You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: Enhanced Methods for Local Ancestry Assignment in Sequenced Admixed Individuals}
Article:
Abstract:
Inferring the ancestry at each locus in the genome of recently admixed individuals( e. g., Latino Americans) plays a major role in medical and population genetic inferences, ranging from finding disease-risk loci, to inferring recombination rates, to mapping missing contigs in the human genome. Although many methods for local ancestry inference have been proposed, most are designed for use with genotyping arrays and fail to make use of the full spectrum of data available from sequencing. In addition, current haplotype-based approaches are very computationally demanding, requiring large computational time for moderately large sample sizes. Here we present new methods for local ancestry inference that leverage continent-specific variants( CSVs) to attain increased performance over existing approaches in sequenced admixed genomes. A key feature of our approach is that it incorporates the admixed genomes themselves jointly with public datasets, such as 1000 Genomes, to improve the accuracy of CSV calling. We use simulations to show that our approach attains accuracy similar to widely used computationally intensive haplotype-based approaches with large decreases in runtime. Most importantly, we show that our method recovers comparable local ancestries, as the 1000 Genomes consensus local ancestry calls in the real admixed individuals from the 1000 Genomes Project. We extend our approach to account for low-coverage sequencing and show that accurate local ancestry inference can be attained at low sequencing coverage. Finally, we generalize CSVs to sub-continental population-specific variants( sCSVs) and show that in some cases it is possible to determine the sub-continental ancestry for short chromosomal segments on the basis of sCSVs.
Introduction:
Advances in high-throughput genotyping technologies have enabled large-scale studies of genetic variation, from genome-wide association studies( GWAS)[1] to inference of population history from genetic data[2]. The most notable use of high-throughput genotyping has been in GWAS where researchers have reproducibly identified thousands of genetic variants associated with many diseases[3]. Although initial studies have focused on homogenous populations[4], the development of accurate methods for discerning population structure has enabled studies across individuals of different ethnicities such as admixed populations( i. e. populations with genetic ancestry from more than one continent)[5]–[8]. Owing to their recent demographic history, admixed individuals have genomes that are a mosaic of segments originating from different continents. A key component of genetic studies in recently admixed populations is the inference of ancestry at each locus in the genome( i. e. the continental origin of each variant, local ancestry). Although local ancestry has been traditionally used to map genes to diseases through admixture mapping[8]–[10], the past few years have seen the use of local ancestry analyses in a wide range of genetic applications. Recent work has shown that admixture mapping can be used to localize missing sequences from the human reference genome[11], while other analyses of local ancestry in large samples of African American individuals have yielded novel insights into the dynamics of recombination rates across the genome[12],[13]. Local ancestry also can be leveraged to make demographic inferences from genetic data of admixed populations[14]–[17] as well as in finding signals of natural selection in African Americans[18]. Finally, local ancestry is also important for disease genetics in correcting for spurious associations in fine-mapping studies[19] as well as in finding new disease risk loci through a combination of association and admixture mapping[6],[20]–[23]. Many methods have been developed to infer local ancestry in admixed individuals. Early methods[24]–[26] relied on ancestry informative markers within hidden Markov models to achieve high accuracy. With decreasing genotyping costs, newer methods[27]–[30] were designed to use the increasing amount of data from genome-wide genotyping arrays while accounting for linkage disequilibrium( LD) among variants. The currently established methods[31]–[33] model LD in the form of haplotypes to achieve superior accuracy over non-haplotype aware approaches. Recent work in parallel to ours[34] explored the use of conditional random forests in performing local ancestry analysis. Although extremely accurate for African Americans, these methods have not achieved the same level of high accuracy in Latino Americans, partially due to the lack of good proxies for the Native American component[35] and more recent divergence among ancestral populations. Rapid cost decreases in sequencing technologies coupled with the increased power for assessing genetic variation has made sequencing the approach of choice for many of the coming genetic studies[36]–[48]. The amount of variants identified by sequencing makes local ancestry inference in large cohorts of sequenced individuals prohibitively time consuming( e. g. existing HMM-based approaches will take 5 CPU years to infer local ancestry in 15, 000 sequenced African Americans, or 18 days per core on a 100-core cluster). This is particularly important as sample sizes continue to increase to hundreds of thousands of individuals[49]. For example, a recent study of obesity included over 15, 000 African Americans[50] and another study included 30, 000 African Americans for recombination mapping[12]. Here we present improved methods for local ancestry inference for fully sequenced admixed genomes. Sequencing, as opposed to genotyping, is able to catalogue much larger sets of variants with a large component of such variants being continent-specific( i. e. variants that are observed only in individuals from one continental group such as Europeans or Africans). For example, the 1000 Genomes Project[51] has found that 17% of variants with frequencies between 0. 5–5% and 53% of variants with frequencies <0. 5% are continent-specific when comparing European, African, East Asian and American populations. We hypothesized that these variants can be used for ultra-fast assignment of ancestry at every locus in the genome. We term these variants as continent-specific variants( CSVs) and model them within standard hidden Markov models of local ancestry to achieve an accurate and computationally efficient method for local ancestry inference( Lanc-CSV). Our model accounts for potential errors induced by low-coverage sequencing as well as by the finite sample size of the reference panels used for local ancestry inference. As opposed to most previous local ancestry methods that require phased reference panels, our approach only requires allele frequency information for each continental group. Our approach is significantly faster than existing standard haplotype-based approaches making it the approach of choice for large-scale sequencing studies( e. g. our approach is able to infer local ancestry in under 42 CPU days in 15, 000 sequenced genomes, or 0. 42 days per core if a 100-core cluster is available). The very-fast computational speed of our approach allows it to be sample aware by iteratively improving the quality of the CSV calls using the admixed individuals themselves to further boost accuracy by eliminating spuriously identified CSVs( see Methods). We use simulations of recently admixed individuals starting from 1000 Genomes data to show that Lanc-CSV achieves comparable accuracy to existing methods( e. g. mean r2 = 0. 92 across simulations of African Americans, Mexicans, and Puerto Ricans as compared to 0. 93 for LAMP-LD[31], 0. 84 for RFMix[52] and 0. 80 for MULTIMIX[53]). We investigate the effect of low coverage sequencing on our method in simulations and show that at 5× coverage our approach achieves an r2 = 0. 86 in African Americans, 0. 70 in Mexicans and 0. 78 in Puerto Ricans. More importantly, we investigate whether similar results can be obtained in real data. We infer local ancestry using our approach in the real African American, Mexican, and Puerto Rican individuals from 1000 Genomes and find that Lanc-CSV agrees with the published consensus local ancestry calls[51]( mean r2 = 0. 79 across the three sets of comparisons as compared to a mean r2 = 0. 81 for a haplotype-based method, see Results). While our current method achieves comparable results to existing methods with the given data sets, we demonstrate that the iterative sample aware CSV updating continues to increase the overall accuracy as the sample size increases. With large studies this may give Lanc-CSV a further accuracy advantage over existing methods. Finally, we extend the concept of CSVs to sub-continental population-specific variants( sCSVs) and show that they can be used to perform ancestry assignment with individuals admixed from two ancestries from the same continent. As the costs of sequencing rapidly decreases and genetic studies sequence more samples, the tradeoff between computational runtime and accuracy becomes critical for local ancestry inference. Using our proposed approaches we can reliably infer local ancestry in very large sequenced cohorts at a fraction of the computational cost of existing approaches. We provide Lanc-CSV as a free software package for the community interested in local ancestry inference at http://bogdan. bioinformatics. ucla. edu/software/lanccsv.
Discussion:
We have presented here an approach for local ancestry inference in fully sequenced recently admixed individuals. Our approach makes use of alleles that are found to be present only in individuals from a given continental group( continent-specific variants, CSVs). Through the use of real data from 1000 Genomes we have shown that the density of such CSVs is high enough across the genome to allow for fast and accurate inference of local ancestry. It should be noted that the 1000 Genomes haplotypes are based on 4× sequencing data. Not only does the low coverage make this data noisier, but it also misses many CSVs that are in the individuals but not called due to the low coverage. As more high coverage reference panels are constructed our method will become increasingly accurate as more CSVs are identified and spurious CSVs removed. Having no pre-compute time and fast runtime per individual allows for our approach to be sample-aware in an iterative fashion. As opposed to previous approaches Lanc-CSV shows increased accuracy as more admixed samples are being analyzed. We show that as the method is run on increasing numbers of simulated individuals, it exceeds the accuracy that is obtained by LAMP-LD on the 200 Mexican and Puerto Rican samples. We expect this feature to become more important as larger sample sizes are being analyzed since the accuracy should continue to increase. The real data analysis demonstrates the necessity of having reference panels well matched to the admixed population or having a sample aware method that can correct for poorly matched reference panels. Lanc-CSV achieves comparable results to LAMP-LD in these few real African-American, Puerto Rican and Mexican individuals. Unlike LAMP-LD, we expect our approach to continue improving in accuracy as more sequenced individuals from each continental population become available. We extended the concept of continent-specific variants to sub-continental population-specific variants and showed that under some scenarios it is possible to determine the sub-continental ancestry. We confirmed that in real ASW individuals, admixture was most likely between individuals from western Africa( near or related to the YRI); as more reference panels become available for these and other populations, we expect sCSVs to be increasingly informative of the sub-continental population ancestries. Although sCSVs show potential for sub-continental ancestry calling in haploid data, more sophisticated methods may prove fruitful for diploid calling. A future direction for research that may prove fruitful is to relax assumptions used in our approach and by finding better ways to parameterize the method. Linkage disequilibrium among the CSVs is a main contributor to errors and further work explicitly modeling the LD structure between CSVs may provide increased accuracy. The current method has a uniform error rate for spurious CSVs across all ancestries. However the number of spurious European CSVs is much higher than the number of spurious African CSVs and the number of reference haplotypes is not controlled for in determining the error rate; therefore a non-uniform error model may further increase accuracy.
Advances in sequencing technologies are dramatically changing the volume and type of data collected in genetic studies . Although most genetic studies so far have focused on individuals of European ancestry , recent studies are increasingly being performed in individuals of admixed ancestry ( i . e . , with recent ancestors from multiple continents , e . g . , Latino Americans ) . A key component in such studies is the accurate inference of continental ancestry at each segment in the genome of these individuals . In this work we present accurate and robust methods that use continent-specific variants ( i . e . , genetic variants observed only in individuals of a given continent ) , now readily accessible through sequencing technology , to perform extremely fast and accurate inference of the ancestral origin of each genomic segment in recently admixed individuals .