Honors & Awards


  • Postdoctoral Fellow, Stanford-SJSU NIH IRACDA (2016)
  • Postdoctoral Fellow, Stanford CEHG (2015)
  • Discovery Fellow, UCSF (2014)

Professional Education


  • Bachelor of Arts, Carleton College, Physics (2005)
  • Master of Science, University of Chicago, Biophysical Sciences (2009)
  • Master of Science, University of Chicago, Computer Science Professional Program (2011)
  • Doctor of Philosophy, University of California San Francisco (2014)

Stanford Advisors


Current Research and Scholarly Interests


Lawrence studies the evolution of complex traits under selection, the joint impact of selection and demography on genetic variation and genetic architecture, how linked selection alters patterns of genetic variation in humans, and how competitive processes between species shape coexistence and exclusion.

All Publications


  • An analytical upper bound on the number of loci required for all splits of a species tree to appear in a set of gene trees BMC BIOINFORMATICS Uricchio, L. H., Warnow, T., Rosenberg, N. A. 2016; 17

    Abstract

    Many methods for species tree inference require data from a sufficiently large sample of genomic loci in order to produce accurate estimates. However, few studies have attempted to use analytical theory to quantify "sufficiently large".Using the multispecies coalescent model, we report a general analytical upper bound on the number of gene trees n required such that with probability q, each bipartition of a species tree is represented at least once in a set of n random gene trees. This bound employs a formula that is straightforward to compute, depends only on the minimum internal branch length of the species tree and the number of taxa, and applies irrespective of the species tree topology. Using simulations, we investigate numerical properties of the bound as well as its accuracy under the multispecies coalescent.Our results are helpful for conservatively bounding the number of gene trees required by the ASTRAL inference method, and the approach has potential to be extended to bound other properties of gene tree sets under the model.

    View details for DOI 10.1186/s12859-016-1266-4

    View details for Web of Science ID 000392515100007

    View details for PubMedID 28185570

  • Selection and explosive growth alter genetic architecture and hamper the detection of causal rare variants GENOME RESEARCH Uricchio, L. H., Zaitlen, N. A., Ye, C. J., Witte, J. S., Hernandez, R. D. 2016; 26 (7): 863-873

    Abstract

    The role of rare alleles in complex phenotypes has been hotly debated, but most rare variant association tests (RVATs) do not account for the evolutionary forces that affect genetic architecture. Here, we use simulation and numerical algorithms to show that explosive population growth, as experienced by human populations, can dramatically increase the impact of very rare alleles on trait variance. We then assess the ability of RVATs to detect causal loci using simulations and human RNA-seq data. Surprisingly, we find that statistical performance is worst for phenotypes in which genetic variance is due mainly to rare alleles, and explosive population growth decreases power. Although many studies have attempted to identify causal rare variants, few have reported novel associations. This has sometimes been interpreted to mean that rare variants make negligible contributions to complex trait heritability. Our work shows that RVATs are not robust to realistic human evolutionary forces, so general conclusions about the impact of rare variants on complex traits may be premature.

    View details for DOI 10.1101/gr.202440.115

    View details for Web of Science ID 000378986000001

    View details for PubMedID 27197206

  • An analytical upper bound on the number of loci required for all splits of a species tree to appear in a set of gene trees BMC Bioinformatics Uricchio, L. H., Warnow, T., Rosenberg, N. A. 2016; 17: 241-250
  • Population Genetic Simulations of Complex Phenotypes with Implications for Rare Variant Association Tests GENETIC EPIDEMIOLOGY Uricchio, L. H., Torres, R., Witte, J. S., Hernandez, R. D. 2015; 39 (1): 35-44

    Abstract

    Demographic events and natural selection alter patterns of genetic variation within populations and may play a substantial role in shaping the genetic architecture of complex phenotypes and disease. However, the joint impact of these basic evolutionary forces is often ignored in the assessment of statistical tests of association. Here, we provide a simulation-based framework for generating DNA sequences that incorporates selection and demography with flexible models for simulating phenotypic variation (sfs_coder). This tool also allows the user to perform locus-specific simulations by automatically querying annotated genomic functional elements and genetic maps. We demonstrate the effects of evolutionary forces on patterns of genetic variation by simulating recently inferred models of human selection and demography. We use these simulations to show that the demographic model and locus-specific features, such as the proportion of sites under selection, may have practical implications for estimating the statistical power of sequencing-based rare variant association tests. In particular, for some phenotype models, there may be higher power to detect rare variant associations in African populations compared to non-Africans, but power is considerably reduced in regions of the genome with rampant negative selection. Furthermore, we show that existing methods for simulating large samples based on resampling from a small set of observed haplotypes fail to recapitulate the distribution of rare variants in the presence of rapid population growth (as has been observed in several human populations).

    View details for DOI 10.1002/gepi.21866

    View details for Web of Science ID 000346469900006

    View details for PubMedID 25417809

  • Robust Forward Simulations of Recurrent Hitchhiking GENETICS Uricchio, L. H., Hernandez, R. D. 2014; 197 (1): 221-236

    Abstract

    Evolutionary forces shape patterns of genetic diversity within populations and contribute to phenotypic variation. In particular, recurrent positive selection has attracted significant interest in both theoretical and empirical studies. However, most existing theoretical models of recurrent positive selection cannot easily incorporate realistic confounding effects such as interference between selected sites, arbitrary selection schemes, and complicated demographic processes. It is possible to quantify the effects of arbitrarily complex evolutionary models by performing forward population genetic simulations, but forward simulations can be computationally prohibitive for large population sizes (>10(5)). A common approach for overcoming these computational limitations is rescaling of the most computationally expensive parameters, especially population size. Here, we show that ad hoc approaches to parameter rescaling under the recurrent hitchhiking model do not always provide sufficiently accurate dynamics, potentially skewing patterns of diversity in simulated DNA sequences. We derive an extension of the recurrent hitchhiking model that is appropriate for strong selection in small population sizes and use it to develop a method for parameter rescaling that provides the best possible computational performance for a given error tolerance. We perform a detailed theoretical analysis of the robustness of rescaling across the parameter space. Finally, we apply our rescaling algorithms to parameters that were previously inferred for Drosophila and discuss practical considerations such as interference between selected sites.

    View details for DOI 10.1534/genetics.113.156935

    View details for Web of Science ID 000335858900017

    View details for PubMedID 24561480

  • Accurate Imputation of Rare and Common Variants in a Founder Population From a Small Number of Sequenced Individuals GENETIC EPIDEMIOLOGY Uricchio, L. H., Chong, J. X., Ross, K. D., Ober, C., Nicolae, D. L. 2012; 36 (4): 312-319

    Abstract

    Advances in DNA sequencing technologies have greatly facilitated the discovery of rare genetic variants in the human genome, many of which may contribute to common disease risk. However, evaluating their individual or even collective effects on disease risk requires very large sample sizes, which involves study designs that are often prohibitively expensive. We present an alternative approach for determining genotypes in large numbers of individuals for all variants discovered in the sequence of relatively few individuals. Specifically, we developed a new imputation algorithm that utilizes whole-exome sequencing data from 25 members of the South Dakota Hutterite population, and genome-wide single nucleotide polymorphism (SNP) genotypes from >1,400 individuals from the same founder population. The algorithm relies on identity-by-descent sharing of phased haplotypes, a different strategy than the linkage disequilibrium methods found in most imputation algorithms. We imputed genotypes discovered in the sequence data to on average ∼77% of chromosomes among the 1,400 individuals. Median R(2) between imputed and directly genotyped data was >0.99. As expected, many variants that are vanishingly rare in European populations have risen to larger frequencies in the founder population and would be amenable to single-SNP analyses.

    View details for DOI 10.1002/gepi.21623

    View details for Web of Science ID 000303319900003

    View details for PubMedID 22460724

  • Population Genetics of Rare Variants and Complex Diseases HUMAN HEREDITY Maher, M. C., Uricchio, L. H., Torgerson, D. G., Hernandez, R. D. 2012; 74 (3-4): 118-128

    Abstract

    Identifying drivers of complex traits from the noisy signals of genetic variation obtained from high-throughput genome sequencing technologies is a central challenge faced by human geneticists today. We hypothesize that the variants involved in complex diseases are likely to exhibit non-neutral evolutionary signatures. Uncovering the evolutionary history of all variants is therefore of intrinsic interest for complex disease research. However, doing so necessitates the simultaneous elucidation of the targets of natural selection and population-specific demographic history.Here we characterize the action of natural selection operating across complex disease categories, and use population genetic simulations to evaluate the expected patterns of genetic variation in large samples. We focus on populations that have experienced historical bottlenecks followed by explosive growth (consistent with many human populations), and describe the differences between evolutionarily deleterious mutations and those that are neutral.Genes associated with several complex disease categories exhibit stronger signatures of purifying selection than non-disease genes. In addition, loci identified through genome-wide association studies of complex traits also exhibit signatures consistent with being in regions recurrently targeted by purifying selection. Through simulations, we show that population bottlenecks and rapid growth enable deleterious rare variants to persist at low frequencies just as long as neutral variants, but low-frequency and common variants tend to be much younger than neutral variants. This has resulted in a large proportion of modern-day rare alleles that have a deleterious effect on function and that potentially contribute to disease susceptibility.The key question for sequencing-based association studies of complex traits is how to distinguish between deleterious and benign genetic variation. We used population genetic simulations to uncover patterns of genetic variation that distinguish these two categories, especially derived allele age, thereby providing inroads into novel methods for characterizing rare genetic variation driving complex diseases.

    View details for DOI 10.1159/000346826

    View details for Web of Science ID 000317569400002

    View details for PubMedID 23594490

  • Exome sequencing reveals a novel mutation for autosomal recessive non-syndromic mental retardation in the TECR gene on chromosome 19p13 HUMAN MOLECULAR GENETICS Caliskan, M., Chong, J. X., Uricchio, L., Anderson, R., Chen, P., Sougnez, C., Garimella, K., Gabriel, S. B., DePristo, M. A., Shakir, K., Matern, D., Das, S., Waggoner, D., Nicolae, D. L., Ober, C. 2011; 20 (7): 1285-1289

    Abstract

    Exome sequencing is a powerful tool for discovery of the Mendelian disease genes. Previously, we reported a novel locus for autosomal recessive non-syndromic mental retardation (NSMR) in a consanguineous family [Nolan, D.K., Chen, P., Das, S., Ober, C. and Waggoner, D. (2008) Fine mapping of a locus for nonsyndromic mental retardation on chromosome 19p13. Am. J. Med. Genet. A, 146A, 1414-1422]. Using linkage and homozygosity mapping, we previously localized the gene to chromosome 19p13. The parents of this sibship were recently included in an exome sequencing project. Using a series of filters, we narrowed the putative causal mutation to a single variant site that segregated with NSMR: the mutation was homozygous in five affected siblings but in none of eight unaffected siblings. This mutation causes a substitution of a leucine for a highly conserved proline at amino acid 182 in TECR (trans-2,3-enoyl-CoA reductase), a synaptic glycoprotein. Our results reveal the value of massively parallel sequencing for identification of novel disease genes that could not be found using traditional approaches and identifies only the seventh causal mutation for autosomal recessive NSMR.

    View details for DOI 10.1093/hmg/ddq569

    View details for Web of Science ID 000288279300004

    View details for PubMedID 21212097

  • Inflammasome-mediated production of IL-1 beta is required for neutrophil recruitment against Staphylococcus aureus in vivo JOURNAL OF IMMUNOLOGY Miller, L. S., Pietras, E. M., Uricchio, L. H., Hirano, K., Rao, S., Lin, H., O'Connell, R. M., Iwakura, Y., Cheung, A. L., Cheng, G., Modlin, R. L. 2007; 179 (10): 6933-6942

    Abstract

    IL-1R activation is required for neutrophil recruitment in an effective innate immune response against Staphylococcus aureus infection. In this study, we investigated the mechanism of IL-1R activation in vivo in a model of S. aureus infection. In response to a S. aureus cutaneous challenge, mice deficient in IL-1beta, IL-1alpha/IL-1beta, but not IL-1alpha, developed larger lesions with higher bacterial counts and had decreased neutrophil recruitment compared with wild-type mice. Neutrophil recruitment and bacterial clearance required IL-1beta expression by bone marrow (BM)-derived cells and not by non-BM-derived resident cells. In addition, mice deficient in the inflammasome component apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC) had the same defects in neutrophil recruitment and host defense as IL-1beta-deficient mice, demonstrating an essential role for the inflammasome in mediating the production of active IL-1beta to promote neutrophil recruitment in host defense against S. aureus. This finding was further supported by the ability of recombinant active IL-1beta to control the infection and promote bacterial clearance in IL-1beta-deficient mice. These studies define a key host defense circuit where inflammasome-mediated IL-1beta production by BM-derived cells signals IL-1R on non-BM-derived resident cells to activate neutrophil recruitment in the innate immune response against S. aureus in vivo.

    View details for Web of Science ID 000250792700062

    View details for PubMedID 17982084

  • Coarse-grained entropy decrease and phase-space focusing in Hamiltonian dynamics PHYSICAL REVIEW A Pattanayak, A. K., Brooks, D. W., de la Fuente, A., Uricchio, L., Holby, E., Krawisz, D., Silva, J. I. 2005; 72 (1)