Ablation of insulin-producing cells prevents obesity but not premature mortality caused by a high-sugar diet in Drosophila

Ageing can be modulated by genetic as well as nutritional interventions. In female Drosophila melanogaster, lifespan is maximized at intermediate concentrations of sucrose as the carbohydrate source, and yeast as the protein source. Dampening the signal through the insulin/IGF signalling (IIS) pathway, by genetic ablation of median neurosecretory cells (mNSCs) that produce insulin-like peptides, extends lifespan and counteracts the detrimental effects of excess yeast. However, how IIS reduction impacts health on a high-sugar diet remains unclear. We find that, while the ablation of the mNSCs can extend lifespan and delay the age-related decline in the health of the neuromuscular system irrespective of the amount of dietary sugar, it cannot rescue the lifespan-shortening effects of excess sugar. On the other hand, ablation of mNSCs can prevent adult obesity resulting from excess sugar, and this effect appears independent from the canonical effector of IIS, dfoxo. Our study indicates that while treatments that reduce IIS have anti-ageing effects irrespective of dietary sugar, additional interventions may be required to achieve full benefits in humans, where excessive sugar consumption is a growing problem. At the same time, pathways regulated by IIS may be suitable targets for treatment of obesity.     

The Sampling Distribution of Linkage Disequilibrium under an Infinite Allele Model without Selection

The sampling distributions of several statistics that measure the association of alleles on gametes (linkage disequilibrium) are estimated under a two-locus neutral infinite allele model using an efficient Monte Carlo method. An often used approximation for the mean squared linkage disequilibrium is shown to be inaccurate unless the proper statistical conditioning is used. The joint distribution of linkage disequilibrium and the allele frequencies in the sample is studied. This estimated joint distribution is sufficient for obtaining an approximate maximum likelihood estimate of C = 4Nc, where N is the population size and c is the recombination rate. It has been suggested that observations of high linkage disequilibrium might be a good basis for rejecting a neutral model in favor of a model in which natural selection maintains genetic variation. It is found that a single sample of chromosomes, examined at two loci cannot provide sufficient information for such a test if C less than 10, because with C this small, very high levels of linkage disequilibrium are not unexpected under the neutral model. In samples of size 50, it is found that, even when C is as large as 50, the distribution of linkage disequilibrium conditional on the allele frequencies is substantially different from the distribution when there is no linkage between the loci. When conditioned on the number of alleles at each locus in the sample, all of the sample statistics examined are nearly independent of theta = 4N mu, where mu is the neutral mutation rate.     

Expected behavior of conditional linkage disequilibrium.

The ubiquitousness of RFLPs in the human genome has greatly helped the mapping of human disease genes, and it has been suggested that population measures of association between disease and marker loci could help with this mapping. For rare diseases, random samples are taken from within disease genotypes in order to obtain reasonable sample sizes, but this sampling strategy requires a modification of the usual measures of association. We present theoretical predictions for the mean and variance of such a modified measure, under the assumption that the disease gene is maintained at a constant low frequency in the population. The coefficient of variation of this modified measure is large enough that caution is needed in using the measure to locate disease genes, and, furthermore, the coefficient of variation cannot be made arbitrarily small by increasing sample size. The modified association measure is calculated for recently published data on cystic fibrosis.     

Multilocus estimation of genetic structure within populations

Spatial structure of genetic variation within populations is well measured by statistics based on the distribution of pairs of individual genotypes, and various such statistics have been widely used in experimental studies. However, the problem of uncharacterized correlations among statistics for different alleles has limited the applications of multiallelic, multilocus summary measures, since these had unknown sampling distributions. Usually multiple alleles and/or multiple loci are required in order to precisely measure spatial structures, and to provide precise indirect estimates of the amount of dispersal in samples of reasonable size. This article examines the correlations among pair-wise statistics, including Moran I-statistics and various measures of conditional kinship, for different alleles of a locus. First the correlations are mathematically derived for random spatial distributions, which allow averages over alleles and loci to be used as more powerful yet exact test statistics for the null hypothesis. Then extensive computer simulations are conducted to examine the correlations among values for different alleles under isolation by distance processes. For loci with more than three alleles, the results show that the correlations are remarkably and perhaps surprisingly small, establishing the principle that then alleles behave as nearly independent realizations of space-time stochastic processes. The results also show that the correlations are largely robust with respect to the degree of spatial structure, and they can be used in a straightforward manner to form confidence intervals for averages. The results allow a precise connection between observations in experimental studies and levels of dispersal in theoretical models.     

hp‐rare 1.0: a computer program for performing rarefaction on measures of allelic richness

The number of alleles in a sample (allelic richness) is a fundamental measure of genetic diversity. However, this diversity measure has been difficult to use because large samples are expected to contain more alleles than small samples. The statistical technique of rarefaction compensates for this sampling disparity. Here I introduce a computer program that performs rarefaction on private alleles and hierarchical sampling designs.     

Estimating genotypes with independently sampled descent graphs

A method for estimating genotypic and identity-by-descent probabilities in complex pedigrees is described. The method consists of an algorithm for drawing independent genotype samples which are consistent with the pedigree and observed genotype. The probability distribution function for samples obtained using the algorithm can be evaluated up to a normalizing constant, and combined with the likelihood to produce a weight for each sample. Importance sampling is then used to estimate genotypic and identity-by-descent probabilities. On small but complex pedigrees, the genotypic probability estimates are demonstrated to be empirically unbiased. On large complex pedigrees, while the algorithm for obtaining genotype samples is feasible, importance sampling may require an infeasible number of samples to estimate genotypic probabilities with accuracy.     

Obtaining high-quality DNA from elusive small mammals using low-tech hair snares

Noninvasive sampling approaches are becoming increasingly important for enabling genetic studies of wildlife populations. While a number of methods have been described to noninvasively sample hair from carnivores and medium-sized mammals, they have largely remained untested in elusive small mammals. Here we describe a novel and inexpensive noninvasive hair snare targeted at an elusive small mammal, the American pika (Ochotona princeps). We explore the quality of the sample by assessing PCR amplification success of mitochondrial and nuclear DNA fragments across four commercially available DNA isolation kits and two different quantities of hair in a factorial design. Additionally, we determined the sex of the individual samples using PCR–RFLP of ZFX/ZFY loci. We found that our snare is effective in obtaining hair that yield DNA of sufficient quality and quantity to successfully amplify a range of mitochondrial and nuclear fragment sizes. Specifically, we found the greatest success in amplifying mitochondrial DNA, nuclear microsatellites and ZFX/ZFY loci using at least 25 hairs as starting material and the DNA IQ™ system. The hair snares thus provide a cost-effective and minimally intrusive approach to sample elusive or rare small mammals. We anticipate that this approach will be useful to obtain samples for molecular studies of the ecology, evolution and conservation of small, elusive mammals.     

Human population structure and its effects on sampling Y chromosome sequence variation

The excess of rare variants in global sequencing studies of the nonrecombining portion of the Y chromosome (NRY) has been interpreted as evidence for the effects of human demographic expansion. However, many NRY polymorphisms are geographically localized and the effect of different geographical sampling on patterns of NRY variation is unknown. We use two sampling designs to detect population structure and its effects on patterns of human NRY polymorphism. First, we sequence 26.5 kb of noncoding Y chromosome DNA from 92 globally distributed males representing 35 populations. We find that the number of polymorphisms with singleton variants is positively correlated with the number of populations sampled and that there is a significant negative correlation of Tajima's D (TD) and Fu and Li's D (FD) statistics with the number of pooled populations. We then sequence the same region in a total of 73 males sampled from 3 distinct populations and find that TD and FD values for the 3 pooled and individual population samples were much less negative than those in the aforementioned global sample. Coalescent simulations show that a simple splitting model of population structure, with no changes in population size, is sufficient to produce the negative values of TD seen in our pooled samples. These empirical and simulation results suggest that observed levels of NRY population structure may lead to an upward bias in the number of singleton variants in global surveys and call into question inferences of population expansion based on global sampling strategies.     

On the sampling variance of intraclass correlations and genetic correlations.

Widely used standard expressions for the sampling variance of intraclass correlations and genetic correlation coefficients were reviewed for small and large sample sizes. For the sampling variance of the intraclass correlation, it was shown by simulation that the commonly used expression, derived using a first-order Taylor series performs better than alternative expressions found in the literature, when the between-sire degrees of freedom were small. The expressions for the sampling variance of the genetic correlation are significantly biased for small sample sizes, in particular when the population values, or their estimates, are close to zero. It was shown, both analytically and by simulation, that this is because the estimate of the sampling variance becomes very large in these cases due to very small values of the denominator of the expressions. It was concluded, therefore, that for small samples, estimates of the heritabilities and genetic correlations should not be used in the expressions for the sampling variance of the genetic correlation. It was shown analytically that in cases where the population values of the heritabilities are known, using the estimated heritabilities rather than their true values to estimate the genetic correlation results in a lower sampling variance for the genetic correlation. Therefore, for large samples, estimates of heritabilities, and not their true values, should be used.     

Do polymorphic loci require large sample sizes to estimate genetic distances?

The coefficient of variation of estimates of three genetic distances (standard genetic distance of Nei, chord distance, FST) was examined with computer simulation to determine if large samples (per population) are necessary to precisely estimate genetic distances at loci with high levels of polymorphism. These simulations showed that loci with high mutation rates produce estimates of genetic distance with lower coefficients of variation than loci with lower mutation rates--without requiring larger sample sizes from each population. In addition, the rate at which increasing sample sizes decreases the coefficient of variation of estimates of genetic distances was shown to be approximately determined by the value of FST between the populations being sampled. When FST was greater than 0.05, sampling fewer than 20 individuals (per population) should be sufficient. When FST was less than 0.01, sampling 100 individuals (per population) or more will be useful.     

Joint linkage of multiple loci for a complex disorder.

Many investigators who have been searching for linkage to complex diseases have by now accumulated a drawer full of negative results. If disease is actually caused by genes at several loci, these data might contain multiple-locus system (MLS) information that the investigator does not realize. Trying to obtain this information formally, through the MLS likelihood, leads to severe computational and statistical difficulties. Therefore, we propose a scheme of inference based on single-locus (SL) statistics, considered jointly. By simulation, we find that the MLS lod score is closely approximated by the sum of SL lod scores. However, we also find that for moderately large systems, say three of four loci, both MLS and SL lod scores are likely to be inconclusive. Nonetheless, MLS can often be detected through the correlation of individual pedigree SL lod scores. Significant correlation is itself evidence of an MLS, because, in the absence of linkage, false-positive lod scores are necessarily random. Under epistasis SL lod scores tend to be positively correlated among pedigrees, while under independent action SL lod scores from high-density samples tend to be negatively correlated.     

Recombination and the frequency spectrum in Drosophila melanogaster and Drosophila simulans

Most "tests of neutrality" assess whether particular data sets depart from the predictions of a standard neutral model with no recombination. For Drosophila, where nuclear polymorphism data routinely show evidence of genetic exchange, the assumption of no recombination is often unrealistic. In addition, while conservative, this assumption is made at the cost of a great loss in power. Perhaps as a result, tests of the frequency spectrum based on zero recombination suggest an adequate fit of Drosophila polymorphism data to the predictions of the standard neutral model. Here, we analyze the frequency spectrum of a large number of loci in Drosophila melanogaster and D. simulans using two summary statistics. We use an estimate of the population recombination rate based on a laboratory estimate of the rate of crossing over per physical length and an estimate of the species' effective population size. In contrast to previous studies, we find that roughly half of the loci depart from the predictions of the standard neutral model. The extent of the departure depends on the exact recombination rate, but the global pattern that emerges is robust. Interestingly, these departures from neutral expectations are not unidirectional. The large variance in outcomes may be due to a complex demographic history and inconsistent sampling, or to the pervasive action of natural selection.     

DNA sampling from eggshell swabbing is widely applicable in wild bird populations as demonstrated in 23 species

There is increasing interest in noninvasive DNA sampling techniques. In birds, there are several methods proposed for sampling DNA, and of these, the use of eggshell swabbing is potentially applicable to a wide range of species. We estimated the effectiveness of this method in the wild by sampling the eggs of 23 bird species. Sampling of eggs was performed twice per nest, soon after the clutch was laid and again at the end of egg incubation. We genotyped DNA samples using a set of five conserved microsatellite markers, which included a Z-linked locus and a sex-typing marker. We successfully collected avian DNA from the eggs of all species tested and from 88.48% of the samples. In most of the cases, the DNA concentration was low (ca. 10 ng/μL). The number of microsatellite loci amplified per sample (0-5) was used as a measure of the genotyping success of the sample. On average, we genotyped 3.01 ± 0.12 loci per sample (mean ± SE), and time of sampling did not seem to have an effect; however, genotyping success differed among species and was greater in those species that used feather material for lining their nest cups. We also checked for the occurrence of possible genotyping errors derived from using samples with very low DNA quantities (i.e. allelic dropout or false alleles) and for DNA contamination from individuals other than the mother, which appeared at a moderate rate (in 44% of the PCR replicates and in 17.36% of samples, respectively). Additionally, we investigated whether the DNA on eggshells corresponded to maternal DNA by comparing the genotypes obtained from the eggshells to those obtained from blood samples of all the nestlings for six nests of magpies. In five of the six magpie nests, we found evidence that the swab genotypes were a mixture of genotypes from both parents and this finding was independent of the time of incubation. Thus, our results broadly confirm that the swabbing of eggshells can be used as a noninvasive method for obtaining DNA and is applicable across a wide range of bird species. Nonetheless, genotyping errors should be properly estimated for each species by using a suite of highly polymorphic loci. These errors may be resolved by sampling only recently laid eggs (to avoid non-maternal DNA contamination) or by performing several PCR replicates per sample (to avoid allelic dropout and false alleles) and/or by increasing the amount of DNA used in the PCR through increasing the volume of the PCR or increasing the concentration of template DNA.     

An Empirical Exploration of the (δμ)2 Genetic Distance for 213 Human Microsatellite Markers

Microsatellites are now used ubiquitously as genetic markers. One important application is to the assessment of population subdivision and phylogenetic relatedness. Such applications require a method of estimation of genetic distance. Here we examine the most widely used measure of microsatellite genetic distance, Goldstein et al.'s delta-mu squared ([delta mu]2), with respect to a large data set of 213 markers typed across samples from four diverse human populations. We find that (delta mu)2 yields plausible interpopulation distances. For the first time, we report significant interpopulation differences in mean microsatellite length, although the effect of these differences on (delta mu)2 is negligible. However, we also show that the method is extremely sensitive to one or two loci that contribute extreme values, even when a sample size of >200 loci is used. Some of these extreme loci can be removed on the grounds that some alleles carry large indels, but for others there is no clear justification for exclusion a priori. Our data suggest a rather recent African/non-African split, with an upper limit of some 70,000-80,000 years ago.     

Multiple modes in a coral species abundance distribution

Species abundance distributions are an important measure of biodiversity and community structure. These distributions are affected by sampling, and alternative species-abundance models often make similar predictions for small sample sizes. Very large samples reveal the relative abundances of rare species, and thus provide information about species relative abundances that small samples cannot. Here, we present the species-abundance distribution for a sample of > 40,000 coral colonies at a single site, exceeding existing samples of coral local assemblages by over an order of magnitude. This abundance distribution is multimodal when examined on a logarithmic scale. Four different model selection procedures all indicate that the underlying community abundance distribution has at least three modes. We show that the multiple modes are not caused by mixtures of species with different habitat preferences. However, spatial aggregation partially explains our results. We inspect published work on species abundance distributions, and suggest that multimodality may be a common feature of large samples.     

The design and analysis of case-control studies with biased sampling

A design is proposed for case-control studies in which selection of subjects for full variable ascertainment is based jointly on disease status and on easily obtained "screening" variables that may be related to the disease. Recruitment of subjects follows an independent Bernoulli sampling scheme, with recruitment probabilities set by the investigator in advance. In particular, the sampling can be set up to achieve, on average, frequency matching, provided prior estimates of the disease rates or odds ratios associated with screening variables such as age and sex are available. Alternatively--for example, when studying a rare exposure--one can enrich the sample with certain categories of subject. Following such a design, there are two valid approaches to logistic regression analysis, both of which allow for efficient estimation of effects associated with the screening variables that were allowed to bias the recruitment. The statistical properties of the estimators are compared, both for large samples, based on asymptotics, and for small samples, based on simulations.     

The estimator of the optimal measure of allelic association: mean, variance and probability distribution when the sample size tends to infinity

The allelic association or linkage disequilibrium between two loci is a parameter of fundamental interest in modern population genetics for evolutionary inference and association mapping studies. Among the many measures available, the optimal measure of allelic association rho presents a strong evolutionary theory basis and is modeled on the physical distance along the chromosome with the Malécot equation for isolation by distance. Moreover, rho is equal to the absolute value of D', the standardized measure of gametic disequilibrium. We studied here the statistical properties of the rho sample estimator. We derived its asymptotic probability distribution and showed that it is neither asymptotically normal nor unbiased when rho=0 or when allelic frequencies are equal at both loci, in contrast to previous claims. This asymptotic study leads to propose a new test for absence of linkage disequilibrium. We compared it to Pearson's Chi2 test for independence in a contingency table and showed by simulations that the range in power of these two tests depends on the sign of D'. The new test outperformed slightly the Chi2 test, when D', polarized with respect to major alleles, is negative. Finally, we derived the asymptotic bias and information of the rho estimator that are due to the experimental sampling and showed by simulation that its bias is large in small samples. The consequences of these findings on applications using the rho measure are then discussed in particular for constructing LD unit maps, and call for a revised statistical treatment.     

Sampling properties of the heterozygote-excess estimator of the effective number of breeders

The effective number of breeders (N _b) for a cohort of progeny can be estimated from an excess of heterozygotes that arises in progeny produced by finite numbers of parents. In principle, N _b is a simple function of the standardized deviation (D) of the proportion of heterozygous progeny from its expectation under random mating. We explored the sampling properties of this D-estimator of N _b through computer simulation. The accuracy of the D-estimator is remarkably robust to variation in numbers of alleles and loci and the presence of rare alleles, though precision can be low if, relative to a given N _b, the sample of progeny or the cumulative number of independent alleles (n _ci) sampled is too small. For N _b up to 30 parents, acceptable accuracy is achieved with sample sizes of 200 or more progeny and 80 or more independent alleles; for N _b of 50–100, a sample of 500–1,000 progeny and 450–900 independent alleles are required for similar accuracy and precision. Though the estimator is most applicable for the situation of random union of gametes (as may occur in some marine invertebrates or fish, for example), it works for other mating systems (monogamous or polygamous pairings, polygyny), when the effective number of breeders is small (N _b ≤ 20). Simulations reveal small overestimation biases with smaller sample sizes, rare alleles, or highly polymorphic loci (≥10 alleles). Despite this bias, multiallelic loci are preferable to many loci with few alleles, which have larger sampling errors.     

Clines, clusters, and the effect of study design on the inference of human population structure

Previously, we observed that without using prior information about individual sampling locations, a clustering algorithm applied to multilocus genotypes from worldwide human populations produced genetic clusters largely coincident with major geographic regions. It has been argued, however, that the degree of clustering is diminished by use of samples with greater uniformity in geographic distribution, and that the clusters we identified were a consequence of uneven sampling along genetic clines. Expanding our earlier dataset from 377 to 993 markers, we systematically examine the influence of several study design variables—sample size, number of loci, number of clusters, assumptions about correlations in allele frequencies across populations, and the geographic dispersion of the sample—on the “clusteredness” of individuals. With all other variables held constant, geographic dispersion is seen to have comparatively little effect on the degree of clustering. Examination of the relationship between genetic and geographic distance supports a view in which the clusters arise not as an artifact of the sampling scheme, but from small discontinuous jumps in genetic distance for most population pairs on opposite sides of geographic barriers, in comparison with genetic distance for pairs on the same side. Thus, analysis of the 993-locus dataset corroborates our earlier results: if enough markers are used with a sufficiently large worldwide sample, individuals can be partitioned into genetic clusters that match major geographic subdivisions of the globe, with some individuals from intermediate geographic locations having mixed membership in the clusters that correspond to neighboring regions.     

Population differences in the human functional olfactory repertoire

Olfactory receptors (OR) constitute the molecular basis for the sense of smell. They are encoded by a large multigene family that in humans includes approximately 400 functional genes and approximately 600 putative pseudogenes, distributed on all but two chromosomes. To examine the ethnogeographic variability in the functional chemosensory repertoire, we resequenced 32 OR loci reported to contain a single coding region disruption in seven Caucasians and seven Pygmies. Thirteen of the 32 OR loci were found to have an interrupted coding region in all 28 alleles sampled, seven had an intact form in all the individuals examined, and 12 were polymorphic, segregating both the intact and the null variants. Among the latter loci, the frequency of the null allele was higher in Caucasians than in Pygmies, suggesting that African populations may have a larger repertoire of functional OR genes. Interestingly, when analyzing the entire OR coding regions, we find an excess of high-frequency derived alleles at many loci in the Caucasian sample but less so in the Pygmy sample. Our observations are unlikely to be accounted for by simple demographic models but may be explained by positive selection acting on OR loci in Caucasians.