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      Genome-wide association study of Hirschsprung disease detects a novel low-frequency variant at the RET locus

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          Abstract

          <p class="first" id="Par1">Hirschsprung disease (HSCR) is a congenital disorder with a population incidence of ~1/5000 live births, defined by an absence of enteric ganglia along variable lengths of the colon. HSCR genome-wide association studies (GWAS) have found common associated variants at <i>RET</i>, <i>SEMA3</i>, and <i>NRG1</i>, but they still fail to explain all of its heritability. To enhance gene discovery, we performed a GWAS of 170 cases identified from the Danish nationwide pathology registry with 4717 controls, based on 6.2 million variants imputed from the haplotype reference consortium panel. We found a novel low-frequency variant (rs144432435), which, when conditioning on the lead <i>RET</i> single-nucleotide polymorphism (SNP), was of genome-wide significance in the discovery analysis. This conditional association signal was replicated in a Swedish HSCR cohort with discovery plus replication meta-analysis conditional odds ratio of 6.6 ( <i>P</i> = 7.7 × 10 <sup>−10</sup>; 322 cases and 4893 controls). The conditional signal was, however, not replicated in two HSCR cohorts from USA and Finland, leading to the hypothesis that rs144432435 tags a rare haplotype present in Denmark and Sweden. Using the genome-wide complex trait analysis method, we estimated the SNP heritability of HSCR to be 88%, close to estimates based on classical family studies. Moreover, by using Lasso (least absolute shrinkage and selection operator) regression we were able to construct a genetic HSCR predictor with a area under the receiver operator characteristics curve of 76% in an independent validation set. In conclusion, we combined the largest collection of sporadic Hirschsprung cases to date (586 cases) to further elucidate HSCR’s genetic architecture. </p>

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          Genotype, haplotype and copy-number variation in worldwide human populations.

          Genome-wide patterns of variation across individuals provide a powerful source of data for uncovering the history of migration, range expansion, and adaptation of the human species. However, high-resolution surveys of variation in genotype, haplotype and copy number have generally focused on a small number of population groups. Here we report the analysis of high-quality genotypes at 525,910 single-nucleotide polymorphisms (SNPs) and 396 copy-number-variable loci in a worldwide sample of 29 populations. Analysis of SNP genotypes yields strongly supported fine-scale inferences about population structure. Increasing linkage disequilibrium is observed with increasing geographic distance from Africa, as expected under a serial founder effect for the out-of-Africa spread of human populations. New approaches for haplotype analysis produce inferences about population structure that complement results based on unphased SNPs. Despite a difference from SNPs in the frequency spectrum of the copy-number variants (CNVs) detected--including a comparatively large number of CNVs in previously unexamined populations from Oceania and the Americas--the global distribution of CNVs largely accords with population structure analyses for SNP data sets of similar size. Our results produce new inferences about inter-population variation, support the utility of CNVs in human population-genetic research, and serve as a genomic resource for human-genetic studies in diverse worldwide populations.
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            Efficient haplotype matching and storage using the positional Burrows–Wheeler transform (PBWT)

            Motivation: Over the last few years, methods based on suffix arrays using the Burrows–Wheeler Transform have been widely used for DNA sequence read matching and assembly. These provide very fast search algorithms, linear in the search pattern size, on a highly compressible representation of the dataset being searched. Meanwhile, algorithmic development for genotype data has concentrated on statistical methods for phasing and imputation, based on probabilistic matching to hidden Markov model representations of the reference data, which while powerful are much less computationally efficient. Here a theory of haplotype matching using suffix array ideas is developed, which should scale too much larger datasets than those currently handled by genotype algorithms. Results: Given M sequences with N bi-allelic variable sites, an O(NM) algorithm to derive a representation of the data based on positional prefix arrays is given, which is termed the positional Burrows–Wheeler transform (PBWT). On large datasets this compresses with run-length encoding by more than a factor of a hundred smaller than using gzip on the raw data. Using this representation a method is given to find all maximal haplotype matches within the set in O(NM) time rather than O(NM 2) as expected from naive pairwise comparison, and also a fast algorithm, empirically independent of M given sufficient memory for indexes, to find maximal matches between a new sequence and the set. The discussion includes some proposals about how these approaches could be used for imputation and phasing. Availability: http://github.com/richarddurbin/pbwt Contact: richard.durbin@sanger.ac.uk
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              The Danish Pathology Register.

              The National Board of Health, Denmark in 1997 published guidelines for reporting of pathology data and the Danish Pathology Register (DPR) was established. DPR contains patient, pathology, and workload data. All records are subject to error tracing. The DPR covers all pathology data in Denmark. The data is used by the pathologists in the daily diagnostic process. The National Board of Health uses the data in the Danish Cancer Registry and DPR is unique for research as data can be linked to tissue biobanks and clinical databases.
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                Author and article information

                Journal
                European Journal of Human Genetics
                Eur J Hum Genet
                Springer Nature
                1018-4813
                1476-5438
                January 29 2018
                Article
                10.1038/s41431-017-0053-7
                5891499
                29379196
                3963988f-7ede-4dae-9cfd-5983290367ac
                © 2018

                http://www.springer.com/tdm

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