Application of permanents of square matrices for DNA identification in multiple-fatality cases.

[Background]DNA profiling is essential for individual identification. In forensic medicine, the likelihood ratio (LR) is commonly used to identify individuals. The LR is calculated by comparing two hypotheses for the sample DNA: that the sample DNA is identical or related to a reference DNA, and that it is randomly sampled from a population. For multiple-fatality cases, however, identification should be considered as an assignment problem, and a particular sample and reference pair should therefore be compared with other possibilities conditional on the entire dataset. [Results]We developed a new method to compute the probability via permanents of square matrices of nonnegative entries. As the exact permanent is known as a #P-complete problem, we applied the Huber–Law algorithm to approximate the permanents. We performed a computer simulation to evaluate the performance of our method via receiver operating characteristic curve analysis compared with LR under the assumption of a closed incident. Differences between the two methods were well demonstrated when references provided neither obligate alleles nor impossible alleles. The new method exhibited higher sensitivity (0.188 vs. 0.055) at a threshold value of 0.999, at which specificity was 1, and it exhibited higher area under a receiver operating characteristic curve (0.990 vs. 0.959, P = 9.6E-15). [Conclusions]Our method therefore offers a solution for a computationally intensive assignment problem and may be a viable alternative to LR-based identification for closed-incident multiple-fatality cases

東アジア人における大規模 eQTL マップはLDマッピングにおいて新規候補遺伝子を見出すとともに、配列多型の転写への影響を全ゲノム的に明らかにする

京都大学0048新制・課程博士博士(医学)甲第18847号医博第3958号新制||医||1007(附属図書館)31798京都大学大学院医学研究科医学専攻(主査)教授 小川 誠司, 教授 小泉 昭夫, 教授 藤渕 航学位規則第4条第1項該当Doctor of Medical ScienceKyoto UniversityDFA

Large-Scale East-Asian eQTL Mapping Reveals Novel Candidate Genes for LD Mapping and the Genomic Landscape of Transcriptional Effects of Sequence Variants

<div><p>Profiles of sequence variants that influence gene transcription are very important for understanding mechanisms that affect phenotypic variation and disease susceptibility. Using genotypes at 1.4 million SNPs and a comprehensive transcriptional profile of 15,454 coding genes and 6,113 lincRNA genes obtained from peripheral blood cells of 298 Japanese individuals, we mapped expression quantitative trait loci (eQTLs). We identified 3,804 <i>cis-</i>eQTLs (within 500 kb from target genes) and 165 <i>trans</i>-eQTLs (>500 kb away or on different chromosomes). <i>Cis-</i>eQTLs were often located in transcribed or adjacent regions of genes; among these regions, 5′ untranslated regions and 5′ flanking regions had the largest effects. Epigenetic evidence for regulatory potential accumulated in public databases explained the magnitude of the effects of our eQTLs. <i>Cis</i>-eQTLs were often located near the respective target genes, if not within genes. Large effect sizes were observed with eQTLs near target genes, and effect sizes were obviously attenuated as the eQTL distance from the gene increased. Using a very stringent significance threshold, we identified 165 large-effect <i>trans</i>-eQTLs. We used our eQTL map to assess 8,069 disease-associated SNPs identified in 1,436 genome-wide association studies (GWAS). We identified genes that might be truly causative, but GWAS might have failed to identify for 148 out of the GWAS-identified SNPs; for example, <i>TUFM</i> (<i>P</i> = 3.3E-48) was identified for inflammatory bowel disease (early onset); <i>ZFP90</i> (<i>P</i> = 4.4E-34) for ulcerative colitis; and <i>IDUA</i> (<i>P</i> = 2.2E-11) for Parkinson's disease. We identified four genes (<i>P</i><2.0E-14) that might be related to three diseases and two hematological traits; each expression is regulated by <i>trans</i>-eQTLs on a different chromosome than the gene.</p></div

Cis-eQTL map.

<p>–log₁₀ P values of cis-eQTLs are plotted against the respective chromosomal positions. eQTLs for mRNA transcripts are shown in red; lincRNA transcripts are shown in green; and other transcripts are shown in black. The vertical dashed lines separate chromosomes.</p

Summary statistics and counts of <i>cis</i>- and <i>trans</i>-eQTLs at thresholds by <i>R2</i> or |β|.

<p>FDR: false discovery rate; FWER: family-wise error rate; SD: standard deviation; IQR: inter-quartile range; <i>R</i>²: proportion of phenotypic variances explained by genotypes; |β|: absolute value of coefficient of genotypes.</p><p>The sum of #unique eQTLs counted within RNA types is not necessarily equal to #unique eQTLs counted for all transcripts because the same eQTLs may be counted in more than one RNA types. The number of genes for All and each type do not match for a similar reason.</p

Cumulative curves of effect magnitudes of <i>cis-</i>eQTLs in gene-structure-based functional categories.

<p>Cumulative curves represent the distributions of |β| values or <i>R</i>² values of <i>cis</i>-eQTLs in each category. Cumulative distribution of all <i>cis-</i>eQTLs (A–D) or all exonic <i>cis-</i>eQTLs (E–F) are shown in grey. The X axis is a log scale. A, B) Distributions of genic and intergenic <i>cis-</i>eQTLs for |β| values (A) or for <i>R</i>² values (B). C, D) Distributions of genic subcategories and intergenics for |β| values (C) or for <i>R</i>² values (D). E, F) Distributions of nonsynonymous and synonymous eQTLs for |β| values (E) and for <i>R</i>² values (F).</p