CEGA: a method for inferring natural selection by comparative population genomic analysis across species.

We developed maximum likelihood method for detecting positive selection or balancing selection using multilocus or genomic polymorphism and divergence data from two species. The method is especially useful for investigating natural selection in noncoding regions. Simulations demonstrate that the method outperforms existing methods in detecting both positive and balancing selection. We apply the method to population genomic data from human and chimpanzee. The list of genes identified under selection in the noncoding regions is prominently enriched in pathways related to the brain and nervous system. Therefore, our method will serve as a useful tool for comparative population genomic analysis.

Genomic and epidemiological perspectives on the first local sporadic cases of Mpox in China.

From June 7th to 11th, 2023, eight cases of Mpox were identified in Guangzhou, China. This is the first report of multiple local sporadic cases after the imported case in Chongqing, China. Epidemiological investigation revealed that these cases had no history of international travel and no connections with each other. Haplotype network and phylogenetic analyses indicated that the possible origin is likely from Japan, although the direct origin may remain uncertain due to limited genomic sequences and sampling bias in GISAID. The three Guangzhou sequences have accumulated several novel mutations, suggesting the local transmission of Mpox may have been ongoing for some time. Based on the daily cases during the early stage of Mpox outbreak in four other countries, the number of possible infected cases in Guangzhou is inferred to be more than 300, suggesting that swift and efficient control measures must be implemented to mitigate the risk of a potential epidemic.

fastHaN: a fast and scalable program for constructing haplotype network for large-sample sequences.

Haplotype networks can be used to demonstrate the genealogical relationships of DNA sequences within species, and thus are widely applied in population genetics, molecular ecology, epidemiology and evolutionary studies. However, existing programs become computationally infeasible as the sample size increases. Here, we present fastHaN, an efficient and scalable program suitable for constructing haplotype networks for large samples. On a data set with the haplotype length of 30 kb, the Median Joining Network (MJN) algorithm implemented by fastHaN is 3000 times faster than PopART and 70 times faster than NETWORK in single-threaded mode. The implementation of the Templeton-Crandall-Sing (TCS) algorithm is 100 times faster than PopART and 5800 times faster than the TCS software. Moreover, fastHaN also enables multi-threaded mode with scalability. The source code is freely available on https://github.com/ChenHuaLab/fastHaN/. A web-based version is also available on https://ngdc.cncb.ac.cn/haplotype/.

The Genome Sequence Archive Family: Toward Explosive Data Growth and Diverse Data Types.

The Genome Sequence Archive (GSA) is a data repository for archiving raw sequence data, which provides data storage and sharing services for worldwide scientific communities. Considering explosive data growth with diverse data types, here we present the GSA family by expanding into a set of resources for raw data archive with different purposes, namely, GSA (https://ngdc.cncb.ac.cn/gsa/), GSA for Human (GSA-Human, https://ngdc.cncb.ac.cn/gsa-human/), and Open Archive for Miscellaneous Data (OMIX, https://ngdc.cncb.ac.cn/omix/). Compared with the 2017 version, GSA has been significantly updated in data model, online functionalities, and web interfaces. GSA-Human, as a new partner of GSA, is a data repository specialized in human genetics-related data with controlled access and security. OMIX, as a critical complement to the two resources mentioned above, is an open archive for miscellaneous data. Together, all these resources form a family of resources dedicated to archiving explosive data with diverse types, accepting data submissions from all over the world, and providing free open access to all publicly available data in support of worldwide research activities.

AIM-SNPtag: A computationally efficient approach for developing ancestry-informative SNP panels.

Inferring an individual's ancestry or group membership using a small set of highly informative genetic markers is very useful in forensic and medical genetics. However, given the huge amount of SNP data available from a diverse of populations, it is challenging to develop informative panels by exhaustively searching for all possible SNP combinations. In this study, we formulate it as an algorithm problem of selecting an optimal set of SNPs that maximizes the inference accuracy while minimizes the set size. Built on this conception, we develop a computational approach that is capable of constructing ancestry informative panels from multi-population genome-wide SNP data efficiently. We evaluated the performance of the method by comparing the panel size and membership inference accuracy of the constructed SNP panels to panels selected through empirical procedures in previous studies. For the membership inference of population groups including Asian, European, African, East Asian and Southeast Asian, a 36-SNP panel developed by our approach has an overall accuracy of 99.07%, and a 21-SNP subset of the panel has an overall accuracy of 95.36%. In comparison, an existing panel requires 74 SNPs to achieve an accuracy of 94.14% on the same set of population groups. We further apply the method to four subpopulations within Europe (Finnish, British, Spanish and Italian); a 175-SNP panel can discriminate individuals of those European subpopulations with an accuracy of 99.36%, of which a 68-SNP subset can achieve an accuracy of 95.07%. We expect our method to be a useful tool for constructing ancestry informative markers in forensic genetics.

Inferring Chinese surnames with Y-STR profiles.

Co-ancestry of human surnames and Y-chromosomes in most human populations and social groups suggests the possibility of inferring one from the other. However, such an intuitive perspective remains to be formally explored. In the present study, we develop two computational methods, based on cosine distance (dcos) and coalescence distance (dcoal) respectively, to infer surnames from Y-STR profiles. We also survey Y-STR variations at 15 loci for 19,009 individuals of Shandong Province in China. For a total of 266 surnames included in the data set, our methods can pinpoint to a single surname with an average accuracy of 65%, and with an average accuracy higher than 80% when providing >4 candidate surnames. We also demonstrate that increasing the sample size of surnames and the number of STR loci improves the accuracy of surname inference. Our results indicate that the 15 non-duplicated Y-STR loci contain information from which surname can be reliably inferred for Chinese populations, showing a promising application in forensics.