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Current approaches to detect and characterize mosaic chromosomal aneuploidy are limited by sensitivity, efficiency, cost, or the need to culture cells. We describe the mosaic aneuploidy detection by massively parallel sequencing (MAD-seq) capture assay and the MADSEQ analytical approach that allow low (<10%) levels of mosaicism for chromosomal aneuploidy or regional loss of heterozygosity to be detected, assigned to a meiotic or mitotic origin, and quantified as a proportion of the cells in the sample. We show results from a multi-ethnic MAD-seq (meMAD-seq) capture design that works equally well in populations of diverse racial and ethnic origins and how the MADSEQ analytical approach can be applied to exome or whole-genome sequencing data, revealing previously unrecognized aneuploidy or copy number neutral loss of heterozygosity in samples studied by the 1000 Genomes Project, cell lines from public repositories, and one of the Illumina Platinum Genomes samples. We have made the meMAD-seq capture design and MADSEQ analytical software open for unrestricted use, with the goal that they can be applied in clinical samples to allow new insights into the unrecognized prevalence of mosaic chromosomal aneuploidy in humans and its phenotypic associations., (© 2018 Kong et al.; Published by Cold Spring Harbor Laboratory Press.)

Five single nucleotide polymorphisms (SNPs) associated with thyroid cancer (TC) risk have been reported: rs2910164 (5q24); rs6983267 (8q24); rs965513 and rs1867277 (9q22); and rs944289 (14q13). Most of these associations have not been replicated in independent populations and the combined effects of the SNPs on risk have not been examined. This study genotyped the five TC SNPs in 781 patients recruited through the TCUKIN study. Genotype data from 6122 controls were obtained from the CORGI and Wellcome Trust Case-Control Consortium studies. Significant associations were detected between TC and rs965513A (p=6.35×10(-34)), rs1867277A (p=5.90×10(-24)), rs944289T (p=6.95×10(-7)), and rs6983267G (p=0.016). rs6983267 was most strongly associated under a recessive model (P(GG vs GT + TT)=0.004), in contrast to the association of this SNP with other cancer types. However, no evidence was found of an association between rs2910164 and disease under any risk model (p>0.7). The rs1867277 association remained significant (p=0.008) after accounting for genotypes at the nearby rs965513 (p=2.3×10(-13)) and these SNPs did not tag a single high risk haplotype. The four validated TC SNPs accounted for a relatively large proportion (∼11%) of the sibling relative risk of TC, principally owing to the large effect size of rs965513 (OR 1.74).

Genome-wide scans of genetic variation can potentially provide detailed information on how modern humans colonized the world but require new methods of analysis. We introduce a statistical approach that uses Single Nucleotide Polymorphism (SNP) data to identify sharing of chromosomal segments between populations and uses the pattern of sharing to reconstruct a detailed colonization scenario. We apply our model to the SNP data for the 53 populations of the Human Genome Diversity Project described in Conrad et al. (Nature Genetics 38,1251-60, 2006). Our results are consistent with the consensus view of a single ''Out-of-Africa'' bottleneck and serial dilution of diversity during global colonization, including a prominent East Asian bottleneck. They also suggest novel details including: (1) the most northerly East Asian population in the sample (Yakut) has received a significant genetic contribution from the ancestors of the most northerly European one (Orcadian). (2) Native South Americans have received ancestry from a source closely related to modern North- East Asians (Mongolians and Oroquen) that is distinct from the sources for native North Americans, implying multiple waves of migration into the Americas. A detailed depiction of the peopling of the world is available in animated form. [ABSTRACT FROM AUTHOR]

Fine-scale estimation of recombination rates remains a challenging problem. Experimental techniques can provide accurate estimates at fine scales but are technically challenging and cannot be applied on a genome-wide scale. An alternative source of information comes from patterns of genetic variation. Several statistical methods have been developed to estimate recombination rates from randomly sampled chromosomes. However, most such methods either make poor assumptions about recombination rate variation, or simply assume that there is no rate variation. Since the discovery of recombination hotspots, it is clear that recombination rates can vary over many orders of magnitude at the fine scale. We present a method for the estimation of recombination rates in the presence of recombination hotspots. We demonstrate that the method is able to detect and accurately quantify recombination rate heterogeneity, and is a substantial improvement over a commonly used method. We then use the method to reanalyze genetic variation data from the HLA and MS32 regions of the human genome and demonstrate that the method is able to provide accurate rate estimates and simultaneously detect hotspots. [ABSTRACT FROM AUTHOR]