Mark D. Shriver

Bio: Mark D. Shriver is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Population & Genetic admixture. The author has an hindex of 73, co-authored 198 publications receiving 28674 citations. Previous affiliations of Mark D. Shriver include University of Toronto & University of Pittsburgh.

A global reference for human genetic variation.

Abstract: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

A global reference for human genetic variation

Abstract: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

SLC24A5, a Putative Cation Exchanger, Affects Pigmentation in Zebrafish and Humans

Abstract: Lighter variations of pigmentation in humans are associated with diminished number, size, and density of melanosomes, the pigmented organelles of melanocytes. Here we show that zebrafish golden mutants share these melanosomal changes and that golden encodes a putative cation exchanger slc24a5 (nckx5) that localizes to an intracellular membrane, likely the melanosome or its precursor. The human ortholog is highly similar in sequence and functional in zebrafish. The evolutionarily conserved ancestral allele of a human coding polymorphism predominates in African and East Asian populations. In contrast, the variant allele is nearly fixed in European populations, is associated with a substantial reduction in regional heterozygosity, and correlates with lighter skin pigmentation in admixed populations, suggesting a key role for the SLC24A5 gene in human pigmentation.

Interrogating a High-Density SNP Map for Signatures of Natural Selection

Abstract: Natural selection, which can be defined as the differential contribution of genetic variants to future generations (Aquadro et al. 2001), is the driving force of Darwinian evolution. Despite intense research, only a relatively small number of regions and genes have been directly implicated as targets of selection in the human genome (Kitano and Saitou 1999; Rana et al. 1999; Huttley et al. 2000; Hollox et al. 2001; Hull et al. 2001; Hurst and Pal 2001; Koda et al. 2001; Sullivan et al. 2001; Tishkoff et al. 2001; Baum et al. 2002; Fullerton et al. 2002; Gilad et al. 2002; Hamblin et al. 2002). A more comprehensive and genomic understanding of how and where natural selection has shaped patterns of genetic variation may provide important insights into the mechanisms of evolutionary change (Otto 2000), guide selection of loci for inclusion in population genetic studies (Vitalis et al. 2001), facilitate the annotation of functionally significant genomic regions (Nielsen 2001), and help elucidate genotype-phenotype correlations in complex diseases (Przeworski et al. 2000; Nielsen 2001). Detecting unambiguous evidence for natural selection remains challenging because the effect of selection on the distribution of genetic variation can be mimicked by population demographic history (i.e., the size, structure, and mating pattern of a population). For instance, both adaptive hitchhiking and population expansion can cause an excess of rare variants observed in DNA sequence data compared with what is expected under a standard neutral model (Tajima 1989; Przeworski et al. 2000). Despite these difficulties, the recent deluge of publicly available single nucleotide polymorphisms (SNPs) provides an exciting opportunity to identify genome-wide signatures of selection (Sunyaev et al. 2000; Fay et al. 2001; Sachidanandam et al. 2001). To this end, examining the variation in SNP allele frequencies between populations, which can be quantified by the statistic FST, is a promising strategy for detecting signatures of natural selection (Lewontin and Krakauer 1973; Rana et al. 1999; Hollox et al. 2001; Fullerton et al. 2002; Gilad et al. 2002; Hamblin et al. 2002). Under selective neutrality, FST is determined by genetic drift, which will affect all loci across the genome in a similar and predictable fashion. On the other hand, natural selection is a locus-specific force that can cause systematic deviations in FST values for a selected gene and nearby genetic markers. For example, geographically restricted directional selection may lead to an increase in FST of a selected locus, whereas balancing or species-wide directional selection may lead to a decrease in FST compared with neutrally evolving loci (Cavalli-Sforza 1966; Bowcock et al. 1991; Andolfatto 2001). Previous studies that have attempted to identify natural selection based on patterns of population differentiation relied on simulations to obtain the expected distribution of FST under selective neutrality (Lewontin and Krakauer 1973; Bowcock et al. 1991; Beaumont and Nichols 1996). However, the simulated distribution of FST strongly depends on the assumed population demographic history, which is rarely known with any degree of certainty. As an expanding number of SNPs are genotyped across multiple populations, a complimentary approach that does not require tenuous assumptions about population demographic history is now becoming feasible. Specifically, by sampling a large number of SNPs throughout the genome, loci that have been affected by natural selection can simply be identified as outliers in the extreme tails of the empirical distribution of FST (Cavalli-Sforza 1966; Black et al. 2001; Goldstein and Chikhi 2002). Recently, this strategy has been used to infer natural selection in the CAPN10 gene; however, the empirical distribution of FST contained <100 loci (Fullerton et al. 2002). In this work, we describe an analysis of 26,530 SNPs with allele frequencies that were determined in three populations: African-American, East Asian, and European-American. The density of this SNP allele frequency map provides a unique and powerful opportunity to interrogate the genome for signatures of natural selection. Through a variety of analyses, we have found statistically significant evidence supporting the hypothesis that selection has influenced extant patterns of human genetic variation. Furthermore, we have identified 174 candidate genes that demonstrate signatures of selection when contrasted to the empirical genome-wide distribution of FST. This analysis provides the conceptual foundation for constructing a high-resolution natural selection map, which will be an important resource in understanding the recent evolutionary history of our species, and will facilitate detailed studies on the identified candidate genes.

Estimating African American admixture proportions by use of population-specific alleles.

Abstract: We analyzed the European genetic contribution to 10 populations of African descent in the United States (Maywood, Illinois; Detroit; New York; Philadelphia; Pittsburgh; Baltimore; Charleston, South Carolina; New Orleans; and Houston) and in Jamaica, using nine autosomal DNA markers. These markers either are population-specific or show frequency differences >45% between the parental populations and are thus especially informative for admixture. European genetic ancestry ranged from 6.8% (Jamaica) to 22.5% (New Orleans). The unique utility of these markers is reflected in the low variance associated with these admixture estimates (SEM 1.3%-2.7%). We also estimated the male and female European contribution to African Americans, on the basis of informative mtDNA (haplogroups H and L) and Y Alu polymorphic markers. Results indicate a sex-biased gene flow from Europeans, the male contribution being substantially greater than the female contribution. mtDNA haplogroups analysis shows no evidence of a significant maternal Amerindian contribution to any of the 10 populations. We detected significant nonrandom association between two markers located 22 cM apart (FY-null and AT3), most likely due to admixture linkage disequilibrium created in the interbreeding of the two parental populations. The strength of this association and the substantial genetic distance between FY and AT3 emphasize the importance of admixed populations as a useful resource for mapping traits with different prevalence in two parental populations.

Inference of population structure using multilocus genotype data

Abstract: We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci— e.g. , seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from http://www.stats.ox.ac.uk/~pritch/home.html.

A global reference for human genetic variation.

Abstract: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

Human biochemical genetics

Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Analysis of protein-coding genetic variation in 60,706 humans

Abstract: Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC). This catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We have used this catalogue to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human 'knockout' variants in protein-coding genes.