Ancestral haplotype reconstruction in endogamous populations using identity-by-descent

In this work we develop a novel algorithm for reconstructing the genomes of ancestral individuals, given genotype or sequence data from contemporary individuals and an extended pedigree of family relationships. A pedigree with complete genomes for every individual enables the study of allele frequency dynamics and haplotype diversity across generations, including deviations from neutrality such as transmission distortion. When studying heritable diseases, ancestral haplotypes can be used to augment genome-wide association studies and track disease inheritance patterns. The building blocks of our reconstruction algorithm are segments of Identity-By-Descent (IBD) shared between two or more genotyped individuals. The method alternates between identifying a source for each IBD segment and assembling IBD segments placed within each ancestral individual. Unlike previous approaches, our method is able to accommodate complex pedigree structures with hundreds of individuals genotyped at millions of SNPs.We apply our method to an Old Order Amish pedigree from Lancaster, Pennsylvania, whose founders came to North America from Europe during the early 18th century. The pedigree includes 1338 individuals from the past 12 generations, 394 with genotype data. The motivation for reconstruction is to understand the genetic basis of diseases segregating in the family through tracking haplotype transmission over time. Using our algorithm thread, we are able to reconstruct an average of 224 ancestral individuals per chromosome. For these ancestral individuals, on average we reconstruct 79% of their haplotypes. We also identify a region on chromosome 16 that is difficult to reconstruct—we find that this region harbors a short Amish-specific copy number variation and the gene HYDIN. thread was developed for endogamous populations, but can be applied to any extensive pedigree with the recent generations genotyped. We anticipate that this type of practical ancestral reconstruction will become more common and necessary to understand rare and complex heritable diseases in extended families.     

Efficiency gain of marker-assisted backcrossing by sequentially increasing marker densities over generations

Expenses for marker assays are the major costs in marker-assisted backcrossing programs for the transfer of target genes from a donor into the genetic background of a recipient genotype. Our objectives were to (1) investigate the effect of employing sequentially increasing marker densities over backcross generations on the recurrent parent genome (RPG) recovery and the number of marker data points (MDP) required, and (2) determine optimum designs for attaining RPG thresholds of 93–98% with a minimum number of MDP. We simulated the introgression of one dominant target gene for genome models of sugar beet (Beta vulgaris L.) and maize (Zea mays L.) with varying marker distances of 5–80 cM and population sizes of 30–250 plants across BC₁ to BC₃ generations. Employing less dense maps in early backcross generations resulted in savings of over 50% in the number of required MDP compared with using a constant set of markers and was accompanied only by small reductions in the attained RPG values. The optimum designs were characterized by increasing marker densities and increasing population sizes in advanced generations for both genome models. We conclude that increasing simultaneously the marker density and the population size from early to advanced backcross generations results in gene introgression with a minimum number of required MDP.     

Strategies for efficient implementation of molecular markers in wheat breeding

Although molecular markers allow more accurate selection in early generations than conventional screens, large numbers can make selection impracticable while screening in later generations may provide little or no advantage over conventional selection techniques. Investigation of different crossing strategies and consideration of when to screen, what proportion to retain and the impacts of dominant vs. codominant marker expression revealed important choices in the design of marker-assisted selection programs that can produce large efficiency gains. Using F₂ enrichment increased the frequency of selected alleles allowing large reductions in minimum population size for recovery of target genotypes (commonly around 90%) and/or selection at a greater number of loci. Increasing homozygosity by inbreeding from F₂ to F_2:3 also reduced population size by around 90% in some crosses with smaller incremental reductions in subsequent generations. Backcrossing was found to be a useful strategy to reduce population size compared with a biparental population where one parent contributed more target alleles than the other and was complementary to F₂ enrichment and increasing homozygosity. Codominant markers removed the need for progeny testing reducing the number of individuals that had to be screened to identify a target genotype. However, although codominant markers allow target alleles to be fixed in early generations, minimum population sizes are often so large in F₂ that it is not efficient to do so at this stage. Formulae and tables for calculating genotypic frequencies and minimum population sizes are provided to allow extension to different breeding systems, numbers of target loci, and probabilities of failure. Principles outlined are applicable to implementation of markers for both quantitative trait loci (QTL) and major genes.     

Historical Pedigree Reconstruction from Extant Populations Using PArtitioning of RElatives (PREPARE)

Recent technological improvements in the field of genetic data extraction give rise to the possibility of reconstructing the historical pedigrees of entire populations from the genotypes of individuals living today. Current methods are still not practical for real data scenarios as they have limited accuracy and assume unrealistic assumptions of monogamy and synchronized generations. In order to address these issues, we develop a new method for pedigree reconstruction, , which is based on formulations of the pedigree reconstruction problem as variants of graph coloring. The new formulation allows us to consider features that were overlooked by previous methods, resulting in a reconstruction of up to 5 generations back in time, with an order of magnitude improvement of false-negatives rates over the state of the art, while keeping a lower level of false positive rates. We demonstrate the accuracy of compared to previous approaches using simulation studies over a range of population sizes, including inbred and outbred populations, monogamous and polygamous mating patterns, as well as synchronous and asynchronous mating.     

Efficiency of the use of pedigree and molecular marker information in conservation programs

The value of molecular markers and pedigree records, separately or in combination, to assist in the management of conserved populations has been tested. The general strategy for managing the population was to optimize contributions of parents to the next generation for minimizing the global weighted coancestry. Strategies differed in the type of information used to compute global coancestries, the number and type of evaluated individuals, and the system of mating. Genealogical information proved to be very useful (at least for 10 generations of management) to arrange individuals' contributions via the minimization of global coancestry. In fact, the level of expected heterozygosity after 10 generations yielded by this strategy was 88-100% of the maximum possible improvement obtained if the genotype for all loci was known. Marker information was of very limited value if used alone. The amount and degree of polymorphism of markers to be used to compute molecular coancestry had to be high to mimic the performance of the strategy relying on pedigree, especially in the short term (for example, >10 markers per chromosome with 10 alleles each were needed if only the parents' genotype was available). When both sources of information are combined to calculate the coancestry conditional on markers, clear increases in effective population size (Ne) were found, but observed diversity levels (either gene or allelic diversity) in the early generations were quite similar to the ones obtained with pedigree alone. The advantage of including molecular information is greater when information is available on a greater number of individuals (offspring and parents vs. parents only). However, for realistic situations (i.e., large genomes) the benefits of using information on offspring are small. The same conclusions were reached when comparing the use of the different types of information (genealogical or/and molecular) to perform minimum coancestry matings.     

Modelling problems in conservation genetics using Drosophila: consequences of fluctuating population sizes

Many natural populations fluctuate widely in population size. This is predicted to reduce effective population size, genetic variation, and reproductive fitness, and to increase inbreeding. The effects of fluctuating population size were examined in small populations of Drosophila melanogaster of the same average size, but maintained using either fluctuating ( FPS ) or equal ( EPS ) population sizes.FPS lines were maintained using seven pairs and one pair in alternate generations, and EPS lines with four pairs per generation. Ten replicates of each treatment were maintained. After eight generations, FPS had a higher inbreeding coefficient than EPS (0.60 vs. 0.38), a lower average allozyme heterozygosity (0.068 vs. 0.131), and a much lower relative fitness (0.03 vs. 0.25). Estimates of effective population sizes for FPS and EPS were 3.8 and 7.9 from pedigree inbreeding, and 4.9 vs. 7.1 from changes in average heterozygosities, as compared to theoretical expectations of 3.3 vs. 8.0. Results were generally in accordance with theoretical predictions. Management strategies for populations of rare and endangered species should aim to minimize population fluctuations over generations.     

Development and testing of a genetic marker‐based pedigree reconstruction system ‘PR‐genie’ incorporating size‐class data

For wildlife populations, it is often difficult to determine biological parameters that indicate breeding patterns and population mixing, but knowledge of these parameters is essential for effective management. A pedigree encodes the relationship between individuals and can provide insight into the dynamics of a population over its recent history. Here, we present a method for the reconstruction of pedigrees for wild populations of animals that live long enough to breed multiple times over their lifetime and that have complex or unknown generational structures. Reconstruction was based on microsatellite genotype data along with ancillary biological information: sex and observed body size class as an indicator of relative age of individuals within the population. Using body size‐class data to infer relative age has not been considered previously in wildlife genealogy and provides a marked improvement in accuracy of pedigree reconstruction. Body size‐class data are particularly useful for wild populations because it is much easier to collect noninvasively than absolute age data. This new pedigree reconstruction system, PR‐genie, performs reconstruction using maximum likelihood with optimization driven by the cross‐entropy method. We demonstrated pedigree reconstruction performance on simulated populations (comparing reconstructed pedigrees to known true pedigrees) over a wide range of population parameters and under assortative and intergenerational mating schema. Reconstruction accuracy increased with the presence of size‐class data and as the amount and quality of genetic data increased. We provide recommendations as to the amount and quality of data necessary to provide insight into detailed familial relationships in a wildlife population using this pedigree reconstruction technique.     

Gene survival in the Asian wild horse (Equus przewalskii): II. Gene survival in the whole population,in subgroups,and through history

In populations with a known pedigree, exact joint probability distributions of numbers of surviving of genes from each founder can now be calculated for moderately large complex pedigrees (1,000–2,000 individuals and much inbreeding). The usefulness of such calculations is shown by our analysis of gene survival in the Asian wild horse (Equus przewalskii), a species now extinet in the wild with a captive population with 1,516 individuals in the known pedigree (12 generations). We calculate the genetic diversity of subsets of the current population interesting to the North American Species Survival Plan, trace the loss of genetic diversity in this species through its history in captivity, and determine genetically important individuals in the North American population—those with relatively high probabilities of having unique copy genes (genes not found in any other living individual in North America).     

The relation between reproductive value and genetic contribution

What determines the genetic contribution that an individual makes to future generations? With biparental reproduction, each individual leaves a “pedigree” of descendants, determined by the biparental relationships in the population. The pedigree of an individual constrains the lines of descent of each of its genes. An individual’s reproductive value is the expected number of copies of each of its genes that is passed on to distant generations conditional on its pedigree. For the simplest model of biparental reproduction (analogous to the Wright–Fisher model), an individual’s reproductive value is determined within ∼10 generations, independent of population size. Partial selfing and subdivision do not greatly slow this convergence. Our central result is that the probability that a gene will survive is proportional to the reproductive value of the individual that carries it and that, conditional on survival, after a few tens of generations, the distribution of the number of surviving copies is the same for all individuals, whatever their reproductive value. These results can be generalized to the joint distribution of surviving blocks of the ancestral genome. Selection on unlinked loci in the genetic background may greatly increase the variance in reproductive value, but the above results nevertheless still hold. The almost linear relationship between survival probability and reproductive value also holds for weakly favored alleles. Thus, the influence of the complex pedigree of descendants on an individual’s genetic contribution to the population can be summarized through a single number: its reproductive value.THE most obvious feature of sexual reproduction is that each individual has two parents. Yet, the pedigrees that describe biparental relationships have received surprisingly little attention, compared with the genealogies that describe the uniparental relationships of genes. (Throughout, we refer to relationships between genes as their “genealogy”, in contrast to the “pedigree” of biparental relationships; genealogy should be understood as a shorthand for “gene genealogy”.) Following the rediscovery of Mendelian genetics, attention focused on the random genetic drift of discrete alleles and on the converse process of inbreeding, by which genes become identical by descent. There has of course been substantial work on the fate of genes within a given pedigree (e.g., Smith 1976, Cannings et al. 1978; Thompson et al. 1978), but relatively little on the pedigrees themselves.Pedigrees are of interest in their own right: it is natural to ask who our ancestors were (Chang 1999; Rohde et al. 2004) and, conversely, how many descendants we will each leave. But, from a genetic point of view, the pedigree constrains what genes can be passed on: with Mendelian inheritance, selection acts solely through the different contributions made by individuals to the pedigree. The recent availability of genomic sequences may focus more attention on pedigrees: given sufficient sequence, we can infer the pedigree many generations back; and given this pedigree, we can ask what contribution is likely to be made to future generations by each ancestral genome. These questions are long standing (Thompson et al. 1978; Thompson 1979a, b), but it has become feasible to answer them only in the past few years (Huff et al. 2011).The notion of reproductive value was introduced by Fisher (1930) to study populations structured by age. The reproductive value of an individual of a given age is its expected future contribution to the population (conditional on having survived to that age). Caswell (1982) generalized this to populations with an arbitrary structure (for example, where individuals vary in size or microhabitat). Grafen (2006) emphasizes that reproductive value can be ascribed to individuals as well as classes and shows rigorously that reproductive value is the target of selection. In the long term, alleles that increase the reproductive value will be the ones that increase, and traits will evolve that tend to maximize an individual’s reproductive value. In this setting, an individual’s reproductive value is defined to be its expected genetic contribution, that is, the expected number of copies of one of its alleles that it leaves in distant future generations, conditional on its pedigree of descendants. Once a pedigree is specified, one can superpose the passage of neutral alleles: offspring, independently, sample one allele from each parent. In this way an individual’s reproductive value is defined to be a function of its pedigree. Thus, we structure the population by the pedigree that connects every individual, rather than with a coarser structure by age or class.An individual’s reproductive value is determined within ∼10 generations, whereas its ultimate genetic contribution is determined over very long timescales. Here, we examine the relationship between pedigrees and genealogies over intermediate timescales of a few tens of generations.It is crucial to realize that overall genetic contribution to future generations is much more complex than simply the reproductive value, which gives the expected contribution at any one locus. The key result of this article is that the reproductive value of an individual determines the survival probability of its genes, but conditional on survival, the distribution of the number of copies of an allele in future generations is the same for all individuals, independent of their reproductive value. This result applies to a single genetic locus. Most of an individual ancestor’s genome is lost, but some small blocks survive in large numbers (Baird et al. 2003). By investigating simple summary statistics of the distribution of surviving blocks, we illustrate that the influence of the pedigree on the whole complex distribution of genetic contribution of an individual is also determined by its reproductive value. Thus, over these intermediate timescales, from the point of view of allele frequencies, the tangled web of relationships that forms an individual’s pedigree can be completely captured in a single number: the reproductive value.     

Island-Model Genomic Selection for Long-Term Genetic Improvement of Autogamous Crops

Acceleration of genetic improvement of autogamous crops such as wheat and rice is necessary to increase cereal production in response to the global food crisis. Population and pedigree methods of breeding, which are based on inbred line selection, are used commonly in the genetic improvement of autogamous crops. These methods, however, produce a few novel combinations of genes in a breeding population. Recurrent selection promotes recombination among genes and produces novel combinations of genes in a breeding population, but it requires inaccurate single-plant evaluation for selection. Genomic selection (GS), which can predict genetic potential of individuals based on their marker genotype, might have high reliability of single-plant evaluation and might be effective in recurrent selection. To evaluate the efficiency of recurrent selection with GS, we conducted simulations using real marker genotype data of rice cultivars. Additionally, we introduced the concept of an “island model” inspired by evolutionary algorithms that might be useful to maintain genetic variation through the breeding process. We conducted GS simulations using real marker genotype data of rice cultivars to evaluate the efficiency of recurrent selection and the island model in an autogamous species. Results demonstrated the importance of producing novel combinations of genes through recurrent selection. An initial population derived from admixture of multiple bi-parental crosses showed larger genetic gains than a population derived from a single bi-parental cross in whole cycles, suggesting the importance of genetic variation in an initial population. The island-model GS better maintained genetic improvement in later generations than the other GS methods, suggesting that the island-model GS can utilize genetic variation in breeding and can retain alleles with small effects in the breeding population. The island-model GS will become a new breeding method that enhances the potential of genomic selection in autogamous crops, especially bringing long-term improvement.     

Long-term response to genomic selection: effects of estimation method and reference population structure for different genetic architectures

Background

Genomic selection has become an important tool in the genetic improvement of animals and plants. The objective of this study was to investigate the impacts of breeding value estimation method, reference population structure, and trait genetic architecture, on long-term response to genomic selection without updating marker effects.

Methods

Three methods were used to estimate genomic breeding values: a BLUP method with relationships estimated from genome-wide markers (GBLUP), a Bayesian method, and a partial least squares regression method (PLSR). A shallow (individuals from one generation) or deep reference population (individuals from five generations) was used with each method. The effects of the different selection approaches were compared under four different genetic architectures for the trait under selection. Selection was based on one of the three genomic breeding values, on pedigree BLUP breeding values, or performed at random. Selection continued for ten generations.

Results

Differences in long-term selection response were small. For a genetic architecture with a very small number of three to four quantitative trait loci (QTL), the Bayesian method achieved a response that was 0.05 to 0.1 genetic standard deviation higher than other methods in generation 10. For genetic architectures with approximately 30 to 300 QTL, PLSR (shallow reference) or GBLUP (deep reference) had an average advantage of 0.2 genetic standard deviation over the Bayesian method in generation 10. GBLUP resulted in 0.6% and 0.9% less inbreeding than PLSR and BM and on average a one third smaller reduction of genetic variance. Responses in early generations were greater with the shallow reference population while long-term response was not affected by reference population structure.

Conclusions

The ranking of estimation methods was different with than without selection. Under selection, applying GBLUP led to lower inbreeding and a smaller reduction of genetic variance while a similar response to selection was achieved. The reference population structure had a limited effect on long-term accuracy and response. Use of a shallow reference population, most closely related to the selection candidates, gave early benefits while in later generations, when marker effects were not updated, the estimation of marker effects based on a deeper reference population did not pay off.     

Genomic BLUP Decoded: A Look into the Black Box of Genomic Prediction

Genomic best linear unbiased prediction (BLUP) is a statistical method that uses relationships between individuals calculated from single-nucleotide polymorphisms (SNPs) to capture relationships at quantitative trait loci (QTL). We show that genomic BLUP exploits not only linkage disequilibrium (LD) and additive-genetic relationships, but also cosegregation to capture relationships at QTL. Simulations were used to study the contributions of those types of information to accuracy of genomic estimated breeding values (GEBVs), their persistence over generations without retraining, and their effect on the correlation of GEBVs within families. We show that accuracy of GEBVs based on additive-genetic relationships can decline with increasing training data size and speculate that modeling polygenic effects via pedigree relationships jointly with genomic breeding values using Bayesian methods may prevent that decline. Cosegregation information from half sibs contributes little to accuracy of GEBVs in current dairy cattle breeding schemes but from full sibs it contributes considerably to accuracy within family in corn breeding. Cosegregation information also declines with increasing training data size, and its persistence over generations is lower than that of LD, suggesting the need to model LD and cosegregation explicitly. The correlation between GEBVs within families depends largely on additive-genetic relationship information, which is determined by the effective number of SNPs and training data size. As genomic BLUP cannot capture short-range LD information well, we recommend Bayesian methods with t-distributed priors.     

Power of the joint segregation analysis method for testing mixed major-gene and polygene inheritance models of quantitative traits

Understanding the genetic architecture of quantitative traits can greatly assist the design of strategies for their manipulation in plant-breeding programs. For a number of traits, genetic variation can be the result of segregation of a few major genes and many polygenes (minor genes). The joint segregation analysis (JSA) is a maximum-likelihood approach for fitting segregation models through the simultaneous use of phenotypic information from multiple generations. Our objective in this paper was to use computer simulation to quantify the power of the JSA method for testing the mixed-inheritance model for quantitative traits when it was applied to the six basic generations: both parents (P₁ and P₂), F₁, F₂, and both backcross generations (B₁ and B₂) derived from crossing the F₁ to each parent. A total of 1968 genetic model-experiment scenarios were considered in the simulation study to quantify the power of the method. Factors that interacted to influence the power of the JSA method to correctly detect genetic models were: (1) whether there were one or two major genes in combination with polygenes, (2) the heritability of the major genes and polygenes, (3) the level of dispersion of the major genes and polygenes between the two parents, and (4) the number of individuals examined in each generation (population size). The greatest levels of power were observed for the genetic models defined with simple inheritance; e.g., the power was greater than 90% for the one major gene model, regardless of the population size and major-gene heritability. Lower levels of power were observed for the genetic models with complex inheritance (major genes and polygenes), low heritability, small population sizes and a large dispersion of favourable genes among the two parents; e.g., the power was less than 5% for the two major-gene model with a heritability value of 0.3 and population sizes of 100 individuals. The JSA methodology was then applied to a previously studied sorghum data-set to investigate the genetic control of the putative drought resistance-trait osmotic adjustment in three crosses. The previous study concluded that there were two major genes segregating for osmotic adjustment in the three crosses. Application of the JSA method resulted in a change in the proposed genetic model. The presence of the two major genes was confirmed with the addition of an unspecified number of polygenes. Received: 18 August 2000 / Accepted: 9 March 2001     

A study of contemporary levels and temporal trends in inbreeding in the Tangier Island, Virginia, population using pedigree data and isonymy

In this study we describe inbreeding in a large pedigree from Tangier Island, Virginia, in which we compare two commonly used methods to estimate inbreeding in humans: pedigree and isonymy (identical surnames of spouses). Genealogical data on 3,512 individuals dating back to 1722 were used. Using the pedigree method, we determined an average inbreeding coefficient (F) of 0.00873 for the community as a whole, and 0.018 for inbred individuals. Analysis of temporal trends showed that inbreeding began around 1800 and peaked at 0.0109 in 1824-1849 and 1875-1899. Thereafter, inbreeding steadily declined to 0.00565 in 1975-1997. Analysis of pedigree structure complexity over time showed that close consanguinity contributes to inbreeding in the earlier cohorts, and remote consanguinity accounts for much of the inbreeding in the later cohorts. The number of common ancestors increases over time, as does the number of paths connecting inbred individuals to these common ancestors. Inbreeding estimates based on the isonymy approach yielded a 2.2-fold higher value of F (0.01945) compared to the pedigree method. Total isonymy estimates over 25-year cohorts overestimated inbreeding values from pedigree data between 1. 5-8-fold. We speculate that the overestimation is probably due to the inability of our data to satisfy the method's assumption of monophyletic origin of each surname. In conclusion, inbreeding in the Tangier Island population is consistent with the isolated nature of its population, and temporal trends reflect patterns in emigration and a breakdown in isolation over time.     

Imputation of missing genotypes from sparse to high density using long-range phasing

Related individuals share potentially long chromosome segments that trace to a common ancestor. We describe a phasing algorithm (ChromoPhase) that utilizes this characteristic of finite populations to phase large sections of a chromosome. In addition to phasing, our method imputes missing genotypes in individuals genotyped at lower marker density when more densely genotyped relatives are available. ChromoPhase uses a pedigree to collect an individual's (the proband) surrogate parents and offspring and uses genotypic similarity to identify its genomic surrogates. The algorithm then cycles through the relatives and genomic surrogates one at a time to find shared chromosome segments. Once a segment has been identified, any missing information in the proband is filled in with information from the relative. We tested ChromoPhase in a simulated population consisting of 400 individuals at a marker density of 1500/M, which is approximately equivalent to a 50K bovine single nucleotide polymorphism chip. In simulated data, 99.9% loci were correctly phased and, when imputing from 100 to 1500 markers, more than 87% of missing genotypes were correctly imputed. Performance increased when the number of generations available in the pedigree increased, but was reduced when the sparse genotype contained fewer loci. However, in simulated data, ChromoPhase correctly imputed at least 12% more genotypes than fastPHASE, depending on sparse marker density. We also tested the algorithm in a real Holstein cattle data set to impute 50K genotypes in animals with a sparse 3K genotype. In these data 92% of genotypes were correctly imputed in animals with a genotyped sire. We evaluated the accuracy of genomic predictions with the dense, sparse, and imputed simulated data sets and show that the reduction in genomic evaluation accuracy is modest even with imperfectly imputed genotype data. Our results demonstrate that imputation of missing genotypes, and potentially full genome sequence, using long-range phasing is feasible.     

Estimation of the number of individuals founding colonized populations

A method for estimating the number of founding chromosomes in an isolated population is introduced. The method assumes that n/2 diploid individuals are sampled from a population and that alleles are identified at L unlinked loci. The population is assumed to have been founded T generations in the past by individuals carrying c chromosomes drawn randomly from a known source population, which has also been sampled. If c is small and the population grew rapidly after it was founded, accurate estimates of c can be obtained and those estimates are not sensitive to details of the history of population sizes. If c is larger or the population remained small after it was founded, then estimates of c depend on the history of population sizes. We test the performance of our method on simulated data and demonstrate its use on data from a rainbow trout (Oncorhynchus mykiss) population.     

Heritability of interpack aggression in a wild pedigreed population of North American grey wolves

Aggression is a quantitative trait deeply entwined with individual fitness. Mapping the genomic architecture underlying such traits is complicated by complex inheritance patterns, social structure, pedigree information and gene pleiotropy. Here, we leveraged the pedigree of a reintroduced population of grey wolves (Canis lupus) in Yellowstone National Park, Wyoming, USA, to examine the heritability of and the genetic variation associated with aggression. Since their reintroduction, many ecological and behavioural aspects have been documented, providing unmatched records of aggressive behaviour across multiple generations of a wild population of wolves. Using a linear mixed model, a robust genetic relationship matrix, 12,288 single nucleotide polymorphisms (SNPs) and 111 wolves, we estimated the SNP‐based heritability of aggression to be 37% and an additional 14% of the phenotypic variation explained by shared environmental exposures. We identified 598 SNP genotypes from 425 grey wolves to resolve a consensus pedigree that was included in a heritability analysis of 141 individuals with SNP genotype, metadata and aggression data. The pedigree‐based heritability estimate for aggression is 14%, and an additional 16% of the phenotypic variation was explained by shared environmental exposures. We find strong effects of breeding status and relative pack size on aggression. Through an integrative approach, these results provide a framework for understanding the genetic architecture of a complex trait that influences individual fitness, with linkages to reproduction, in a social carnivore. Along with a few other studies, we show here the incredible utility of a pedigreed natural population for dissecting a complex, fitness‐related behavioural trait.     

Genetic population structure and neighbourhood population size estimates of the caddisfly Plectrocnemia conspersa

1. We used both genetic and ecological methods to evaluate the role of history and the scale of colonisation in structuring populations of the caddisfly Plectrocnemia conspersa. There was no genetic differentiation between sites up to 20 km apart, despite population sizes suggesting that genetic drift could create substantial variation at this scale. 2. Genetic differentiation between populations separated by more than 20 km was greater than expected given the contrasting short‐range trend, and implied a neighbourhood population size that is implausibly small. Therefore, the evolutionary processes that affect the short‐range trend do not explain differentiation over greater distances. 3. At small scales (<20 km), relatively short flights by winged adults spread over a number of generations could account for the spread of genes. For instance, dispersing individuals could found small (often temporary) populations, which may then grow and exchange genes with larger and more permanent local populations, amplifying the effects of the initial gene flow. 4. Over larger scales (20–500 km), substantial gaps between regions containing suitable habitat patches could reduce the number of colonisation events. Genetic patterns at this scale may date from the time they were last colonised. Previous ecological studies have rarely examined the dynamics of aquatic insect populations over these larger geographical scales, yet these processes may be central to their persistence and spread.     

Estimation of genotype error rate using samples with pedigree information--an application on the GeneChip Mapping 10K array

Currently, most analytical methods assume all observed genotypes are correct; however, it is clear that errors may reduce statistical power or bias inference in genetic studies. We propose procedures for estimating error rate in genetic analysis and apply them to study the GeneChip Mapping 10K array, which is a technology that has recently become available and allows researchers to survey over 10,000 SNPs in a single assay. We employed a strategy to estimate the genotype error rate in pedigree data. First, the "dose-response" reference curve between error rate and the observable error number were derived by simulation, conditional on given pedigree structures and genotypes. Second, the error rate was estimated by calibrating the number of observed errors in real data to the reference curve. We evaluated the performance of this method by simulation study and applied it to a data set of 30 pedigrees genotyped using the GeneChip Mapping 10K array. This method performed favorably in all scenarios we surveyed. The dose-response reference curve was monotone and almost linear with a large slope. The method was able to estimate accurately the error rate under various pedigree structures and error models and under heterogeneous error rates. Using this method, we found that the average genotyping error rate of the GeneChip Mapping 10K array was about 0.1%. Our method provides a quick and unbiased solution to address the genotype error rate in pedigree data. It behaves well in a wide range of settings and can be easily applied in other genetic projects. The robust estimation of genotyping error rate allows us to estimate power and sample size and conduct unbiased genetic tests. The GeneChip Mapping 10K array has a low overall error rate, which is consistent with the results obtained from alternative genotyping assays.     

Mitochondrial and nuclear genetic contribution of female founders to a contemporary population in northeast Quebec.

A common challenge in population genetics is to reconstruct the evolutionary history of populations on the basis of current allele frequencies. Through pedigree analysis, we have the opportunity to study the genetic contribution of founders to the contemporary population. This contribution over many generations accounts for the probable introduction, survival, and extinction of genes in the population. I use this method to follow nuclear and mitochondrial genes in the Saguenay population of northeast Quebec by tracing back ascending genealogies of 160,315 individuals born between 1950 and 1971 by using the BALSAC database. This study leads us to conclude that even in a growing population, the loss rate of mtDNA is high. The survival of mtDNA in the population is independent of the time of introduction in the population. The number of copies of a particular mtDNA gene in the contemporary population is higher for genes introduced earlier, but the correlation between these two variables is low (the relation is not linear). Compared to nuclear contribution, mitochondrial contribution is higher, but the loss rate of nuclear DNA is lower. The differential contribution (the fact that few founders contribute a lot) is the same proportion for nuclear and mtDNA, but only 592 female founders contribute 50% of the mtDNA gene pool of the contemporary cohort, compared to 994 for nuclear DNA. Since we have no molecular data on founders' haplotypes, these results cannot give us the diversity level in the population. However, this study enables us to compare the evolutionary fates of nuclear and mitochondrial genes in this expanding population.