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Journal of Animal Science May 2022This study investigated using imputed genotypes from non-genotyped animals which were not in the pedigree for the purpose of genetic selection and improving genetic gain...
This study investigated using imputed genotypes from non-genotyped animals which were not in the pedigree for the purpose of genetic selection and improving genetic gain for economically relevant traits. Simulations were used to mimic a 3-breed crossbreeding system that resembled a modern swine breeding scheme. The simulation consisted of three purebred (PB) breeds A, B, and C each with 25 and 425 mating males and females, respectively. Males from A and females from B were crossed to produce AB females (n = 1,000), which were crossed with males from C to produce crossbreds (CB; n = 10,000). The genome consisted of three chromosomes with 300 quantitative trait loci and ~9,000 markers. Lowly heritable reproductive traits were simulated for A, B, and AB (h2 = 0.2, 0.2, and 0.15, respectively), whereas a moderately heritable carcass trait was simulated for C (h2 = 0.4). Genetic correlations between reproductive traits in A, B, and AB were moderate (rg = 0.65). The goal trait of the breeding program was AB performance. Selection was practiced for four generations where AB and CB animals were first produced in generations 1 and 2, respectively. Non-genotyped AB dams were imputed using FImpute beginning in generation 2. Genotypes of PB and CB were used for imputation. Imputation strategies differed by three factors: 1) AB progeny genotyped per generation (2, 3, 4, or 6), 2) known or unknown mates of AB dams, and 3) genotyping rate of females from breeds A and B (0% or 100%). PB selection candidates from A and B were selected using estimated breeding values for AB performance, whereas candidates from C were selected by phenotype. Response to selection using imputed genotypes of non-genotyped animals was then compared to the scenarios where true AB genotypes (trueGeno) or no AB genotypes/phenotypes (noGeno) were used in genetic evaluations. The simulation was replicated 20 times. The average increase in genotype concordance between unknown and known sire imputation strategies was 0.22. Genotype concordance increased as the number of genotyped CB increased with little additional gain beyond 9 progeny. When mates of AB were known and more than 4 progeny were genotyped per generation, the phenotypic response in AB did not differ (P > 0.05) from trueGeno yet was greater (P < 0.05) than noGeno. Imputed genotypes of non-genotyped animals can be used to increase performance when 4 or more progeny are genotyped and sire pedigrees of CB animals are known.
Topics: Animals; Female; Genotype; Hybridization, Genetic; Male; Models, Genetic; Pedigree; Phenotype; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Swine
PubMed: 35451025
DOI: 10.1093/jas/skac148 -
Heredity Mar 2017The proportion of an individual's genome that is identical by descent (GWIBD) can be estimated from pedigrees (inbreeding coefficient 'Pedigree F') or molecular markers...
The proportion of an individual's genome that is identical by descent (GWIBD) can be estimated from pedigrees (inbreeding coefficient 'Pedigree F') or molecular markers ('Marker F'), but both estimators come with error. Assuming unrelated pedigree founders, Pedigree F is the expected proportion of GWIBD given a specific inbreeding constellation. Meiotic recombination introduces variation around that expectation (Mendelian noise) and related pedigree founders systematically bias Pedigree F downward. Marker F is an estimate of the actual proportion of GWIBD but it suffers from the sampling error of markers plus the error that occurs when a marker is homozygous without reflecting common ancestry (identical by state). We here show via simulation of a zebra finch and a human linkage map that three aspects of meiotic recombination (independent assortment of chromosomes, number of crossovers and their distribution along chromosomes) contribute to variation in GWIBD and thus the precision of Pedigree and Marker F. In zebra finches, where the genome contains large blocks that are rarely broken up by recombination, the Mendelian noise was large (nearly twofold larger s.d. values compared with humans) and Pedigree F thus less precise than in humans, where crossovers are distributed more uniformly along chromosomes. Effects of meiotic recombination on Marker F were reversed, such that the same number of molecular markers yielded more precise estimates of GWIBD in zebra finches than in humans. As a consequence, in species inheriting large blocks that rarely recombine, even small numbers of microsatellite markers will often be more informative about inbreeding and fitness than large pedigrees.
Topics: Animals; Chromosome Mapping; Finches; Genetic Linkage; Genetic Markers; Genotyping Techniques; Homozygote; Humans; Inbreeding; Meiosis; Microsatellite Repeats; Pedigree; Recombination, Genetic
PubMed: 27804967
DOI: 10.1038/hdy.2016.95 -
American Journal of Human Genetics Nov 2021Pedigree inference from genotype data is a challenging problem, particularly when pedigrees are sparsely sampled and individuals may be distantly related to their...
Pedigree inference from genotype data is a challenging problem, particularly when pedigrees are sparsely sampled and individuals may be distantly related to their closest genotyped relatives. We present a method that infers small pedigrees of close relatives and then assembles them into larger pedigrees. To assemble large pedigrees, we introduce several formulas and tools including a likelihood for the degree separating two small pedigrees, a generalization of the fast DRUID point estimate of the degree separating two pedigrees, a method for detecting individuals who share background identity-by-descent (IBD) that does not reflect recent common ancestry, and a method for identifying the ancestral branches through which distant relatives are connected. Our method also takes several approaches that help to improve the accuracy and efficiency of pedigree inference. In particular, we incorporate age information directly into the likelihood rather than using ages only for consistency checks and we employ a heuristic branch-and-bound-like approach to more efficiently explore the space of possible pedigrees. Together, these approaches make it possible to construct large pedigrees that are challenging or intractable for current inference methods.
Topics: Algorithms; Female; Genotype; Humans; Likelihood Functions; Male; Models, Genetic; Pedigree
PubMed: 34739834
DOI: 10.1016/j.ajhg.2021.09.013 -
Genetics Mar 2021Errors in genotype calling can have perverse effects on genetic analyses, confounding association studies, and obscuring rare variants. Analyses now routinely...
Errors in genotype calling can have perverse effects on genetic analyses, confounding association studies, and obscuring rare variants. Analyses now routinely incorporate error rates to control for spurious findings. However, reliable estimates of the error rate can be difficult to obtain because of their variance between studies. Most studies also report only a single estimate of the error rate even though genotypes can be miscalled in more than one way. Here, we report a method for estimating the rates at which different types of genotyping errors occur at biallelic loci using pedigree information. Our method identifies potential genotyping errors by exploiting instances where the haplotypic phase has not been faithfully transmitted. The expected frequency of inconsistent phase depends on the combination of genotypes in a pedigree and the probability of miscalling each genotype. We develop a model that uses the differences in these frequencies to estimate rates for different types of genotype error. Simulations show that our method accurately estimates these error rates in a variety of scenarios. We apply this method to a dataset from the whole-genome sequencing of owl monkeys (Aotus nancymaae) in three-generation pedigrees. We find significant differences between estimates for different types of genotyping error, with the most common being homozygous reference sites miscalled as heterozygous and vice versa. The approach we describe is applicable to any set of genotypes where haplotypic phase can reliably be called and should prove useful in helping to control for false discoveries.
Topics: Animals; Aotidae; Female; Genotype; Genotyping Techniques; Male; Pedigree; Reference Standards; Reproducibility of Results
PubMed: 33683359
DOI: 10.1093/genetics/iyaa014 -
BMC Medical Genetics Jul 2020Hearing loss is the most common sensory defect, and it affects over 6% of the population worldwide. Approximately 50-60% of hearing loss patients are attributed to...
BACKGROUND
Hearing loss is the most common sensory defect, and it affects over 6% of the population worldwide. Approximately 50-60% of hearing loss patients are attributed to genetic causes. Currently, more than 100 genes have been reported to cause non-syndromic hearing loss. It is possible and efficient to screen all potential disease-causing genes for hereditary hearing loss by whole exome sequencing (WES).
METHODS
We collected 5 consanguineous pedigrees from Pakistan with hearing loss and applied WES in selected patients for each pedigree, followed by bioinformatics analysis and Sanger validation to identify the causal genes.
RESULTS
Variants in 7 genes were identified and validated in these pedigrees. We identified single candidate variant for 3 pedigrees: GIPC3 (c.937 T > C), LOXHD1 (c.6136G > A) and TMPRSS3 (c.941 T > C). The remaining 2 pedigrees each contained two candidate variants: TECTA (c.4045G > A) and MYO15A (c.3310G > T and c.9913G > C) for one pedigree and DFNB59 (c.494G > A) and TRIOBP (c.1952C > T) for the other pedigree. The candidate variants were validated in all available samples by Sanger sequencing.
CONCLUSION
The candidate variants in hearing-loss genes were validated to be co-segregated in the pedigrees, and they may indicate the aetiologies of hearing loss in such patients. We also suggest that WES may be a suitable strategy for hearing-loss gene screening in clinical detection.
Topics: Consanguinity; Female; Hearing Loss; Humans; Male; Mutation; Pakistan; Pedigree; Reproducibility of Results; Exome Sequencing
PubMed: 32682410
DOI: 10.1186/s12881-020-01087-x -
Scientific Reports Nov 2022As the challenges of food insecurity and population explosion become more pressing, there is a dire need to revamp the existing breeding and animal management systems....
As the challenges of food insecurity and population explosion become more pressing, there is a dire need to revamp the existing breeding and animal management systems. This can be achieved by the introduction of technology for efficiency and the improvement of the genetic merit of animals. A fundamental requirement for animal breeding is the availability of accurate and reliable pedigreed data and tools facilitating sophisticated computations. Keeping this in view, Smart Sheep Breeder (SSB) was developed using the waterfall methodology and multiple programming languages. It is a multi-use online artificial intelligence (AI) based and internet of things (IoT) compatible decision support system (DSS). It is capable of automatic performance recording, farm data management, data mining, biometrical analysis, e-governance, and decision-making in sheep farms. A centralized database was also developed capable of ranking sheep across multiple farms based on genetic merit and effective dissemination of germplasm. The system in India is available as a web-based tool and android application which facilitates performance recording and generates customized reports on various aspects of sheep production. SSB uses artificial intelligence and biometrical genetic algorithms to calculate breeding values, and inbreeding coefficients, construct selection indices and generate pedigree, and history sheets as well as more than 40 types of custom-tailored animal and farm reports and graphs. The algorithms used were validated using on farms using farm data and also by comparison with established methods and software. Smart Sheep Breeder could thus prove to be indispensable for the present farming systems which could be used by sheep farm managers and breeders across India.
Topics: Sheep; Animals; Artificial Intelligence; Inbreeding; Pedigree; India
PubMed: 36371508
DOI: 10.1038/s41598-022-24091-y -
PLoS Genetics Aug 2017Pedigrees contain information about the genealogical relationships among individuals and are of fundamental importance in many areas of genetic studies. However,...
Pedigrees contain information about the genealogical relationships among individuals and are of fundamental importance in many areas of genetic studies. However, pedigrees are often unknown and must be inferred from genetic data. Despite the importance of pedigree inference, existing methods are limited to inferring only close relationships or analyzing a small number of individuals or loci. We present a simulated annealing method for estimating pedigrees in large samples of otherwise seemingly unrelated individuals using genome-wide SNP data. The method supports complex pedigree structures such as polygamous families, multi-generational families, and pedigrees in which many of the member individuals are missing. Computational speed is greatly enhanced by the use of a composite likelihood function which approximates the full likelihood. We validate our method on simulated data and show that it can infer distant relatives more accurately than existing methods. Furthermore, we illustrate the utility of the method on a sample of Greenlandic Inuit.
Topics: Computer Simulation; Data Interpretation, Statistical; Genome, Human; Genotype; Haplotypes; Humans; Likelihood Functions; Models, Genetic; Pedigree; Polymorphism, Single Nucleotide
PubMed: 28827797
DOI: 10.1371/journal.pgen.1006963 -
PLoS Computational Biology Mar 2015Founder populations and large pedigrees offer many well-known advantages for genetic mapping studies, including cost-efficient study designs. Here, we describe PRIMAL...
Founder populations and large pedigrees offer many well-known advantages for genetic mapping studies, including cost-efficient study designs. Here, we describe PRIMAL (PedigRee IMputation ALgorithm), a fast and accurate pedigree-based phasing and imputation algorithm for founder populations. PRIMAL incorporates both existing and original ideas, such as a novel indexing strategy of Identity-By-Descent (IBD) segments based on clique graphs. We were able to impute the genomes of 1,317 South Dakota Hutterites, who had genome-wide genotypes for ~300,000 common single nucleotide variants (SNVs), from 98 whole genome sequences. Using a combination of pedigree-based and LD-based imputation, we were able to assign 87% of genotypes with >99% accuracy over the full range of allele frequencies. Using the IBD cliques we were also able to infer the parental origin of 83% of alleles, and genotypes of deceased recent ancestors for whom no genotype information was available. This imputed data set will enable us to better study the relative contribution of rare and common variants on human phenotypes, as well as parental origin effect of disease risk alleles in >1,000 individuals at minimal cost.
Topics: Algorithms; Female; Founder Effect; Genome, Human; Genomics; Humans; Male; Models, Genetic; Pedigree; Polymorphism, Single Nucleotide; Sequence Analysis, DNA; Software; South Dakota; White People
PubMed: 25735005
DOI: 10.1371/journal.pcbi.1004139 -
Journal of Clinical Laboratory Analysis Nov 2018Taqman fluorescent probe was frequently applied in single nucleotide polymorphism (SNP) genotyping. However, the characteristic of calling error and the influencing...
BACKGROUND
Taqman fluorescent probe was frequently applied in single nucleotide polymorphism (SNP) genotyping. However, the characteristic of calling error and the influencing factors remain unclear.
METHOD
Calling errors of Taqman genotyping was evaluated systematically based on Mendelian inheritance. Twenty-two SNPs were genotyped by Taqman probe for 419 pedigrees. Mendelian genetic errors were counted for every SNP and pedigree. Cluster analysis was applied to investigate the compatibility between Taqman probes and DNA sample.
RESULTS
On one hand, errors were found for all the SNPs. The error number ranged from 4 to 33 with median of 10.5. On the other hand, Mendelian genetic errors showed features of both randomness and cluster. Half of the pedigrees containing errors had only 1 Mendelian genetic error. But there was also a pedigree containing up to 10 Mendelian genetic errors. Furthermore, cluster analysis indicated that errors of different SNPs took place in different pedigree cluster.
CONCLUSION
It could be concluded that calling error is inevitable for Taqman genotyping of large samples. The quality of Taqman probe and DNA sample, as well as their compatibility, may account for the error incidence.
Topics: Cluster Analysis; Diabetes Mellitus, Type 2; Diagnostic Errors; Female; Genotype; Genotyping Techniques; Humans; Male; Nuclear Family; Pedigree; Polymorphism, Single Nucleotide
PubMed: 29943492
DOI: 10.1002/jcla.22613 -
PloS One 2013Studying population isolates with large, complex pedigrees has many advantages for discovering genetic susceptibility loci; however, statistical analyses can be...
Studying population isolates with large, complex pedigrees has many advantages for discovering genetic susceptibility loci; however, statistical analyses can be computationally challenging. Allelic association tests need to be corrected for relatedness among study participants, and linkage analyses require subdividing and simplifying the pedigree structures. We have extended GenomeSIMLA to simulate SNP data in complex pedigree structures based on an Amish pedigree to generate the same structure and distribution of sampled individuals. We evaluated type 1 error rates when no disease SNP was simulated and power when disease SNPs with recessive, additive, and dominant modes of inheritance and odds ratios of 1.1, 1.5, 2.0, and 5.0 were simulated. We generated subpedigrees with a maximum bit-size of 24 using PedCut and performed two-point and multipoint linkage using Merlin. We also ran MQLS on the subpedigrees and unified pedigree. We saw no inflation of type 1 error when running MQLS on either the whole pedigrees or the sub-pedigrees, and we saw low type 1 error for two-point and multipoint linkage. Power was reduced when running MQLS on the subpedigrees versus the whole pedigree, and power was low for two-point and multipoint linkage analyses of the subpedigrees. These data suggest that MQLS has appropriate type 1 error rates in our Amish pedigree structure, and while type 1 error does not seem to be affected when dividing the pedigree prior to linkage analysis, power to detect linkage is diminished when the pedigree is divided.
Topics: Alleles; Amish; Computer Simulation; Female; Genetic Linkage; Genetic Predisposition to Disease; Genome, Human; Humans; Inheritance Patterns; Male; Models, Genetic; Pedigree; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Software
PubMed: 23658753
DOI: 10.1371/journal.pone.0062615