The accelerating rate at which DNA sequence data are now generated by high-throughput sequencing instruments provides both opportunities and challenges for population genetic and ecological investigations of animals and plants. We show here how the common practice of calling genotypes from a single SNP per sequenced region ignores substantial additional information in the phased short-read sequences that are provided by these sequencing instruments. We target sequenced regions with multiple SNPs in kelp rockfish (Sebastes atrovirens) to determine "microhaplotypes" and then call these microhaplotypes as alleles at each locus. We then demonstrate how these multi-allelic marker data from such loci dramatically increase power for relationship inference. The microhaplotype approach decreases false-positive rates by several orders of magnitude, relative to calling bi-allelic SNPs, for two challenging analytical procedures, full-sibling and single parent-offspring pair identification. We also show how the identification of half-sibling pairs requires so much data that physical linkage becomes a consideration, and that most published studies that attempt to do so are dramatically underpowered. The advent of phased short-read DNA sequence data, in conjunction with emerging analytical tools for their analysis, promises to improve efficiency by reducing the number of loci necessary for a particular level of statistical confidence, thereby lowering the cost of data collection and reducing the degree of physical linkage amongst markers used for relationship estimation. Such advances will facilitate collaborative research and management for migratory and other widespread species.

The little greenbul, a common rainforest passerine from sub-Saharan Africa, has been the subject of long-term evolutionary studies to understand the mechanisms leading to rainforest speciation. Previous research found morphological and behavioural divergence across rainforest-savannah transition zones (ecotones), and a pattern of divergence with gene flow suggesting divergent natural selection has contributed to adaptive divergence and ecotones could be important areas for rainforests speciation. Recent advances in genomics and environmental modelling make it possible to examine patterns of genetic divergence in a more comprehensive fashion. To assess the extent to which natural selection may drive patterns of differentiation, here we investigate patterns of genomic differentiation among populations across environmental gradients and regions. We find compelling evidence that individuals form discrete genetic clusters corresponding to distinctive environmental characteristics and habitat types. Pairwise F between populations in different habitats is significantly higher than within habitats, and this differentiation is greater than what is expected from geographic distance alone. Moreover, we identified 140 SNPs that showed extreme differentiation among populations through a genomewide selection scan. These outliers were significantly enriched in exonic and coding regions, suggesting their functional importance. Environmental association analysis of SNP variation indicates that several environmental variables, including temperature and elevation, play important roles in driving the pattern of genomic diversification. Results lend important new genomic evidence for environmental gradients being important in population differentiation.

We develop a computational framework for addressing pedigree inference problems using small numbers (80-400) of single nucleotide polymorphisms (SNPs). Our approach relaxes the assumptions, which are commonly made, that sampling is complete with respect to the pedigree and that there is no genotyping error. It relies on representing the inferred pedigree as a factor graph and invoking the Sum-Product algorithm to compute and store quantities that allow the joint probability of the data to be rapidly computed under a large class of rearrangements of the pedigree structure. This allows efficient MCMC sampling over the space of pedigrees, and, hence, Bayesian inference of pedigree structure. In this paper we restrict ourselves to inference of pedigrees without loops using SNPs assumed to be unlinked. We present the methodology in general for multigenerational inference, and we illustrate the method by applying it to the inference of full sibling groups in a large sample (n=1157) of Chinook salmon typed at 95 SNPs. The results show that our method provides a better point estimate and estimate of uncertainty than the currently best-available maximum-likelihood sibling reconstruction method. Extensions of this work to more complex scenarios are briefly discussed.