@article {950, title = {8q24 genetic variation and comprehensive haplotypes altering familial risk of prostate cancer.}, journal = {Nat Commun}, volume = {11}, year = {2020}, month = {2020 03 23}, pages = {1523}, abstract = {

The 8q24 genomic locus is tied to the origin of numerous cancers. We investigate its contribution to hereditary prostate cancer (HPC) in independent study populations of the Nashville Familial Prostate Cancer Study and International Consortium for Prostate Cancer Genetics (combined: 2,836 HPC cases, 2,206 controls of European ancestry). Here we report 433 variants concordantly associated with HPC in both study populations, accounting for 9\% of heritability and modifying age of diagnosis as well as aggressiveness; 183 reach genome-wide significance. The variants comprehensively distinguish independent risk-altering haplotypes overlapping the 648 kb locus (three protective, and four risk (peak odds ratios: 1.5, 4, 5, and 22)). Sequence of the near-Mendelian haplotype reveals eleven causal mutation candidates. We introduce a linkage disequilibrium-based algorithm discerning eight independent sentinel variants, carrying considerable risk prediction ability (AUC = 0.625) for a single locus. These findings elucidate 8q24 locus structure and correlates for clinical prediction of prostate cancer risk.

}, keywords = {Aged, Case-Control Studies, Chromosomes, Human, Pair 8, European Continental Ancestry Group, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Haplotypes, Heterozygote, Humans, Linkage Disequilibrium, Male, Middle Aged, Mutation, Prostatic Neoplasms, Protective Factors, Risk Factors}, issn = {2041-1723}, doi = {10.1038/s41467-020-15122-1}, author = {Dupont, William D and Breyer, Joan P and Plummer, W Dale and Chang, Sam S and Cookson, Michael S and Smith, Joseph A and Blue, Elizabeth E and Bamshad, Michael J and Smith, Jeffrey R} } @article {875, title = {Non-parametric Polygenic Risk Prediction via Partitioned GWAS Summary Statistics.}, journal = {Am J Hum Genet}, volume = {107}, year = {2020}, month = {2020 07 02}, pages = {46-59}, abstract = {

In complex trait genetics, the ability to predict phenotype from genotype is the ultimate measure of our understanding of genetic architecture underlying the heritability of a trait. A complete understanding of the genetic basis of a trait should allow for predictive methods with accuracies approaching the trait{\textquoteright}s heritability. The highly polygenic nature of quantitative traits and most common phenotypes has motivated the development of statistical strategies focused on combining myriad individually non-significant genetic effects. Now that predictive accuracies are improving, there is a growing interest in the practical utility of such methods for predicting risk of common diseases responsive to early therapeutic intervention. However, existing methods require individual-level genotypes or depend on accurately specifying the genetic architecture underlying each disease to be predicted. Here, we propose a polygenic risk prediction method that does not require explicitly modeling any underlying genetic architecture. We start with summary statistics in the form of SNP effect sizes from a large GWAS cohort. We then remove the correlation structure across summary statistics arising due to linkage disequilibrium and apply a piecewise linear interpolation on conditional mean effects. In both simulated and real datasets, this new non-parametric shrinkage (NPS) method can reliably allow for linkage disequilibrium in summary statistics of 5 million dense genome-wide markers and consistently improves prediction accuracy. We show that NPS improves the identification of groups at high risk for breast cancer, type 2 diabetes, inflammatory bowel disease, and coronary heart disease, all of which have available early intervention or prevention treatments.

}, keywords = {Aged, Cohort Studies, Diabetes Mellitus, Type 2, Female, Genome-Wide Association Study, Genotype, Humans, Linkage Disequilibrium, Male, Middle Aged, Models, Genetic, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2020.05.004}, author = {Chun, Sung and Imakaev, Maxim and Hui, Daniel and Patsopoulos, Nikolaos A and Neale, Benjamin M and Kathiresan, Sekar and Stitziel, Nathan O and Sunyaev, Shamil R} } @article {373, title = {Identification of Rare Variants in Metabolites of the Carnitine Pathway by Whole Genome Sequencing Analysis.}, journal = {Genet Epidemiol}, volume = {40}, year = {2016}, month = {2016 09}, pages = {486-91}, abstract = {

We use whole genome sequence data and rare variant analysis methods to investigate a subset of the human serum metabolome, including 16 carnitine-related metabolites that are important components of mammalian energy metabolism. Medium pass sequence data consisting of 12,820,347 rare variants and serum metabolomics data were available on 1,456 individuals. By applying a penalization method, we identified two genes FGF8 and MDGA2 with significant effects on lysine and cis-4-decenoylcarnitine, respectively, using Δ-AIC and likelihood ratio test statistics. Single variant analyses in these regions did not identify a single low-frequency variant (minor allele count > 3) responsible for the underlying signal. The results demonstrate the utility of whole genome sequence and innovative analyses for identifying candidate regions influencing complex phenotypes.

}, keywords = {Biomarkers, Carnitine, Female, Fibroblast Growth Factor 8, Genetic Variation, GPI-Linked Proteins, High-Throughput Nucleotide Sequencing, Humans, Linkage Disequilibrium, Lysine, Male, Metabolomics, Middle Aged, Neural Cell Adhesion Molecules, Polymorphism, Single Nucleotide, Sequence Analysis, DNA}, issn = {1098-2272}, doi = {10.1002/gepi.21980}, author = {Yazdani, Akram and Yazdani, Azam and Liu, Xiaoming and Boerwinkle, Eric} } @article {123, title = {PRIMUS: improving pedigree reconstruction using mitochondrial and Y haplotypes.}, journal = {Bioinformatics}, volume = {32}, year = {2016}, month = {2016 Feb 15}, pages = {596-8}, abstract = {

UNLABELLED: PRIMUS is a pedigree reconstruction algorithm that uses estimates of genome-wide identity by descent to reconstruct pedigrees consistent with observed genetic data. However, when genetic data for individuals within a pedigree are missing, often multiple pedigrees can be reconstructed that fit the data. We report a major expansion of PRIMUS that uses mitochondrial (mtDNA) and non-recombining Y chromosome (NRY) haplotypes to eliminate many pedigree structures that are inconsistent with the genetic data. We demonstrate that discordances in mtDNA and NRY haplotypes substantially reduce the number of potential pedigrees, and often lead to the identification of the correct pedigree.

AVAILABILITY AND IMPLEMENTATION: We have implemented PRIMUS updates in PERL and it is available at primus.gs.washington.edu.

}, keywords = {Algorithms, Chromosomes, Human, Y, Computer Simulation, DNA, Mitochondrial, Genetics, Population, Haplotypes, Humans, Linkage Disequilibrium, Pedigree, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btv618}, author = {Staples, Jeffrey and Ekunwe, Lynette and Lange, Ethan and Wilson, James G and Nickerson, Deborah A and Below, Jennifer E} } @article {279, title = {Rare-variant extensions of the transmission disequilibrium test: application to autism exome sequence data.}, journal = {Am J Hum Genet}, volume = {94}, year = {2014}, month = {2014 Jan 02}, pages = {33-46}, abstract = {

Many population-based rare-variant (RV) association tests, which aggregate variants across a region, have been developed to analyze sequence data. A drawback of analyzing population-based data is that it is difficult to adequately control for population substructure and admixture, and spurious associations can occur. For RVs, this problem can be substantial, because the spectrum of rare variation can differ greatly between populations. A solution is to analyze parent-child trio data, by using the transmission disequilibrium test (TDT), which is robust to population substructure and admixture. We extended the TDT to test for RV associations using four commonly used methods. We demonstrate that for all RV-TDT methods, using proper analysis strategies, type I error is well-controlled even when there are high levels of population substructure or admixture. For trio data, unlike for population-based data, RV allele-counting association methods will lead to inflated type I errors. However type I errors can be properly controlled by obtaining p values empirically through haplotype permutation. The power of the RV-TDT methods was evaluated and compared to the analysis of case-control data with a number of genetic and disease models. The RV-TDT was also used to analyze exome data from 199 Simons Simplex Collection autism trios and an association was observed with variants in ABCA7. Given the problem of adequately controlling for population substructure and admixture in RV association studies and the growing number of sequence-based trio studies, the RV-TDT is extremely beneficial to elucidate the involvement of RVs in the etiology of complex traits.

}, keywords = {Alleles, Autistic Disorder, Computer Simulation, Exome, Gene Frequency, Genetic Association Studies, Genetic Predisposition to Disease, Genetic Variation, Haplotypes, Humans, Linkage Disequilibrium, Models, Genetic, Phenotype, Sequence Analysis, DNA}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2013.11.021}, author = {He, Zongxiao and O{\textquoteright}Roak, Brian J and Smith, Joshua D and Wang, Gao and Hooker, Stanley and Santos-Cortez, Regie Lyn P and Li, Biao and Kan, Mengyuan and Krumm, Nik and Nickerson, Deborah A and Shendure, Jay and Eichler, Evan E and Leal, Suzanne M} }