Fine-scale population structure and the era of next-generation sequencing

Can Radiofrequency Ablation Replace Liver Resection for Solitary Colorectal Liver Metastasis? A Systemic Review and Meta-Analysis

Radiofrequency ablation (RFA) can be a favorable option for patients with colorectal liver metastasis (CRLM). However, current reports about the therapeutic efficacy of liver resection (LR) and RFA for colorectal liver metastasis (CRLM) still remain controversial, especially for solitary CRLM. Therefore, this meta-analysis was performed to evaluate the therapeutic efficacy between LR and RFA for solitary CRLM. First, a comprehensive search for published studies was conducted using PubMed, the Cochrane Library Central, and Web of Science. Each study was reviewed and data extracted. In this meta-analysis, 10 studies (11 study arms) were finally included. The meta-analysis was performed using risk ratio (RR) and random effect model or fixed effect model, in which 95% confidence intervals (95% CI) for RR were calculated. The primary outcomes were disease-free survival (DFS) and overall survival (OS) at 1, 3, or 5 years plus complication rate. The results showed that patients treated by LR achieved better PFS and OS than those by RFA, but subgroup analysis and meta-regression displayed that the efficacy of RFA was equivalent to that of LR in solitary CRLM, when conditions were limited to tumors of ≤ 3 cm and fewer synchronous metastasis in the publication years 2011–2018. Meanwhile, RFA achieved lower complication rates when compared with LR. In conclusion, although patients treated by RFA cannot achieve better PFS and OS than those by LR, RFA can be considered a viable treatment option for solitary CRLM, with potentially lower complication rates.

Clustering-Based Compression for Population DNA Sequences

Due to the advancement of DNA sequencing techniques, the number of sequenced individual genomes has experienced an exponential growth. Thus, effective compression of this kind of sequences is highly desired. In this work, we present a novel compression algorithm called Reference-based Compression algorithm using the concept of Clustering (RCC). The rationale behind RCC is based on the observation about the existence of substructures within the population sequences. To utilize these substructures, k-means clustering is employed to partition sequences into clusters for better compression. A reference sequence is then constructed for each cluster so that sequences in that cluster can be compressed by referring to this reference sequence. The reference sequence of each cluster is also compressed with reference to a sequence which is derived from all the reference sequences. Experiments show that RCC can further reduce the compressed size by up to 91.0 percent when compared with state-of-the-art compression approaches. There is a compromise between compressed size and processing time. The current implementation in Matlab has time complexity in a factor of thousands higher than the existing algorithms implemented in C/C++. Further investigation is required to improve processing time in future.

Ancient selection for derived alleles at a GDF5 enhancer influencing human growth and osteoarthritis risk

Variants in GDF5 are associated with human arthritis and decreased height, but the causal mutations are still unknown. We surveyed the Gdf5 locus for regulatory regions in transgenic mice and fine-mapped separate enhancers controlling expression in joints versus growing ends of long bones. A large downstream regulatory region contains a novel growth enhancer (GROW1), which is required for normal Gdf5 expression at ends of developing bones and for normal bone lengths in vivo. Human GROW1 contains a common base-pair change that decreases enhancer activity and colocalizes with peaks of positive selection in humans. The derived allele is rare in Africa but common in Eurasia and is found in Neandertals and Denisovans. Our results suggest that an ancient regulatory variant in GROW1 has been repeatedly selected in northern environments and that past selection on growth phenotypes explains the high frequency of a GDF5 haplotype that also increases arthritis susceptibility in many human populations.

Genome-Wide Association Studies of Chemotherapeutic Toxicities: Genomics of Inequality

With an estimated global population of cancer survivors exceeding 32 million and growing, there is a heightened awareness of the long-term toxicities resulting from cancer treatments and their impact on quality of life. Unexplained heterogeneity in the persistence and development of toxicities, as well as an incomplete understanding of their mechanisms, have generated a growing need for the identification of predictive pharmacogenomic markers. Early studies addressing this need used a candidate gene approach; however, over the last decade, unbiased and comprehensive genome-wide association studies (GWAS) have provided markers of phenotypic risk and potential targets to explore the mechanistic and regulatory pathways of biological functions associated with chemotherapeutic toxicity. In this review, we provide the current status of GWAS of chemotherapeutic toxicities with an emphasis on examining the ancestral diversity of the representative cohorts within these studies. Persistent calls to incorporate both ancestrally diverse and/or admixed populations into genomic efforts resulted in a recent rise in the number of studies utilizing cohorts of East Asian descent; however, few pharmacogenomic studies to date include cohorts of African, Indigenous American, Southwest Asian, and admixed populations. Through comprehensively evaluating sample size, composition by ancestry, genome-wide significant variants, and population-specific minor allele frequencies as reported by HapMap/dbSNP using NCBI PubMed and the NHGRI-EBI GWAS Catalog, we illustrate how allele frequencies and effect sizes tend to vary among individuals of differing ancestries. In an era of personalized medicine, the lack of diversity in genome-wide studies of anticancer agent toxicity may contribute to the health disparity gap. Clin Cancer Res; 23(15); 4010-9. ©2017 AACR.

A meta-analysis of randomized and placebo-controlled clinical trials suggests that coenzyme Q10 at low dose improves glucose and HbA1c levels

The influence of coenzyme Q10 (CoQ10) on blood glucose (BGL) and HbA1c (HL) levels has been previously investigated; however, the results are inconsistent. Therefore, the purpose of this meta-analysis was to determine if CoQ10 could affect BGL and HL levels based on the existing evidence. PubMed, Cochrane Library, Web of Science, Embase, and Scopus databases were searched for randomized clinical trials from September 1, 1956, to March 01, 2016. To calculate pooled overall effects, a random effect model was used. Because of the presence of heterogeneity, the subgroup analysis and the meta-regression were performed. In total, 18 studies (19 study arms) were included in our investigation focusing on the effects of CoQ10 on BGL (17 arms) and HL (12 arms) changes. CoQ10 significantly reduced BGL, whereas it was ineffective in the reduction of the HL. Because of the significant heterogeneity, in the arms involving BGL, we found that lower doses of CoQ10 (<200 mg/d) and a shorter duration of study created a positive effect on BGL. Also, it appeared that CoQ10 could reduce BGL in patients with a glucose level >6 mmol/L as well as in certain ethnic groups. However, because the meta-regression failed to support the subgroup analysis, the result related to the ethnic group should be used only to generate a hypothesis, which is planned in the future. In conclusion, CoQ10 can reduce BGL, particularly when used in lower doses (< 200 mg/d) and when administration was not longer than 12 weeks, in patients both with and without high BGL.

Estimating the mutation load in human genomes

Next-generation sequencing technology has facilitated the discovery of millions of genetic variants in human genomes. A sizeable fraction of these variants are predicted to be deleterious. Here, we review the pattern of deleterious alleles as ascertained in genome sequencing data sets and ask whether human populations differ in their predicted burden of deleterious alleles - a phenomenon known as mutation load. We discuss three demographic models that are predicted to affect mutation load and relate these models to the evidence (or the lack thereof) for variation in the efficacy of purifying selection in diverse human genomes. We also emphasize why accurate estimation of mutation load depends on assumptions regarding the distribution of dominance and selection coefficients - quantities that remain poorly characterized for current genomic data sets.

Genetic association analysis under complex survey sampling: the Hispanic Community Health Study/Study of Latinos

The cohort design allows investigators to explore the genetic basis of a variety of diseases and traits in a single study while avoiding major weaknesses of the case-control design. Most cohort studies employ multistage cluster sampling with unequal probabilities to conveniently select participants with desired characteristics, and participants from different clusters might be genetically related. Analysis that ignores the complex sampling design can yield biased estimation of the genetic association and inflation of the type I error. Herein, we develop weighted estimators that reflect unequal selection probabilities and differential nonresponse rates, and we derive variance estimators that properly account for the sampling design and the potential relatedness of participants in different sampling units. We compare, both analytically and numerically, the performance of the proposed weighted estimators with unweighted estimators that disregard the sampling design. We demonstrate the usefulness of the proposed methods through analysis of MetaboChip data in the Hispanic Community Health Study/Study of Latinos, which is the largest health study of the Hispanic/Latino population in the United States aimed at identifying risk factors for various diseases and determining the role of genes and environment in the occurrence of diseases. We provide guidelines on the use of weighted and unweighted estimators, as well as the relevant software.

Fine-scale human genetic structure in Western France

The difficulties arising from association analysis with rare variants underline the importance of suitable reference population cohorts, which integrate detailed spatial information. We analyzed a sample of 1684 individuals from Western France, who were genotyped at genome-wide level, from two cohorts D.E.S.I.R and CavsGen. We found that fine-scale population structure occurs at the scale of Western France, with distinct admixture proportions for individuals originating from the Brittany Region and the Vendée Department. Genetic differentiation increases with distance at a high rate in these two parts of Northwestern France and linkage disequilibrium is higher in Brittany suggesting a lower effective population size. When looking for genomic regions informative about Breton origin, we found two prominent associated regions that include the lactase region and the HLA complex. For both the lactase and the HLA regions, there is a low differentiation between Bretons and Irish, and this is also found at the genome-wide level. At a more refined scale, and within the Pays de la Loire Region, we also found evidence of fine-scale population structure, although principal component analysis showed that individuals from different departments cannot be confidently discriminated. Because of the evidence for fine-scale genetic structure in Western France, we anticipate that rare and geographically localized variants will be identified in future full-sequence analyses.

A genome-wide association study of prostate cancer in West African men

Age-adjusted mortality rates for prostate cancer are higher for African-American men compared with those of European ancestry. Recent data suggest that West African men also have elevated risk for prostate cancer relative to European men. Genetic susceptibility to prostate cancer could account for part of this difference. We conducted a genome-wide association study (GWAS) of prostate cancer in West African men in the Ghana Prostate Study. Association testing was performed using multivariable logistic regression adjusted for age and genetic ancestry for 474 prostate cancer cases and 458 population-based controls on the Illumina HumanOmni-5 Quad BeadChip. The most promising association was at 10p14 within an intron of a long non-coding RNA (lncRNA RP11-543F8.2) 360 kb centromeric of GATA3 (p = 1.29E-7). In sub-analyses, SNPs at 5q31.3 were associated with high Gleason score (≥7) cancers, the strongest of which was a missense SNP in PCDHA1 (rs34575154, p = 3.66E-8), and SNPs at Xq28 (rs985081, p = 8.66E-9) and 6q21 (rs2185710, p = 5.95E-8) were associated with low Gleason score (<7) cancers. We sought to validate our findings in silico in the African Ancestry Prostate Cancer GWAS Consortium, but only one SNP, at 10p14, replicated at p < 0.05. Of the 90 prostate cancer loci reported from studies of men of European, Asian or African-American ancestry, we were able to test 81 in the Ghana Prostate Study, and 10 of these replicated at p < 0.05. Further genetic studies of prostate cancer in West African men are needed to confirm our promising susceptibility loci.

Inference of historical migration rates via haplotype sharing

Summary: Pairs of individuals from a study cohort will often share long-range haplotypes identical-by-descent. Such haplotypes are transmitted from common ancestors that lived tens to hundreds of generations in the past, and they can now be efficiently detected in high-resolution genomic datasets, providing a novel source of information in several domains of genetic analysis. Recently, haplotype sharing distributions were studied in the context of demographic inference, and they were used to reconstruct recent demographic events in several populations. We here extend the framework to handle demographic models that contain multiple demes interacting through migration. We extensively test our formulation in several demographic scenarios, compare our approach with methods based on ancestry deconvolution and use this method to analyze Masai samples from the HapMap 3 dataset.

Availability: DoRIS, a Java implementation of the proposed method, and its source code are freely available at http://www.cs.columbia.edu/∼pier/doris.

Contact: itsik@cs.columbia.edu

The GenoChip: a new tool for genetic anthropology

The Genographic Project is an international effort aimed at charting human migratory history. The project is nonprofit and nonmedical, and, through its Legacy Fund, supports locally led efforts to preserve indigenous and traditional cultures. Although the first phase of the project was focused on uniparentally inherited markers on the Y-chromosome and mitochondrial DNA (mtDNA), the current phase focuses on markers from across the entire genome to obtain a more complete understanding of human genetic variation. Although many commercial arrays exist for genome-wide single-nucleotide polymorphism (SNP) genotyping, they were designed for medical genetic studies and contain medically related markers that are inappropriate for global population genetic studies. GenoChip, the Genographic Project’s new genotyping array, was designed to resolve these issues and enable higher resolution research into outstanding questions in genetic anthropology. The GenoChip includes ancestry informative markers obtained for over 450 human populations, an ancient human (Saqqaq), and two archaic hominins (Neanderthal and Denisovan) and was designed to identify all known Y-chromosome and mtDNA haplogroups. The chip was carefully vetted to avoid inclusion of medically relevant markers. To demonstrate its capabilities, we compared the F_ST distributions of GenoChip SNPs to those of two commercial arrays. Although all arrays yielded similarly shaped (inverse J) F_ST distributions, the GenoChip autosomal and X-chromosomal distributions had the highest mean F_ST, attesting to its ability to discern subpopulations. The chip performances are illustrated in a principal component analysis for 14 worldwide populations. In summary, the GenoChip is a dedicated genotyping platform for genetic anthropology. With an unprecedented number of approximately 12,000 Y-chromosomal and approximately 3,300 mtDNA SNPs and over 130,000 autosomal and X-chromosomal SNPs without any known health, medical, or phenotypic relevance, the GenoChip is a useful tool for genetic anthropology and population genetics.

Adjusting for population stratification in a fine scale with principal components and sequencing data

Population stratification is of primary interest in genetic studies to infer human evolution history and to avoid spurious findings in association testing. Although it is well studied with high-density single nucleotide polymorphisms (SNPs) in genome-wide association studies (GWASs), next-generation sequencing brings both new opportunities and challenges to uncovering population structures in finer scales. Several recent studies have noticed different confounding effects from variants of different minor allele frequencies (MAFs). In this paper, using a low-coverage sequencing dataset from the 1000 Genomes Project, we compared a popular method, principal component analysis (PCA), with a recently proposed spectral clustering technique, called spectral dimensional reduction (SDR), in detecting and adjusting for population stratification at the level of ethnic subgroups. We investigated the varying performance of adjusting for population stratification with different types and sets of variants when testing on different types of variants. One main conclusion is that principal components based on all variants or common variants were generally most effective in controlling inflations caused by population stratification; in particular, contrary to many speculations on the effectiveness of rare variants, we did not find much added value with the use of only rare variants. In addition, SDR was confirmed to be more robust than PCA, especially when applied to rare variants.

Special considerations in prognostic research in cancer involving genetic polymorphisms

Analysis of genetic polymorphisms may help identify putative prognostic markers and determine the biological basis of variable prognosis in patients. However, in contrast to other variables commonly used in the prognostic studies, there are special considerations when studying genetic polymorphisms. For example, variable inheritance patterns (recessive, dominant, codominant, and additive genetic models) need to be explored to identify the specific genotypes associated with the outcome. In addition, several characteristics of genetic polymorphisms, such as their minor allele frequency and linkage disequilibrium among multiple polymorphisms, and the population substructure of the cohort investigated need to be accounted for in the analyses. In addition, in cancer research due to the genomic differences between the tumor and non-tumor DNA, differences in the genetic information obtained using these tissues need to be carefully assessed in prognostic studies. In this article, we review these and other considerations specific to genetic polymorphism by focusing on genetic prognostic studies in cancer.

Length distributions of identity by descent reveal fine-scale demographic history

Data-driven studies of identity by descent (IBD) were recently enabled by high-resolution genomic data from large cohorts and scalable algorithms for IBD detection. Yet, haplotype sharing currently represents an underutilized source of information for population-genetics research. We present analytical results on the relationship between haplotype sharing across purportedly unrelated individuals and a population's demographic history. We express the distribution of IBD sharing across pairs of individuals for segments of arbitrary length as a function of the population's demography, and we derive an inference procedure to reconstruct such demographic history. The accuracy of the proposed reconstruction methodology was extensively tested on simulated data. We applied this methodology to two densely typed data sets: 500 Ashkenazi Jewish (AJ) individuals and 56 Kenyan Maasai (MKK) individuals (HapMap 3 data set). Reconstructing the demographic history of the AJ cohort, we recovered two subsequent population expansions, separated by a severe founder event, consistent with previous analysis of lower-throughput genetic data and historical accounts of AJ history. In the MKK cohort, high levels of cryptic relatedness were detected. The spectrum of IBD sharing is consistent with a demographic model in which several small-sized demes intermix through high migration rates and result in enrichment of shared long-range haplotypes. This scenario of historically structured demographies might explain the unexpected abundance of runs of homozygosity within several populations.

The great human expansion

Genetic and paleoanthropological evidence is in accord that today's human population is the result of a great demic (demographic and geographic) expansion that began approximately 45,000 to 60,000 y ago in Africa and rapidly resulted in human occupation of almost all of the Earth's habitable regions. Genomic data from contemporary humans suggest that this expansion was accompanied by a continuous loss of genetic diversity, a result of what is called the "serial founder effect." In addition to genomic data, the serial founder effect model is now supported by the genetics of human parasites, morphology, and linguistics. This particular population history gave rise to the two defining features of genetic variation in humans: genomes from the substructured populations of Africa retain an exceptional number of unique variants, and there is a dramatic reduction in genetic diversity within populations living outside of Africa. These two patterns are relevant for medical genetic studies mapping genotypes to phenotypes and for inferring the power of natural selection in human history. It should be appreciated that the initial expansion and subsequent serial founder effect were determined by demographic and sociocultural factors associated with hunter-gatherer populations. How do we reconcile this major demic expansion with the population stability that followed for thousands years until the inventions of agriculture? We review advances in understanding the genetic diversity within Africa and the great human expansion out of Africa and offer hypotheses that can help to establish a more synthetic view of modern human evolution.

Evolutionary history and adaptation from high-coverage whole-genome sequences of diverse African hunter-gatherers

To reconstruct modern human evolutionary history and identify loci that have shaped hunter-gatherer adaptation, we sequenced the whole genomes of five individuals in each of three different hunter-gatherer populations at > 60× coverage: Pygmies from Cameroon and Khoesan-speaking Hadza and Sandawe from Tanzania. We identify 13.4 million variants, substantially increasing the set of known human variation. We found evidence of archaic introgression in all three populations, and the distribution of time to most recent common ancestors from these regions is similar to that observed for introgressed regions in Europeans. Additionally, we identify numerous loci that harbor signatures of local adaptation, including genes involved in immunity, metabolism, olfactory and taste perception, reproduction, and wound healing. Within the Pygmy population, we identify multiple highly differentiated loci that play a role in growth and anterior pituitary function and are associated with height.

Assessing the accuracy and power of population genetic inference from low-pass next-generation sequencing data

Next-generation sequencing (NGS) technologies have made it possible to address population genetic questions in almost any system, but high error rates associated with such data can introduce significant biases into downstream analyses, necessitating careful experimental design and interpretation in studies based on short-read sequencing. Exploration of population genetic analyses based on NGS has revealed some of the potential biases, but previous work has emphasized parameters relevant to human population genetics and further examination of parameters relevant to other systems is necessary, including situations where sample sizes are small and genetic variation is high. To assess experimental power to address several principal objectives of population genetic studies under these conditions, we simulated population samples under selective sweep, population growth, and population subdivision models and tested the power to accurately infer population genetic parameters from sequence polymorphism data obtained through simulated 4×, 8×, and 15× read depth sequence data. We found that estimates of population genetic differentiation and population growth parameters were systematically biased when inference was based on 4× sequencing, but biases were markedly reduced at even 8× read depth. We also found that the power to identify footprints of positive selection depends on an interaction between read depth and the strength of selection, with strong selection being recovered consistently at all read depths, but weak selection requiring deeper read depths for reliable detection. Although we have explored only a small subset of the many possible experimental designs and population genetic models, using only one SNP-calling approach, our results reveal some general patterns and provide some assessment of what biases could be expected under similar experimental structures.

Cryptic distant relatives are common in both isolated and cosmopolitan genetic samples

Although a few hundred single nucleotide polymorphisms (SNPs) suffice to infer close familial relationships, high density genome-wide SNP data make possible the inference of more distant relationships such as 2^nd to 9^th cousinships. In order to characterize the relationship between genetic similarity and degree of kinship given a timeframe of 100–300 years, we analyzed the sharing of DNA inferred to be identical by descent (IBD) in a subset of individuals from the 23andMe customer database (n = 22,757) and from the Human Genome Diversity Panel (HGDP-CEPH, n = 952). With data from 121 populations, we show that the average amount of DNA shared IBD in most ethnolinguistically-defined populations, for example Native American groups, Finns and Ashkenazi Jews, differs from continentally-defined populations by several orders of magnitude. Via extensive pedigree-based simulations, we determined bounds for predicted degrees of relationship given the amount of genomic IBD sharing in both endogamous and ‘unrelated’ population samples. Using these bounds as a guide, we detected tens of thousands of 2^nd to 9^th degree cousin pairs within a heterogenous set of 5,000 Europeans. The ubiquity of distant relatives, detected via IBD segments, in both ethnolinguistic populations and in large ‘unrelated’ populations samples has important implications for genetic genealogy, forensics and genotype/phenotype mapping studies.

Manifold learning for human population structure studies

The dimension of the population genetics data produced by next-generation sequencing platforms is extremely high. However, the "intrinsic dimensionality" of sequence data, which determines the structure of populations, is much lower. This motivates us to use locally linear embedding (LLE) which projects high dimensional genomic data into low dimensional, neighborhood preserving embedding, as a general framework for population structure and historical inference. To facilitate application of the LLE to population genetic analysis, we systematically investigate several important properties of the LLE and reveal the connection between the LLE and principal component analysis (PCA). Identifying a set of markers and genomic regions which could be used for population structure analysis will provide invaluable information for population genetics and association studies. In addition to identifying the LLE-correlated or PCA-correlated structure informative marker, we have developed a new statistic that integrates genomic information content in a genomic region for collectively studying its association with the population structure and LASSO algorithm to search such regions across the genomes. We applied the developed methodologies to a low coverage pilot dataset in the 1000 Genomes Project and a PHASE III Mexico dataset of the HapMap. We observed that 25.1%, 44.9% and 21.4% of the common variants and 89.2%, 92.4% and 75.1% of the rare variants were the LLE-correlated markers in CEU, YRI and ASI, respectively. This showed that rare variants, which are often private to specific populations, have much higher power to identify population substructure than common variants. The preliminary results demonstrated that next generation sequencing offers a rich resources and LLE provide a powerful tool for population structure analysis.

Sequencing technologies and genome sequencing

The high-throughput - next generation sequencing (HT-NGS) technologies are currently the hottest topic in the field of human and animals genomics researches, which can produce over 100 times more data compared to the most sophisticated capillary sequencers based on the Sanger method. With the ongoing developments of high throughput sequencing machines and advancement of modern bioinformatics tools at unprecedented pace, the target goal of sequencing individual genomes of living organism at a cost of $1,000 each is seemed to be realistically feasible in the near future. In the relatively short time frame since 2005, the HT-NGS technologies are revolutionizing the human and animal genome researches by analysis of chromatin immunoprecipitation coupled to DNA microarray (ChIP-chip) or sequencing (ChIP-seq), RNA sequencing (RNA-seq), whole genome genotyping, genome wide structural variation, de novo assembling and re-assembling of genome, mutation detection and carrier screening, detection of inherited disorders and complex human diseases, DNA library preparation, paired ends and genomic captures, sequencing of mitochondrial genome and personal genomics. In this review, we addressed the important features of HT-NGS like, first generation DNA sequencers, birth of HT-NGS, second generation HT-NGS platforms, third generation HT-NGS platforms: including single molecule Heliscope™, SMRT™ and RNAP sequencers, Nanopore, Archon Genomics X PRIZE foundation, comparison of second and third HT-NGS platforms, applications, advances and future perspectives of sequencing technologies on human and animal genome research.