An integrative approach to predicting the functional effects of small indels in non-coding regions of the human genome

Background: Small insertions and deletions (indels) have a significant influence in human disease and, in terms of frequency, they are second only to single nucleotide variants as pathogenic mutations. As the majority of mutations associated with complex traits are located outside the exome, it is crucial to investigate the potential pathogenic impact of indels in non-coding regions of the human genome. Results: We present FATHMM-indel, an integrative approach to predict the functional effect, pathogenic or neutral, of indels in non-coding regions of the human genome. Our method exploits various genomic annotations in addition to sequence data. When validated on benchmark data, FATHMM-indel significantly outperforms CADD and GAVIN, state of the art models in assessing the pathogenic impact of non-coding variants. FATHMM-indel is available via a web server at indels.biocompute.org.uk. Conclusions: FATHMM-indel can accurately predict the functional impact and prioritise small indels throughout the whole non-coding genome

Mosaic structural variation in children with developmental disorders

Delineating the genetic causes of developmental disorders is an area of active investigation. Mosaic structural abnormalities, defined as copy number or loss of heterozygosity events that are large and present in only a subset of cells, have been detected in 0.2–1.0% of children ascertained for clinical genetic testing. However, the frequency among healthy children in the community is not well characterized, which, if known, could inform better interpretation of the pathogenic burden of this mutational category in children with developmental disorders. In a case–control analysis, we compared the rate of large-scale mosaicism between 1303 children with developmental disorders and 5094 children lacking developmental disorders, using an analytical pipeline we developed, and identified a substantial enrichment in cases (odds ratio = 39.4, P-value 1.073e − 6). A meta-analysis that included frequency estimates among an additional 7000 children with congenital diseases yielded an even stronger statistical enrichment (P-value 1.784e − 11). In addition, to maximize the detection of low-clonality events in probands, we applied a trio-based mosaic detection algorithm, which detected two additional events in probands, including an individual with genome-wide suspected chimerism. In total, we detected 12 structural mosaic abnormalities among 1303 children (0.9%). Given the burden of mosaicism detected in cases, we suspected that many of the events detected in probands were pathogenic. Scrutiny of the genotypic–phenotypic relationship of each detected variant assessed that the majority of events are very likely pathogenic. This work quantifies the burden of structural mosaicism as a cause of developmental disorders

TCTEX1D2 mutations underlie Jeune asphyxiating thoracic dystrophy with impaired retrograde intraflagellar transport

Tiina Paunio on työryhmän UK10K jäsen.The analysis of individuals with ciliary chondrodysplasias can shed light on sensitive mechanisms controlling ciliogenesis and cell signalling that are essential to embryonic development and survival. Here we identify TCTEX1D2 mutations causing Jeune asphyxiating thoracic dystrophy with partially penetrant inheritance. Loss of TCTEX1D2 impairs retrograde intraflagellar transport (IFT) in humans and the protist Chlamydomonas, accompanied by destabilization of the retrograde IFT dynein motor. We thus define TCTEX1D2 as an integral component of the evolutionarily conserved retrograde IFT machinery. In complex with several IFT dynein light chains, it is required for correct vertebrate skeletal formation but may be functionally redundant under certain conditions.Peer reviewe

Whole-genome sequence-based analysis of thyroid function

Tiina Paunio on työryhmän UK10K Consortium jäsen.Normal thyroid function is essential for health, but its genetic architecture remains poorly understood. Here, for the heritable thyroid traits thyrotropin (TSH) and free thyroxine (FT4), we analyse whole-genome sequence data from the UK10K project (N = 2,287). Using additional whole-genome sequence and deeply imputed data sets, we report meta-analysis results for common variants (MAF >= 1%) associated with TSH and FT4 (N = 16,335). For TSH, we identify a novel variant in SYN2 (MAF = 23.5%, P = 6.15 x 10(-9)) and a new independent variant in PDE8B (MAF = 10.4%, P = 5.94 x 10(-14)). For FT4, we report a low-frequency variant near B4GALT6/ SLC25A52 (MAF = 3.2%, P = 1.27 x 10(-9)) tagging a rare TTR variant (MAF = 0.4%, P = 2.14 x 10(-11)). All common variants explain >= 20% of the variance in TSH and FT4. Analysis of rare variants (MAFPeer reviewe

Improved Jaguar Algorithm with Tabu List to Solve Function Optimization Problem

[[abstract]]用於解各式現實生活中最佳化問題的演算法，是近期相當熱門的研究議題之一。為了在有限的時間或成本找到問題的最佳解，傳統演算法致力於平衡演算法在解空間中廣度搜尋及深度搜索力道。傳統的搜尋方法可能會有潛在的問題，需要針對不同的最佳化問題調整大量的參數來權衡廣及深的力道，但會容易造成廣和深的搜索力道互相拉扯。本論文的研究是基於美洲豹演算法，一個以全新理念設計，不論在任何區域，先達到深度搜尋的極限，並紀錄這次搜尋的資訊建立領地，再利用探勘到的領地資訊全力進行跳脫，達到廣度搜尋的效果；跳脫的時候，以好的方向為導引增加找到最佳解的效率。綜合這些能力，美洲豹演算法同時擁有強健的深度及廣度搜尋能力。我們的方法是啟發於美洲豹演算法的領地性類似禁忌搜尋演算法的禁忌，本論文提出的領地禁忌機制是利用美洲豹演算法的領地資訊來防止演算法的粒子重複探勘，藉此達到減少計算次數提高搜尋效率的目的。在後面的實驗章節，我們的方法會同時與原本的美洲豹演算法和各式演算法比較。如：蜂群演算法、粒子群優演算法和基因演算法等等，其結果也有良好的表現。[[abstract]]Meta-heuristic is one of the popular research which is implemented to solve optimization problems in real life. In order to obtain the best solution in limited cost or time,traditional meta-Heuristics are devoted to balancing the capabilities of exploration and exploitation. For the sake of the purpose, traditional methods implement lots of weights (parameters) to balance two capabilities, and implement random variables or numerous population and generation to increase opportunities for finding a better solution. However, the searching mode of traditional methods might have some potential problems. Such as exploration and exploitation in traditional methods might restrict to each other. And implemented parameters shall be adjusted for different problems. Therefore, Jaguar Algorithm is designed in a new concept. We concentrate on exploitation before exploration. At first, proposed method tries its best to find the optimal solution in the arbitrary area. Then it focuses on jumping to better area based on the information of the history. Along the tendency of found areas to find out the place of the global optimum. The proposed method achieves strong capabilities of both exploitation and exploration with these features. Our idea comes from that the idea of territory is similar to Tabu Search Algorithm. The proposed method used Jaguar Algorithm territory’s information to prevent other jaguars or itself entering those searched areas again and reduce the evaluations.[[note]]碩