of piglets born. However, for the non-linear method, BLUP|GA showed only a small increase or even a decrease in prediction accuracy compared with the use of only pre-selected SNPs. Overall, the best genomic evaluation strategy for reproduction-related traits for a combined population was found to be GBLUP performed with a non-linear genomic relationship matrix using variants pre-selected from the 80K chip data based on Fst scores.     

Detection of Genomic Variation by Selection of a 9 Mb DNA Region and High Throughput Sequencing

Detection of the rare polymorphisms and causative mutations of genetic diseases in a targeted genomic area has become a major goal in order to understand genomic and phenotypic variability. We have interrogated repeat-masked regions of 8.9 Mb on human chromosomes 21 (7.8 Mb) and 7 (1.1 Mb) from an individual from the International HapMap Project (NA12872). We have optimized a method of genomic selection for high throughput sequencing. Microarray-based selection and sequencing resulted in 260-fold enrichment, with 41% of reads mapping to the target region. 83% of SNPs in the targeted region had at least 4-fold sequence coverage and 54% at least 15-fold. When assaying HapMap SNPs in NA12872, our sequence genotypes are 91.3% concordant in regions with coverage≥4-fold, and 97.9% concordant in regions with coverage≥15-fold. About 81% of the SNPs recovered with both thresholds are listed in dbSNP. We observed that regions with low sequence coverage occur in close proximity to low-complexity DNA. Validation experiments using Sanger sequencing were performed for 46 SNPs with 15-20 fold coverage, with a confirmation rate of 96%, suggesting that DNA selection provides an accurate and cost-effective method for identifying rare genomic variants.     

Allele-specific copy-number discovery from whole-genome and whole-exome sequencing

Copy-number variants (CNVs) are a major form of genetic variation and a risk factor for various human diseases, so it is crucial to accurately detect and characterize them. It is conceivable that allele-specific reads from high-throughput sequencing data could be leveraged to both enhance CNV detection and produce allele-specific copy number (ASCN) calls. Although statistical methods have been developed to detect CNVs using whole-genome sequence (WGS) and/or whole-exome sequence (WES) data, information from allele-specific read counts has not yet been adequately exploited. In this paper, we develop an integrated method, called AS-GENSENG, which incorporates allele-specific read counts in CNV detection and estimates ASCN using either WGS or WES data. To evaluate the performance of AS-GENSENG, we conducted extensive simulations, generated empirical data using existing WGS and WES data sets and validated predicted CNVs using an independent methodology. We conclude that AS-GENSENG not only predicts accurate ASCN calls but also improves the accuracy of total copy number calls, owing to its unique ability to exploit information from both total and allele-specific read counts while accounting for various experimental biases in sequence data. Our novel, user-friendly and computationally efficient method and a complete analytic protocol is freely available at https://sourceforge.net/projects/asgenseng/.     

BlackOPs: increasing confidence in variant detection through mappability filtering

Identifying variants using high-throughput sequencing data is currently a challenge because true biological variants can be indistinguishable from technical artifacts. One source of technical artifact results from incorrectly aligning experimentally observed sequences to their true genomic origin (‘mismapping’) and inferring differences in mismapped sequences to be true variants. We developed BlackOPs, an open-source tool that simulates experimental RNA-seq and DNA whole exome sequences derived from the reference genome, aligns these sequences by custom parameters, detects variants and outputs a blacklist of positions and alleles caused by mismapping. Blacklists contain thousands of artifact variants that are indistinguishable from true variants and, for a given sample, are expected to be almost completely false positives. We show that these blacklist positions are specific to the alignment algorithm and read length used, and BlackOPs allows users to generate a blacklist specific to their experimental setup. We queried the dbSNP and COSMIC variant databases and found numerous variants indistinguishable from mapping errors. We demonstrate how filtering against blacklist positions reduces the number of potential false variants using an RNA-seq glioblastoma cell line data set. In summary, accounting for mapping-caused variants tuned to experimental setups reduces false positives and, therefore, improves genome characterization by high-throughput sequencing.     

Comprehensive Characterization of Human Genome Variation by High Coverage Whole-Genome Sequencing of Forty Four Caucasians

Whole genome sequencing studies are essential to obtain a comprehensive understanding of the vast pattern of human genomic variations. Here we report the results of a high-coverage whole genome sequencing study for 44 unrelated healthy Caucasian adults, each sequenced to over 50-fold coverage (averaging 65.8×). We identified approximately 11 million single nucleotide polymorphisms (SNPs), 2.8 million short insertions and deletions, and over 500,000 block substitutions. We showed that, although previous studies, including the 1000 Genomes Project Phase 1 study, have catalogued the vast majority of common SNPs, many of the low-frequency and rare variants remain undiscovered. For instance, approximately 1.4 million SNPs and 1.3 million short indels that we found were novel to both the dbSNP and the 1000 Genomes Project Phase 1 data sets, and the majority of which (∼96%) have a minor allele frequency less than 5%. On average, each individual genome carried ∼3.3 million SNPs and ∼492,000 indels/block substitutions, including approximately 179 variants that were predicted to cause loss of function of the gene products. Moreover, each individual genome carried an average of 44 such loss-of-function variants in a homozygous state, which would completely “knock out” the corresponding genes. Across all the 44 genomes, a total of 182 genes were “knocked-out” in at least one individual genome, among which 46 genes were “knocked out” in over 30% of our samples, suggesting that a number of genes are commonly “knocked-out” in general populations. Gene ontology analysis suggested that these commonly “knocked-out” genes are enriched in biological process related to antigen processing and immune response. Our results contribute towards a comprehensive characterization of human genomic variation, especially for less-common and rare variants, and provide an invaluable resource for future genetic studies of human variation and diseases.     

Exploration of Genetic Variations through Single‐cell Whole‐genome Sequencing in the Model Ciliate Tetrahymena thermophila

Ciliates are unicellular eukaryotes with separate germline and somatic genomes and diverse life cycles, which make them a unique model to improve our understanding of population genetics through the detection of genetic variations. However, traditional sequencing methods cannot be directly applied to ciliates because the majority are uncultivated. Single‐cell whole‐genome sequencing (WGS) is a powerful tool for studying genetic variation in microbes, but no studies have been performed in ciliates. We compared the use of single‐cell WGS and bulk DNA WGS to detect genetic variation, specifically single nucleotide polymorphisms (SNPs), in the model ciliate Tetrahymena thermophila. Our analyses showed that (i) single‐cell WGS has excellent performance regarding mapping rate and genome coverage but lower sequencing uniformity compared with bulk DNA WGS due to amplification bias (which was reproducible); (ii) false‐positive SNP sites detected by single‐cell WGS tend to occur in genomic regions with particularly high sequencing depth and high rate of C:G to T:A base changes; (iii) SNPs detected in three or more cells should be reliable (an detection efficiency of 83.4–97.4% was obtained for combined data from three cells). This analytical method could be adapted to measure genetic variation in other ciliates and broaden research into ciliate population genetics.     

Network- and attribute-based classifiers can prioritize genes and pathways for autism spectrum disorders and intellectual disability

Autism spectrum disorders (ASD) are a group of related neurodevelopmental disorders with significant combined prevalence (～1%) and high heritability. Dozens of individually rare genes and loci associated with high-risk for ASD have been identified, which overlap extensively with genes for intellectual disability (ID). However, studies indicate that there may be hundreds of genes that remain to be identified. The advent of inexpensive massively parallel nucleotide sequencing can reveal the genetic underpinnings of heritable complex diseases, including ASD and ID. However, whole exome sequencing (WES) and whole genome sequencing (WGS) provides an embarrassment of riches, where many candidate variants emerge. It has been argued that genetic variation for ASD and ID will cluster in genes involved in distinct pathways and protein complexes. For this reason, computational methods that prioritize candidate genes based on additional functional information such as protein-protein interactions or association with specific canonical or empirical pathways, or other attributes, can be useful. In this study we applied several supervised learning approaches to prioritize ASD or ID disease gene candidates based on curated lists of known ASD and ID disease genes. We implemented two network-based classifiers and one attribute-based classifier to show that we can rank and classify known, and predict new, genes for these neurodevelopmental disorders. We also show that ID and ASD share common pathways that perturb an overlapping synaptic regulatory subnetwork. We also show that features relating to neuronal phenotypes in mouse knockouts can help in classifying neurodevelopmental genes. Our methods can be applied broadly to other diseases helping in prioritizing newly identified genetic variation that emerge from disease gene discovery based on WES and WGS.     

Identification of de novo germline mutations and causal genes for sporadic diseases using trio‐based whole‐exome/genome sequencing

Whole‐genome or whole‐exome sequencing (WGS/WES) of the affected proband together with normal parents (trio) is commonly adopted to identify de novo germline mutations (DNMs) underlying sporadic cases of various genetic disorders. However, our current knowledge of the occurrence and functional effects of DNMs remains limited and accurately identifying the disease‐causing DNM from a group of irrelevant DNMs is complicated. Herein, we provide a general‐purpose discussion of important issues related to pathogenic gene identification based on trio‐based WGS/WES data. Specifically, the relevance of DNMs to human sporadic diseases, current knowledge of DNM biogenesis mechanisms, and common strategies or software tools used for DNM detection are reviewed, followed by a discussion of pathogenic gene prioritization. In addition, several key factors that may affect DNM identification accuracy and causal gene prioritization are reviewed. Based on recent major advances, this review both sheds light on how trio‐based WGS/WES technologies can play a significant role in the identification of DNMs and causal genes for sporadic diseases, and also discusses existing challenges.     

Genomic Analyses Reveal Mutational Signatures and Frequently Altered Genes in Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers worldwide and the fourth most lethal cancer in China. However, although genomic studies have identified some mutations associated with ESCC, we know little of the mutational processes responsible. To identify genome-wide mutational signatures, we performed either whole-genome sequencing (WGS) or whole-exome sequencing (WES) on 104 ESCC individuals and combined our data with those of 88 previously reported samples. An APOBEC-mediated mutational signature in 47% of 192 tumors suggests that APOBEC-catalyzed deamination provides a source of DNA damage in ESCC. Moreover, PIK3CA hotspot mutations (c.1624G>A [p.Glu542Lys] and c.1633G>A [p.Glu545Lys]) were enriched in APOBEC-signature tumors, and no smoking-associated signature was observed in ESCC. In the samples analyzed by WGS, we identified focal (<100 kb) amplifications of CBX4 and CBX8. In our combined cohort, we identified frequent inactivating mutations in AJUBA, ZNF750, and PTCH1 and the chromatin-remodeling genes CREBBP and BAP1, in addition to known mutations. Functional analyses suggest roles for several genes (CBX4, CBX8, AJUBA, and ZNF750) in ESCC. Notably, high activity of hedgehog signaling and the PI3K pathway in approximately 60% of 104 ESCC tumors indicates that therapies targeting these pathways might be particularly promising strategies for ESCC. Collectively, our data provide comprehensive insights into the mutational signatures of ESCC and identify markers for early diagnosis and potential therapeutic targets.     

Whole-Genome Sequencing of Individuals from a Founder Population Identifies Candidate Genes for Asthma

Asthma is a complex genetic disease caused by a combination of genetic and environmental risk factors. We sought to test classes of genetic variants largely missed by genome-wide association studies (GWAS), including copy number variants (CNVs) and low-frequency variants, by performing whole-genome sequencing (WGS) on 16 individuals from asthma-enriched and asthma-depleted families. The samples were obtained from an extended 13-generation Hutterite pedigree with reduced genetic heterogeneity due to a small founding gene pool and reduced environmental heterogeneity as a result of a communal lifestyle. We sequenced each individual to an average depth of 13-fold, generated a comprehensive catalog of genetic variants, and tested the most severe mutations for association with asthma. We identified and validated 1960 CNVs, 19 nonsense or splice-site single nucleotide variants (SNVs), and 18 insertions or deletions that were out of frame. As follow-up, we performed targeted sequencing of 16 genes in 837 cases and 540 controls of Puerto Rican ancestry and found that controls carry a significantly higher burden of mutations in IL27RA (2.0% of controls; 0.23% of cases; nominal p = 0.004; Bonferroni p = 0.21). We also genotyped 593 CNVs in 1199 Hutterite individuals. We identified a nominally significant association (p = 0.03; Odds ratio (OR) = 3.13) between a 6 kbp deletion in an intron of NEDD4L and increased risk of asthma. We genotyped this deletion in an additional 4787 non-Hutterite individuals (nominal p = 0.056; OR = 1.69). NEDD4L is expressed in bronchial epithelial cells, and conditional knockout of this gene in the lung in mice leads to severe inflammation and mucus accumulation. Our study represents one of the early instances of applying WGS to complex disease with a large environmental component and demonstrates how WGS can identify risk variants, including CNVs and low-frequency variants, largely untested in GWAS.     

Patterns of relatedness and genetic diversity inferred from whole genome sequencing of archival blood fluke miracidia (Schistosoma japonicum)

Genomic approaches hold great promise for resolving unanswered questions about transmission patterns and responses to control efforts for schistosomiasis and other neglected tropical diseases. However, the cost of generating genomic data and the challenges associated with obtaining sufficient DNA from individual schistosome larvae (miracidia) from mammalian hosts have limited the application of genomic data for studying schistosomes and other complex macroparasites. Here, we demonstrate the feasibility of utilizing whole genome amplification and sequencing (WGS) to analyze individual archival miracidia. As an example, we sequenced whole genomes of 22 miracidia from 11 human hosts representing two villages in rural Sichuan, China, and used these data to evaluate patterns of relatedness and genetic diversity. We also down-sampled our dataset to test how lower coverage sequencing could increase the cost effectiveness of WGS while maintaining power to accurately infer relatedness. Collectively, our results illustrate that population-level WGS datasets are attainable for individual miracidia and represent a powerful tool for ultimately providing insight into overall genetic diversity, parasite relatedness, and transmission patterns for better design and evaluation of disease control efforts.     

Effect of reduced genomic representation on using runs of homozygosity for inbreeding characterization

Genomic measures of inbreeding based on identical-by-descent (IBD) segments are increasingly used to measure inbreeding and mostly estimated on SNP arrays and whole-genome sequencing (WGS) data. However, some softwares recurrently used for their estimation assume that genomic positions which have not been genotyped are nonvariant. This might be true for WGS data, but not for reduced genomic representations and can lead to spurious IBD segments estimation. In this project, we simulated the outputs of WGS, two SNP arrays of different sizes and RAD-sequencing for three populations with different sizes and histories. We compare the results of IBD segments estimation with two softwares: runs of homozygosity (ROHs) estimated with PLINK and homozygous-by-descent (HBD) segments estimated with RZooRoH. We demonstrate that to obtain meaningful estimates of inbreeding, RZooRoH requires a SNPs density 11 times smaller compared to PLINK: ranks of inbreeding coefficients were conserved among individuals above 22 SNPs/Mb for PLINK and 2 SNPs/Mb for RZooRoH. We also show that in populations with simple demographic histories, distribution of ROHs and HBD segments are correctly estimated with both SNP arrays and WGS. PLINK correctly estimated distribution of ROHs with SNP densities above 22 SNPs/Mb, while RZooRoH correctly estimated distribution of HBD segments with SNPs densities above 11 SNPs/Mb. However, in a population with a more complex demographic history, RZooRoH resulted in better distribution of IBD segments estimation compared to PLINK even with WGS data. Consequently, we advise researchers to use either methods relying on excess homozygosity averaged across SNPs or model-based HBD segments calling methods for inbreeding estimations.