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Anne-louise Leutenegger - One of the best experts on this subject based on the ideXlab platform.
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high level of Inbreeding in final phase of 1000 genomes project
Scientific Reports, 2015Co-Authors: Steven Gazal, Emmanuelle Genin, Anne-louise Leutenegger, Mourad Sahbatou, Marieclaude BabronAbstract:The 1000 Genomes Project provides a unique source of whole genome sequencing data for studies of human population genetics and human diseases. The last release of this project includes more than 2,500 sequenced individuals from 26 populations. Although relationships among individuals have been investigated in some of the populations, Inbreeding has never been studied. In this article, we estimated the genomic Inbreeding Coefficient of each individual and found an unexpected high level of Inbreeding in 1000 Genomes data: nearly a quarter of the individuals were inbred and around 4% of them had Inbreeding Coefficients similar or greater than the ones expected for first-cousin offspring. Inbred individuals were found in each of the 26 populations, with some populations showing proportions of inbred individuals above 50%. We also detected 227 previously unreported pairs of close relatives (up to and including first-cousins). Thus, we propose subsets of unrelated and outbred individuals, for use by the scientific community. In addition, because admixed populations are present in the 1000 Genomes Project, we performed simulations to study the robustness of Inbreeding Coefficient estimates in the presence of admixture. We found that our multi-point approach (FSuite) was quite robust to admixture, unlike single-point methods (PLINK).
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Inbreeding Coefficient estimation with dense SNP data: comparison of strategies and application to HapMap III.
Human Heredity, 2014Co-Authors: Steven Gazal, Emmanuelle Genin, Mourad Sahbatou, Hervé Perdry, Sébastien Letort, Anne-louise LeuteneggerAbstract:Background/Aims: If the parents of an individual are related, it is possible for the individual to have received at 1 locus 2 identical-by-descent alleles that are copies of a single allele carried by the parents' common ancestor. The Inbreeding Coefficient measures the probability of this event and increases with increasing relatedness between the parents. It is traditionally computed from the observed Inbreeding loops in the genealogies and its accuracy thus depends on the depth and reliability of the genealogies. With the availability of genome-wide genetic data, it has become possible to compute a genome-based Inbreeding Coefficient f, and different methods have been developed to estimate f and identify inbred individuals in a sample from the observed patterns of homozygosity at markers. Methods: For this paper, we performed simulations with known genealogies using different SNP panels with different levels of linkage disequilibrium (LD) to compare several estimators of f, including single-point estimates, methods based on the length of runs of homozygosity (ROHs) and different methods that use hidden Markov models (HMMs). We also compared the performances of some of these estimators to identify inbred individuals in a sample using either HMM likelihood ratio tests or an adapted version of the ERSA software. Results: Single-point methods were found to have higher standard deviations than other methods. ROHs gave the best estimates provided the correct length threshold is known. HMMs on sparse data gave equivalent or better results than HMMs modeling LD. Provided LD is correctly accounted for, the Inbreeding estimates were very similar using the different SNP panels. The HMM likelihood ratio tests were found to perform better at detecting inbred individuals in a sample than the adapted ERSA. All methods accurately detected Inbreeding up to second-cousin offspring. We applied the best method on release 3 of the HapMap phase III project, found up to 4% of inbred individuals, and created HAP1067, an unrelated and outbred dataset of this release. Conclusions: We recommend using HMMs on multiple sparse maps to estimate and detect Inbreeding in large samples. If the sample of individuals is too small to estimate allele frequencies, we advise to estimate them on reference panels or to use 1,500-kb ROHs. Finally, we suggest to investigators using HapMap to be careful with inbred individuals, especially in the GIH (Gujarati Indians from Houston in Texas) population.
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Inbreeding Coefficient Estimation with Dense SNP Data: Comparison of Strategies and Application to HapMap III
Human Heredity, 2014Co-Authors: Steven Gazal, Emmanuelle Genin, Mourad Sahbatou, Hervé Perdry, Sébastien Letort, Anne-louise LeuteneggerAbstract:Background/AimsIf the parents of an individual are related, it is possible for the individual to have received at a locus two identical by descent (IBD) alleles that are copies of a single allele carried by the parents’ common ancestor. The Inbreeding Coefficient measures the probability of this event and increases with increasing relatedness between the parents. It is traditionally computed from the observed Inbreeding loops in the genealogies and its accuracy thus depends on the depth and reliability of genealogies. With the availability of genome-wide genetic data, it has become possible to compute a genome-based Inbreeding Coefficient f and different methods have been developed to estimate f and identify inbred individuals in a sample from the observed patterns of homozygosity at markers. MethodsIn this paper, we performed simulations with known genealogies using different SNP panels with different levels of linkage disequilibrium (LD) to compare several estimators of f, including single-point estimates, methods based on the length of runs of homozygosity (ROHs) and different methods that use hidden Markov models (HMMs). We also compared the performances of some of these estimators to identify inbred individuals in a sample using either HMM likelihood ratio tests or an adapted version of ERSA software.ResultsSingle-points methods were found to have higher standard deviations than other methods. ROHs give the best estimates provided the correct length threshold is known. HMM on sparse data gave equivalent or better results than HMM modeling LD. Provided LD is correctly accounted for, Inbreeding estimates were very similar using the different SNP panels. HMM likelihood ratio tests were found to perform better at detecting inbred individuals in a sample than the adapted ERSA. All methods accurately detected Inbreeding up to 2nd cousin offspring. We applied the best method on the release 3 of HapMap phase III project, found up to 4% of inbred individuals, and created HAP1067, an unrelated and outbred dataset of this release.ConclusionsWe recommend using HMMs on multiple sparse maps to estimate and detect Inbreeding on large samples. If the sample of individuals is too small to estimate allele frequencies, we advise to estimate them on reference panels or to use 1,500 kb ROHs. Finally, we suggest to investigators using HapMap to be careful with inbred individuals, especially in the GIH population.
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Estimation of the Inbreeding Coefficient through Use of Genomic Data
The American Journal of Human Genetics, 2003Co-Authors: Anne-louise Leutenegger, Bernard Prum, Arnaud Lemainque, Emmanuelle Genin, Françoise Clerget-darpoux, Christophe Verny, Elizabeth A ThompsonAbstract:Many linkage studies are performed in inbred populations, either small isolated populations or large populations with a long tradition of marriages between relatives. In such populations, there exist very complex genealogies with unknown loops. Therefore, the true Inbreeding Coefficient of an individual is often unknown. Good estimators of the Inbreeding Coefficient (f) are important, since it has been shown that underestimation of f may lead to false linkage conclusions. When an individual is genotyped for markers spanning the whole genome, it should be possible to use this genomic information to estimate that individual's f. To do so, we propose a maximum-likelihood method that takes marker dependencies into account through a hidden Markov model. This methodology also allows us to infer the full probability distribution of the identity-by-descent (IBD) status of the two alleles of an individual at each marker along the genome (posterior IBD probabilities) and provides a variance for the estimates. We simulate a full genome scan mimicking the true autosomal genome for (1) a first-cousin pedigree and (2) a quadruple-second-cousin pedigree. In both cases, we find that our method accurately estimates f for different marker maps. We also find that the proportion of genome IBD in an individual with a given genealogy is very variable. The approach is illustrated with data from a study of demyelinating autosomal recessive Charcot-Marie-Tooth disease.
Jinliang Wang - One of the best experts on this subject based on the ideXlab platform.
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Pedigrees or markers: Which are better in estimating relatedness and Inbreeding Coefficient?
Theoretical Population Biology, 2015Co-Authors: Jinliang WangAbstract:Individual Inbreeding Coefficient (F) and pairwise relatedness (r) are fundamental parameters in population genetics and have important applications in diverse fields such as human medicine, forensics, plant and animal breeding, conservation and evolutionary biology. Traditionally, both parameters are calculated from pedigrees, but are now increasingly estimated from genetic marker data. Conceptually, a pedigree gives the expected F and r values, FP and rP, with the expectations being taken (hypothetically) over an infinite number of individuals with the same pedigree. In contrast, markers give the realised (actual) F and r values at the particular marker loci of the particular individuals, FM and rM. Both pedigree (FP, rP) and marker (FM, rM) estimates can be used as inferences of genomic Inbreeding Coefficients FG and genomic relatedness rG, which are the underlying quantities relevant to most applications (such as estimating Inbreeding depression and heritability) of F and r. In the pre-genomic era, it was widely accepted that pedigrees are much better than markers in delineating FG and rG, and markers should better be used to validate, amend and construct pedigrees rather than to replace them. Is this still true in the genomic era when genome-wide dense SNPs are available? In this simulation study, I showed that genomic markers can yield much better estimates of FG and rG than pedigrees when they are numerous (say, 104 SNPs) under realistic situations (e.g. genome and population sizes). Pedigree estimates are especially poor for species with a small genome, where FG and rG are determined to a large extent by Mendelian segregations and may thus deviate substantially from their expectations (FP and rP). Simulations also confirmed that FM, when estimated from many SNPs, can be much more powerful than FP for detecting Inbreeding depression in viability. However, I argue that pedigrees cannot be replaced completely by genomic SNPs, because the former allows for the calculation of more complicated IBD Coefficients (involving more than 2 individuals, more than one locus, and more than 2 genes at a locus) for which the latter may have reduced capacity or limited power, and because the former has social and other significance for remote relationships which have little genetic significance and cannot be inferred reliably from markers.
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Effect of Excluding Sib Matings on Inbreeding Coefficient and Effective Size of Finite Diploid Populations
Biometrics, 1997Co-Authors: Jinliang WangAbstract:SUMMARY The effects of excluding full-sib matings, half-sib matings, or both under random selection or equal family size selection on the Inbreeding Coefficient and effective size in finite populations with unequal sex ratio have been studied. Recurrent equations for the Inbreeding Coefficient and approximate formulas for effective size are derived for different breeding systems. It is shown that avoidance of sib matings results in lower Inbreeding Coefficients in any generations under random selection. Under equal family size selection, however, excluding sib matings gives rise to lower Inbreeding Coefficients only in the first few generations and will eventually result in a higher Inbreeding in later generations compared with random mating. Exclusion of sib matings increases effective size under random selection while it decreases effective size under equal family size selection. The relative effectiveness on effective size of different sib mating avoidance depends only on sex ratio of the population. The importance of full-sib mating decreases and that of half-sib mating increases with the increment of the value of sex ratio (r). When r = 3, full-sib mating has the same effect on effective size as half-sib mating.
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Inbreeding Coefficient and effective size for an X-linked locus in nonrandom mating populations
Heredity, 1996Co-Authors: Jinliang WangAbstract:Formulae are given for equilibrium Inbreeding Coefficients for an X-linked locus in infinite populations under partial full-sib, half-sib and parent-offspring matings, respectively. An exact recurrence equation for the Inbreeding Coefficient for an X-linked locus is derived for a finite population with equal numbers of males and females under partial full-sib mating. Following the approach of variance of change in gene frequency, two general equations for effective size for an X-linked locus are obtained. The equations consider an arbitrary distribution of family size, unequal numbers of males and females and nonrandom mating. For some special cases, the equations reduce to the simple expressions derived by previous authors. Comparisons are made between expressions for effective size for an X-linked locus and those for an autosomal locus. Some interesting conclusions are drawn from the analysis and discussed.
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Exact Inbreeding Coefficient and effective size of finite populations under partial sib mating.
Genetics, 1995Co-Authors: Jinliang WangAbstract:An exact recurrence equation for Inbreeding Coefficient is derived for a partially sib-mated population of N individuals mated in N/2 pairs. From the equation, a formula for effective size (Ne) taking second order terms of 1/N into consideration is derived. When the family sizes are Poisson or equally distributed, the formula reduces to Ne = [(4 - 3 beta) N/(4 - 2 beta)] + 1 or Ne = [(4 - 3 beta) N/(2 - 2 beta)] - 8/(4 - 3 beta), approximately. For the special case of sib-mating exclusion and Poisson distribution of family size, the formula simplifies to Ne = N + 1, which differs from the previous results derived by many authors by a value of one. Stochastic simulations are run to check our results where disagreements with others are involved.
Nobuhiko Taniguchi - One of the best experts on this subject based on the ideXlab platform.
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genetic variability and pedigree tracing of a hatchery reared stock of red sea bream pagrus major used for stock enhancement based on microsatellite dna markers
Aquaculture, 1999Co-Authors: Ricardo Perezenriquez, Motohiro Takagi, Nobuhiko TaniguchiAbstract:Abstract Stock enhancement programs that use a small number of breeders for the production of hatchery-reared juveniles to be released to the environment, may have negative effects on the genetic diversity of wild populations due to a reduced genetic variability of the released stock. This study compared the genetic diversity of a hatchery-reared stock of red sea bream ( Pagrus major ) used for stock enhancement with that of their broodstock. Its pedigree was also traced, using four to five microsatellite DNA markers, to quantify the actual number of reproducing parents. Then, the effective number of contributing parents ( N e ) and the Inbreeding Coefficient were estimated. It was found that the genetic diversity of the hatchery-reared stock in terms of the mean observed heterozygosity ( H o =0.856), was not significant different ( P >0.05) than that of the broodstock ( H o =0.841). However, significant differences ( P N e was N e =63.7, consequently the estimated Inbreeding Coefficient was less than 0.8%. The results provide no evidence to consider a loss of genetic variation of the hatchery-reared stock, and a discussion on the possible effects of its release is presented.
Emmanuelle Genin - One of the best experts on this subject based on the ideXlab platform.
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high level of Inbreeding in final phase of 1000 genomes project
Scientific Reports, 2015Co-Authors: Steven Gazal, Emmanuelle Genin, Anne-louise Leutenegger, Mourad Sahbatou, Marieclaude BabronAbstract:The 1000 Genomes Project provides a unique source of whole genome sequencing data for studies of human population genetics and human diseases. The last release of this project includes more than 2,500 sequenced individuals from 26 populations. Although relationships among individuals have been investigated in some of the populations, Inbreeding has never been studied. In this article, we estimated the genomic Inbreeding Coefficient of each individual and found an unexpected high level of Inbreeding in 1000 Genomes data: nearly a quarter of the individuals were inbred and around 4% of them had Inbreeding Coefficients similar or greater than the ones expected for first-cousin offspring. Inbred individuals were found in each of the 26 populations, with some populations showing proportions of inbred individuals above 50%. We also detected 227 previously unreported pairs of close relatives (up to and including first-cousins). Thus, we propose subsets of unrelated and outbred individuals, for use by the scientific community. In addition, because admixed populations are present in the 1000 Genomes Project, we performed simulations to study the robustness of Inbreeding Coefficient estimates in the presence of admixture. We found that our multi-point approach (FSuite) was quite robust to admixture, unlike single-point methods (PLINK).
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Inbreeding Coefficient estimation with dense SNP data: comparison of strategies and application to HapMap III.
Human Heredity, 2014Co-Authors: Steven Gazal, Emmanuelle Genin, Mourad Sahbatou, Hervé Perdry, Sébastien Letort, Anne-louise LeuteneggerAbstract:Background/Aims: If the parents of an individual are related, it is possible for the individual to have received at 1 locus 2 identical-by-descent alleles that are copies of a single allele carried by the parents' common ancestor. The Inbreeding Coefficient measures the probability of this event and increases with increasing relatedness between the parents. It is traditionally computed from the observed Inbreeding loops in the genealogies and its accuracy thus depends on the depth and reliability of the genealogies. With the availability of genome-wide genetic data, it has become possible to compute a genome-based Inbreeding Coefficient f, and different methods have been developed to estimate f and identify inbred individuals in a sample from the observed patterns of homozygosity at markers. Methods: For this paper, we performed simulations with known genealogies using different SNP panels with different levels of linkage disequilibrium (LD) to compare several estimators of f, including single-point estimates, methods based on the length of runs of homozygosity (ROHs) and different methods that use hidden Markov models (HMMs). We also compared the performances of some of these estimators to identify inbred individuals in a sample using either HMM likelihood ratio tests or an adapted version of the ERSA software. Results: Single-point methods were found to have higher standard deviations than other methods. ROHs gave the best estimates provided the correct length threshold is known. HMMs on sparse data gave equivalent or better results than HMMs modeling LD. Provided LD is correctly accounted for, the Inbreeding estimates were very similar using the different SNP panels. The HMM likelihood ratio tests were found to perform better at detecting inbred individuals in a sample than the adapted ERSA. All methods accurately detected Inbreeding up to second-cousin offspring. We applied the best method on release 3 of the HapMap phase III project, found up to 4% of inbred individuals, and created HAP1067, an unrelated and outbred dataset of this release. Conclusions: We recommend using HMMs on multiple sparse maps to estimate and detect Inbreeding in large samples. If the sample of individuals is too small to estimate allele frequencies, we advise to estimate them on reference panels or to use 1,500-kb ROHs. Finally, we suggest to investigators using HapMap to be careful with inbred individuals, especially in the GIH (Gujarati Indians from Houston in Texas) population.
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Inbreeding Coefficient Estimation with Dense SNP Data: Comparison of Strategies and Application to HapMap III
Human Heredity, 2014Co-Authors: Steven Gazal, Emmanuelle Genin, Mourad Sahbatou, Hervé Perdry, Sébastien Letort, Anne-louise LeuteneggerAbstract:Background/AimsIf the parents of an individual are related, it is possible for the individual to have received at a locus two identical by descent (IBD) alleles that are copies of a single allele carried by the parents’ common ancestor. The Inbreeding Coefficient measures the probability of this event and increases with increasing relatedness between the parents. It is traditionally computed from the observed Inbreeding loops in the genealogies and its accuracy thus depends on the depth and reliability of genealogies. With the availability of genome-wide genetic data, it has become possible to compute a genome-based Inbreeding Coefficient f and different methods have been developed to estimate f and identify inbred individuals in a sample from the observed patterns of homozygosity at markers. MethodsIn this paper, we performed simulations with known genealogies using different SNP panels with different levels of linkage disequilibrium (LD) to compare several estimators of f, including single-point estimates, methods based on the length of runs of homozygosity (ROHs) and different methods that use hidden Markov models (HMMs). We also compared the performances of some of these estimators to identify inbred individuals in a sample using either HMM likelihood ratio tests or an adapted version of ERSA software.ResultsSingle-points methods were found to have higher standard deviations than other methods. ROHs give the best estimates provided the correct length threshold is known. HMM on sparse data gave equivalent or better results than HMM modeling LD. Provided LD is correctly accounted for, Inbreeding estimates were very similar using the different SNP panels. HMM likelihood ratio tests were found to perform better at detecting inbred individuals in a sample than the adapted ERSA. All methods accurately detected Inbreeding up to 2nd cousin offspring. We applied the best method on the release 3 of HapMap phase III project, found up to 4% of inbred individuals, and created HAP1067, an unrelated and outbred dataset of this release.ConclusionsWe recommend using HMMs on multiple sparse maps to estimate and detect Inbreeding on large samples. If the sample of individuals is too small to estimate allele frequencies, we advise to estimate them on reference panels or to use 1,500 kb ROHs. Finally, we suggest to investigators using HapMap to be careful with inbred individuals, especially in the GIH population.
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Estimation of the Inbreeding Coefficient through Use of Genomic Data
The American Journal of Human Genetics, 2003Co-Authors: Anne-louise Leutenegger, Bernard Prum, Arnaud Lemainque, Emmanuelle Genin, Françoise Clerget-darpoux, Christophe Verny, Elizabeth A ThompsonAbstract:Many linkage studies are performed in inbred populations, either small isolated populations or large populations with a long tradition of marriages between relatives. In such populations, there exist very complex genealogies with unknown loops. Therefore, the true Inbreeding Coefficient of an individual is often unknown. Good estimators of the Inbreeding Coefficient (f) are important, since it has been shown that underestimation of f may lead to false linkage conclusions. When an individual is genotyped for markers spanning the whole genome, it should be possible to use this genomic information to estimate that individual's f. To do so, we propose a maximum-likelihood method that takes marker dependencies into account through a hidden Markov model. This methodology also allows us to infer the full probability distribution of the identity-by-descent (IBD) status of the two alleles of an individual at each marker along the genome (posterior IBD probabilities) and provides a variance for the estimates. We simulate a full genome scan mimicking the true autosomal genome for (1) a first-cousin pedigree and (2) a quadruple-second-cousin pedigree. In both cases, we find that our method accurately estimates f for different marker maps. We also find that the proportion of genome IBD in an individual with a given genealogy is very variable. The approach is illustrated with data from a study of demyelinating autosomal recessive Charcot-Marie-Tooth disease.
G C French - One of the best experts on this subject based on the ideXlab platform.
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the relationship between flower size Inbreeding Coefficient and inferred selfing rate in british euphrasia species
Heredity, 2005Co-Authors: A J Silverside, G C French, Richard A Ennos, Peter M HollingsworthAbstract:The relationship between flower size, Inbreeding Coefficient and inferred selfing rate in British Euphrasia species
