The ancestral recombination graph (ARG) contains the full genealogical information of the sample, and many population genetic inference problems can be solved using inferred or sampled ARGs. In particular, the waiting distance between tree changes along the genome can be used to make inference about the distribution and evolution of recombination rates. To this end, we here derive an analytic expression for the distribution of waiting distances between tree changes under the sequentially Markovian coalescent model and obtain an accurate approximation to the distribution of waiting distances for topology changes. We use these results to show that some of the recently proposed methods for inferring sequences of trees along the genome provide strongly biased distributions of waiting distances. In addition, we provide a correction to an undercounting problem facing all available ARG inference methods, thereby facilitating the use of ARG inference methods to estimate temporal changes in the recombination rate.

The ancestral recombination graph is a structure that describes the joint genealogies of sampled DNA sequences along the genome. Recent computational methods have made impressive progress toward scalably estimating whole-genome genealogies. In addition to inferring the ancestral recombination graph, some of these methods can also provide ancestral recombination graphs sampled from a defined posterior distribution. Obtaining good samples of ancestral recombination graphs is crucial for quantifying statistical uncertainty and for estimating population genetic parameters such as effective population size, mutation rate, and allele age. Here, we use standard neutral coalescent simulations to benchmark the estimates of pairwise coalescence times from 3 popular ancestral recombination graph inference programs: ARGweaver, Relate, and tsinfer+tsdate. We compare (1) the true coalescence times to the inferred times at each locus; (2) the distribution of coalescence times across all loci to the expected exponential distribution; (3) whether the sampled coalescence times have the properties expected of a valid posterior distribution. We find that inferred coalescence times at each locus are most accurate in ARGweaver, and often more accurate in Relate than in tsinfer+tsdate. However, all 3 methods tend to overestimate small coalescence times and underestimate large ones. Lastly, the posterior distribution of ARGweaver is closer to the expected posterior distribution than Relate's, but this higher accuracy comes at a substantial trade-off in scalability. The best choice of method will depend on the number and length of input sequences and on the goal of downstream analyses, and we provide guidelines for the best practices.

Phylogenetic models of molecular evolution are central to numerous biological applications spanning diverse timescales, from hundreds of millions of years involving orthologous proteins to just tens of days relating to single cells within an organism. A fundamental problem in these applications is estimating model parameters, for which maximum likelihood estimation is typically employed. Unfortunately, maximum likelihood estimation is a computationally expensive task, in some cases prohibitively so. To address this challenge, we here introduce CherryML, a broadly applicable method that achieves several orders of magnitude speedup by using a quantized composite likelihood over cherries in the trees. The massive speedup offered by our method should enable researchers to consider more complex and biologically realistic models than previously possible. Here we demonstrate CherryMLs utility by applying it to estimate a general 400 × 400 rate matrix for residue-residue coevolution at contact sites in three-dimensional protein structures; we estimate that using current state-of-the-art methods such as the expectation-maximization algorithm for the same task would take >100,000 times longer.

Systemic lupus erythematosus (SLE) is a prototypic systemic autoimmune disorder. Considerable progress has been made to delineate the genetic control of this complex disorder. In this review, selected aspects of human and mouse genetics related to SLE are reviewed with emphasis on genes that contribute to both innate and adaptive immunity and to genes that contribute directly to susceptibility to end organ damage. It is concluded that the interactions among these two major pathways will provide further insight into the pathogenesis of SLE. An interactive model of the two major pathways is proposed without emphasis on the importance of breaking tolerance to autoantigens.

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease with a strong genetic component characterized by autoantibody production and a type I interferon signature. Here we report a missense variant (g.74779296G>A; p.Arg90His) in NCF1, encoding the p47^phox subunit of the phagocyte NADPH oxidase (NOX2), as the putative underlying causal variant that drives a strong SLE-associated signal detected by the Immunochip in the GTF2IRD1-GTF2I region at 7q11.23 with a complex genomic structure. We show that the p.Arg90His substitution, which is reported to cause reduced reactive oxygen species (ROS) production, predisposes to SLE (odds ratio (OR) = 3.47 in Asians (P_meta = 3.1 × 10^-104), OR = 2.61 in European Americans, OR = 2.02 in African Americans) and other autoimmune diseases, including primary Sjögren's syndrome (OR = 2.45 in Chinese, OR = 2.35 in European Americans) and rheumatoid arthritis (OR = 1.65 in Koreans). Additionally, decreased and increased copy numbers of NCF1 predispose to and protect against SLE, respectively. Our data highlight the pathogenic role of reduced NOX2-derived ROS levels in autoimmune diseases.

Objective

The increased risk of thrombosis in systemic lupus erythematosus (SLE) may be partially explained by interrelated genetic pathways for thrombosis and SLE. The present study was undertaken to investigate whether 33 established and novel single-nucleotide polymorphisms (SNPs) in 20 genes involved in hemostasis pathways that have been associated with deep venous thrombosis (DVT) in the general population are risk factors for SLE among Asian subjects.

Methods

Patients in the discovery cohort were enrolled in 1 of 2 North American SLE cohorts. Patients in the replication cohort were enrolled in 1 of 4 Asian or 2 North American cohorts. We first genotyped 263 Asian patients with SLE and 357 healthy Asian control subjects for 33 SNPs in the discovery phase, and then genotyped 5 SNPs in up to an additional 1,496 patients and 993 controls in the replication phase. Patients were compared to controls for bivariate association with minor alleles. Principal components analysis was used to control for intra-Asian ancestry in the replication cohort.

Results

Two genetic variants in the gene VKORC1 were highly significant in both the discovery and replication cohorts: rs9934438 (in the discovery cohort, odds ratio [OR] 2.45, P=2×10(-9); in the replication cohort, OR 1.54, P=4×10(-6)) and rs9923231 (in the discovery cohort, OR 2.40, P=6×10(-9); in the replication cohort, OR 1.53, P=5×10(-6)). These associations were significant in the replication cohort after adjustment for intra-Asian ancestry: for rs9934438, OR 1.34, P=0.0029; for rs9923231, OR 1.34, P=0.0032.

Conclusion

Genetic variants in VKORC1, which are involved in vitamin K reduction and associated with DVT, correlate with SLE development in Asian subjects. These results suggest that there may be intersecting genetic pathways for the development of SLE and thrombosis.