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BackgroundVariation in the prevalence of Huntington’s disease (HD) is associated with differences in chromosome 4 haplogroup proportions between populations. No data have been published on mutant HTT (mHTT) repeat stability in different haplogroups.AimTo study intergenerational CAG repeat stability in different mHTT haplotypes.MethodsA previously identified Finnish cohort of 207 HD patients and data on mHTT repeat lengths obtained from the diagnostic laboratories were used as a basis to identify parent-offspring pairs using several national registries. DNA remaining from diagnostic testing was analysed to determine haplogroups defined by the SNPs rs762855 and rs4690073. Haplogroup A haplotypes were further defined using four additional SNPs, rs2857936, rs363096, rs2276881 and rs362307. (Warby et al. 2009) The SNPs were determined either with restriction fragment analysis, allele specific amplification using locked nucleic acid primers or by sequencing.ResultsmHTT haplogroup and CAG repeat could be phased in 49 transmissions (haplogroup A, 38; haplogroup C, 10; other haplogroup, 1). The mean change in the length of CAG repeats differed between haplogroups A and C in paternal inheritances (p=0.038), but not in maternal inheritances (p=0.17). The change in haplogroup C was negative in paternal as well as maternal transmissions (p=0.74 for difference; figure 1), whereas the repeats in haplogroup A expanded in paternal transmissions in comparison to maternal transmissions (p=0.008). The difference was most obvious in haplotype A1 inheritances (p=0.022).Abstract C07 Figure 1The mean change (%) in the length of CAG repeats in haplogroups A and C in paternal and maternal transmissions[Figure omitted. See PDF]ConclusionsIntergenerational stability of the CAG repeat differed between mHTT haplogroups in a sex-dependent manner.

Lin D, Feng R, Chen J (2010) Maximum Likelihood Methods for Assessing Genetic Imprinting Using Case-Control Mother-Child Pair Data. Citation: Feng R, Wu Y, Jang GH, Ordovas JM, Arnett D (2012) Correction: A Powerful Test of Parent-of-Origin Effects for Quantitative Traits Using Haplotypes.

The S locus, a single polymorphic locus, is responsible for self-incompatibility (SI) in the Brassicaceae family and many related plant families. Despite its importance, our knowledge of S-locus evolution is largely restricted to the causal genes encoding the S-locus receptor kinase (SRK) receptor and S-locus cysteine-rich protein (SCR) ligand of the SI system. Here, we present high-quality sequences of the genomic region of six S-locus haplotypes: Arabidopsis (Arabidopsis thaliana; one haplotype), Arabidopsis lyrata (four haplotypes), and Capsella rubella (one haplotype). We compared these with reference S-locus haplotypes of the self-compatible Arabidopsis and its SI congener A. lyrata. We subsequently reconstructed the likely genomic organization of the S locus in the most recent common ancestor of Arabidopsis and Capsella. As previously reported, the two SI-determining genes, SCR and SRK, showed a pattern of coevolution. In addition, consistent with previous studies, we found that duplication, gene conversion, and positive selection have been important factors in the evolution of these two genes and appear to contribute to the generation of new recognition specificities. Intriguingly, the inactive pseudo-S-locus haplotype in the self-compatible species C. rubella is likely to be an old S-locus haplotype that only very recently became fixed when C. rubella split off from its SI ancestor, Capsella grandiflora.

Summary Genome phasing is a recently developed assembly method that separates heterozygous eukaryotic genomic regions and builds haplotype‐resolved assemblies. Because differences between haplotypes are ignored in most published de novo genomes, assemblies are available as consensus genomes consisting of haplotype mixtures, thus increasing the need for genome phasing. Here, we review the operating principles and characteristics of several freely available and widely used phasing tools (TrioCanu, FALCON‐Phase, and ALLHiC). An examination of downstream analyses using haplotype‐resolved genome assemblies in plants indicated significant differences among haplotypes regarding chromosomal rearrangements, sequence insertions, and expression of specific alleles that contribute to the acquisition of the biological characteristics of plant species. Finally, we suggest directions to solve addressing limitations of current genome‐phasing methods. This review provides insights into the current progress, limitations, and future directions of de novo genome phasing, which will enable researchers to easily access and utilize genome‐phasing in studies involving highly heterozygous complex plant genomes.

Signatures of positive selection in the genome are a characteristic mark of adaptation that can reveal an ongoing, recent, or ancient response to environmental change throughout the evolution of a population. New sources of food, climate conditions, and exposure to pathogens are only some of the possible sources of selective pressure, and the rise of advantageous genetic variants is a crucial determinant of survival and reproduction. In this context, the ability to detect these signatures of selection may pinpoint genetic variants that are responsible for a significant change in gene regulation, gene expression, or protein synthesis, structure, and function. This review focuses on statistical methods that take advantage of linkage disequilibrium and haplotype determination to reveal signatures of positive selection in whole-genome sequencing data, showing that they emerge from different descriptions of the same underlying event. Moreover, considerations are provided around the application of these statistics to different species, their suitability for ancient DNA, and the usefulness of discovering variants under selection for biomedicine and public health in an evolutionary medicine framework.

Routine haplotype-resolved genome assembly from single samples remains an unresolved problem. Here we describe an algorithm that combines PacBio HiFi reads and Hi-C chromatin interaction data to produce a haplotype-resolved assembly without the sequencing of parents. Applied to human and other vertebrate samples, our algorithm consistently outperforms existing single-sample assembly pipelines and generates assemblies of similar quality to the best pedigree-based assemblies. Haplotype-resolved genome assemblies are generated by combining HiFi reads with Hi-C long-range interactions.

Pangenome references address biases of reference genomes by storing a representative set of diverse haplotypes and their alignment, usually as a graph. Alternate alleles determined by variant callers can be used to construct pangenome graphs, but advances in long-read sequencing are leading to widely available, high-quality phased assemblies. Constructing a pangenome graph directly from assemblies, as opposed to variant calls, leverages the graph’s ability to represent variation at different scales. Here we present the Minigraph-Cactus pangenome pipeline, which creates pangenomes directly from whole-genome alignments, and demonstrate its ability to scale to 90 human haplotypes from the Human Pangenome Reference Consortium. The method builds graphs containing all forms of genetic variation while allowing use of current mapping and genotyping tools. We measure the effect of the quality and completeness of reference genomes used for analysis within the pangenomes and show that using the CHM13 reference from the Telomere-to-Telomere Consortium improves the accuracy of our methods. We also demonstrate construction of a Drosophila melanogaster pangenome. Constructing genome graphs directly from genome assemblies overcomes single-reference bias.

Fine-mapping to plausible causal variation may be more effective in multi-ancestry cohorts, particularly in the MHC, which has population-specific structure. To enable such studies, we constructed a large ( n  = 21,546) HLA reference panel spanning five global populations based on whole-genome sequences. Despite population-specific long-range haplotypes, we demonstrated accurate imputation at G-group resolution (94.2%, 93.7%, 97.8% and 93.7% in admixed African (AA), East Asian (EAS), European (EUR) and Latino (LAT) populations). Applying HLA imputation to genome-wide association study data for HIV-1 viral load in three populations (EUR, AA and LAT), we obviated effects of previously reported associations from population-specific HIV studies and discovered a novel association at position 156 in HLA-B. We pinpointed the MHC association to three amino acid positions (97, 67 and 156) marking three consecutive pockets (C, B and D) within the HLA-B peptide-binding groove, explaining 12.9% of trait variance. A high-resolution reference panel based on whole-genome sequencing data enables accurate imputation of HLA alleles across diverse populations and fine-mapping of HLA association signals for HIV-1 host response.