Montgomery Slatkin

Finding the missing heritability of complex diseases

Genome-wide association studies have identified hundreds of genetic variants associated with complex human diseases and traits, and have provided valuable insights into their genetic architecture. Most variants identified so far confer relatively small increments in risk, and explain only a small proportion of familial clustering, leading many to question how the remaining, ‘missing’ heritability can be explained. Here we examine potential sources of missing heritability and propose research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.

Isolation by distance in equilibrium and non-equilibrium populations

It is shown that for allele frequency data a useful measure of the extent of gene flow between a pair of populations is M∘=(1/FST‐1)/4 , which is the estimated level of gene flow in an island model at equilibrium. For DNA sequence data, the same formula can be used if FST is replaced by NST. In a population with restricted dispersal, analytic theory shows that there is a simple relationship between M̂ and geographic distance in both equilibrium and non‐equilibrium populations and that this relationship is approximately independent of mutation rate when the mutation rate is small. Simulation results show that with reasonable sample sizes, isolation by distance can indeed be detected and that, at least in some cases, non‐equilibrium patterns can be distinguished. This approach to analyzing isolation by distance is used for two allozyme data sets, one from gulls and one from pocket gophers.

A draft sequence of the Neandertal genome.

Kissing Cousins Neandertals, our closest relatives, ranged across Europe and Southwest Asia before their extinction approximately 30,000 years ago. Green et al. (p. 710) report a draft sequence of the Neandertal genome, created from three individuals, and compare it with genomes of five modern humans. The results suggest that ancient genomes of human relatives can be recovered with acceptably low contamination from modern human DNA. Because ancient DNA can be contaminated with microbial DNA, Burbano et al. (p. 723) developed a target sequence capture approach to obtain 14 kilobases of Neandertal DNA from a fairly poorly preserved sample with a high microbial load. A number of genomic regions and genes were revealed as candidates for positive selection early in modern human history. The genomic data suggest that Neandertals mixed with modern human ancestors some 120,000 years ago, leaving traces of Neandertal DNA in contemporary humans. Gene flow has occurred from Neandertals to humans of Eurasian descent, but not to Africans. Neandertals, the closest evolutionary relatives of present-day humans, lived in large parts of Europe and western Asia before disappearing 30,000 years ago. We present a draft sequence of the Neandertal genome composed of more than 4 billion nucleotides from three individuals. Comparisons of the Neandertal genome to the genomes of five present-day humans from different parts of the world identify a number of genomic regions that may have been affected by positive selection in ancestral modern humans, including genes involved in metabolism and in cognitive and skeletal development. We show that Neandertals shared more genetic variants with present-day humans in Eurasia than with present-day humans in sub-Saharan Africa, suggesting that gene flow from Neandertals into the ancestors of non-Africans occurred before the divergence of Eurasian groups from each other.

A comparison of three indirect methods for estimating average levels of gene flow

Three methods for estimating the average level of gene flow in natural population are discussed and compared. The three methods are FST, rare alleles, and maximum likelihood. All three methods yield estimates of the combination of parameters (the number of migrants [Nm] in a demic model or the neighborhood size [4πDσ2] in a continuum model) that determines the relative importance of gene flow and genetic drift. We review the theory underlying these methods and derive new analytic results for the expectation of FST in stepping‐stone and continuum models when small sets of samples are taken. We also compare the effectiveness of the different methods using a variety of simulated data. We found that the FST and rare‐alleles methods yield comparable estimates under a wide variety of conditions when the population being sampled is demographically stable. They are roughly equally sensitive to selection and to variation in population structure, and they approach their equilibrium values at approximately the same rate. We found that two different maximum‐likelihood methods tend to yield biased estimates when relatively small numbers of locations are sampled but more accurate estimates when larger numbers are sampled. Our conclusion is that, although FST and rare‐alleles methods are expected to be equally effective in analyzing ideal data, practical problems in estimating the frequencies of rare alleles in electrophoretic studies suggest that FST is likely to be more useful under realistic conditions.

RARE ALLELES AS INDICATORS OF GENE FLOW

A. method for estimating the average level of gene flow in a subdivided population, as measured by the average number of migrants exchanged between local populations, Nm, is presented. The results from a computer simulation model show that the logarithm of Nm is approximately linearly related to the logarithm of the average frequency of private alleles, p̄(1), in a sample of alleles from the population. It is shown that this result is relatively insensitive to changes in parameters of the model other than Nm and the number of individuals sampled per population. The dependence of the value of p̄(1) on the numbers of individuals sampled provides a rough way to correct for differences in sample size.

Genetic history of an archaic hominin group from Denisova Cave in Siberia

Using DNA extracted from a finger bone found in Denisova Cave in southern Siberia, we have sequenced the genome of an archaic hominin to about 1.9-fold coverage. This individual is from a group that shares a common origin with Neanderthals. This population was not involved in the putative gene flow from Neanderthals into Eurasians; however, the data suggest that it contributed 4–6% of its genetic material to the genomes of present-day Melanesians. We designate this hominin population ‘Denisovans’ and suggest that it may have been widespread in Asia during the Late Pleistocene epoch. A tooth found in Denisova Cave carries a mitochondrial genome highly similar to that of the finger bone. This tooth shares no derived morphological features with Neanderthals or modern humans, further indicating that Denisovans have an evolutionary history distinct from Neanderthals and modern humans.

A High-Coverage Genome Sequence from an Archaic Denisovan Individual

Ancient Genomics The Denisovans were archaic humans closely related to Neandertals, whose populations overlapped with the ancestors of modern-day humans. Using a single-stranded library preparation method, Meyer et al. (p. 222, published online 30 August) provide a detailed analysis of a high-quality Denisovan genome. The genomic sequence provides evidence for very low rates of heterozygosity in the Denisova, probably not because of recent inbreeding, but instead because of a small population size. The genome sequence also illuminates the relationships between humans and archaics, including Neandertals, and establishes a catalog of genetic changes within the human lineage. A close-up look provides clues to the relationships between modern humans, Denisovans, and Neandertals. We present a DNA library preparation method that has allowed us to reconstruct a high-coverage (30×) genome sequence of a Denisovan, an extinct relative of Neandertals. The quality of this genome allows a direct estimation of Denisovan heterozygosity indicating that genetic diversity in these archaic hominins was extremely low. It also allows tentative dating of the specimen on the basis of “missing evolution” in its genome, detailed measurements of Denisovan and Neandertal admixture into present-day human populations, and the generation of a near-complete catalog of genetic changes that swept to high frequency in modern humans since their divergence from Denisovans.

The complete genome sequence of a Neanderthal from the Altai Mountains

We present a high-quality genome sequence of a Neanderthal woman from Siberia. We show that her parents were related at the level of half-siblings and that mating among close relatives was common among her recent ancestors. We also sequenced the genome of a Neanderthal from the Caucasus to low coverage. An analysis of the relationships and population history of available archaic genomes and 25 present-day human genomes shows that several gene flow events occurred among Neanderthals, Denisovans and early modern humans, possibly including gene flow into Denisovans from an unknown archaic group. Thus, interbreeding, albeit of low magnitude, occurred among many hominin groups in the Late Pleistocene. In addition, the high-quality Neanderthal genome allows us to establish a definitive list of substitutions that became fixed in modern humans after their separation from the ancestors of Neanderthals and Denisovans.

Testing for linkage disequilibrium in genotypic data using the Expectation-Maximization algorithm

We generalize an approach suggested by Hill (Heredity, 33, 229–239, 1974) for testing for significant association among alleles at two loci when only genotype and not haplotype frequencies are available. The principle is to use the Expectation-Maximization (EM) algorithm to resolve double heterozygotes into haplotypes and then apply a likelihood ratio test in order to determine whether the resolutions of haplotypes are significantly nonrandom, which is equivalent to testing whether there is statistically significant linkage disequilibrium between loci. The EM algorithm in this case relies on the assumption that genotype frequencies at each locus are in Hardy-Weinberg proportions. This method can accommodate X-linked loci and samples from haplodiploid species. We use three methods for testing the significance of the likelihood ratio: the empirical distribution in a large number of randomized data sets, the χ2 approximation for the distribution of likelihood ratios, and the Z2 test. The performance of each method is evaluated by applying it to simulated data sets and comparing the tail probability with the tail probability from Fishers exact test applied to the actual haplotype data. For realistic sample sizes (50–150 individuals) all three methods perform well with two or three alleles per locus, but only the empirical distribution is adequate when there are five to eight alleles per locus, as is typical of hypervariable loci such as microsatellites. The method is applied to a data set of 32 microsatellite loci in a Finnish population and the results confirm the theoretical predictions. We conclude that with highly polymorphic loci, the EM algorithm does lead to a useful test for linkage disequilibrium, but that it is necessary to find the empirical distribution of likelihood ratios in order to perform a test of significance correctly.

Gene flow and genetic drift in a species subject to frequent local extinctions

Abstract Two models of the effect of extinction and recolonization on the genetic differentiation of local populations are analyzed. One model is Wrights “island model” in which there is gene flow from a source of fixed gene frequency. The other is an island model with a continuous production of new alleles and gene flow among all the populations. Individual and group selection are not considered. It is shown that the extent of population differentiation and the direction of the effect of the colonization and extinction process depend on the manner in which the propagules that establish new colonies are formed. Two extreme cases are considered. In the “propagule pool” model all the individuals in a single propagule are derived from one population while in the “migrant pool” model, the individuals in a propagule are derived from a random sample of the entire collection of populations.