Fixing Rule 702: The PCAST Report and Steps to Ensure the Reliability of Forensic Feature-Comparison Methods in the Criminal Courts

In response to PCAST’s recommendation, the Standing Advisory Committee on Evidence Rules convened a meeting on forensic expert testimony, Daubert, and Rule 702 on October 27, 2017, at Boston College Law School to inform itself about the issues.22 The meeting included presentations by twenty-six speakers (including myself) and discussion among the attendees. The purpose of this Article is to summarize aspects of the PCAST report relevant to its recommendation to the Standing Advisory Committee on Evidence Rules and to propose a path forward with respect to Rule 702

The distribution of clusters in random graphs

AbstractGiven a random graph, we investigate the occurrence of subgraphs especially rich in edges. Specifically, given a ϵ [0,1], a set of k points in a graph G is defined to be an a-cluster of cardinality k if the induced subgraph contains at least ak2 edges, so that in the extreme case a = 1, an a-cluster is the same as a clique. We let G = G(n, p) be a random graph on n vertices with edges chosen independently with probability p. Let W denote the number of a-clusters of cardinality k in G, where k and n tend to infinity so that the expected number λ of a-clusters of cardinality k does not grow or decay too rapidly. We prove that W is asymptotically distributed as Zλ, whose distribution is Poisson with mean λ, which is the same result that Bollobás and Erdös have proved for cliques. In contrast to the situation for cliques (a = 1) however, for all a < 1 the second moment of W blows up, i.e., the expected number of neighbors of a given cluster tends to infinity. Nevertheless, the probability that there exists at least one pair of neighboring clusters tends to zero, and a Poisson approximation for W is valid

Ribosome Profiling Provides Evidence that Large Noncoding RNAs Do Not Encode Proteins

Large noncoding RNAs are emerging as an important component in cellular regulation. Considerable evidence indicates that these transcripts act directly as functional RNAs rather than through an encoded protein product. However, a recent study of ribosome occupancy reported that many large intergenic ncRNAs (lincRNAs) are bound by ribosomes, raising the possibility that they are translated into proteins. Here, we show that classical noncoding RNAs and 5′ UTRs show the same ribosome occupancy as lincRNAs, demonstrating that ribosome occupancy alone is not sufficient to classify transcripts as coding or noncoding. Instead, we define a metric based on the known property of translation whereby translating ribosomes are released upon encountering a bona fide stop codon. We show that this metric accurately discriminates between protein-coding transcripts and all classes of known noncoding transcripts, including lincRNAs. Taken together, these results argue that the large majority of lincRNAs do not function through encoded proteins

Subtype-specific genomic alterations define new targets for soft tissue sarcoma therapy

2011 February 1Soft-tissue sarcomas, which result in approximately 10,700 diagnoses and 3,800 deaths per year in the United States1, show remarkable histologic diversity, with more than 50 recognized subtypes2. However, knowledge of their genomic alterations is limited. We describe an integrative analysis of DNA sequence, copy number and mRNA expression in 207 samples encompassing seven major subtypes. Frequently mutated genes included TP53 (17% of pleomorphic liposarcomas), NF1 (10.5% of myxofibrosarcomas and 8% of pleomorphic liposarcomas) and PIK3CA (18% of myxoid/round-cell liposarcomas, or MRCs). PIK3CA mutations in MRCs were associated with Akt activation and poor clinical outcomes. In myxofibrosarcomas and pleomorphic liposarcomas, we found both point mutations and genomic deletions affecting the tumor suppressor NF1. Finally, we found that short hairpin RNA (shRNA)-based knockdown of several genes amplified in dedifferentiated liposarcoma, including CDK4 and YEATS4, decreased cell proliferation. Our study yields a detailed map of molecular alterations across diverse sarcoma subtypes and suggests potential subtype-specific targets for therapy.Memorial Sloan-Kettering Cancer Center (Soft Tissue Sarcoma Program Project P01 CA047179

Measuring missing heritability: Inferring the contribution of common variants

Genome-wide association studies (GWASs), also called common variant association studies (CVASs), have uncovered thousands of genetic variants associated with hundreds of diseases. However, the variants that reach statistical significance typically explain only a small fraction of the heritability. One explanation for the “missing heritability” is that there are many additional disease-associated common variants whose effects are too small to detect with current sample sizes. It therefore is useful to have methods to quantify the heritability due to common variation, without having to identify all causal variants. Recent studies applied restricted maximum likelihood (REML) estimation to case–control studies for diseases. Here, we show that REML considerably underestimates the fraction of heritability due to common variation in this setting. The degree of underestimation increases with the rarity of disease, the heritability of the disease, and the size of the sample. Instead, we develop a general framework for heritability estimation, called phenotype correlation–genotype correlation (PCGC) regression, which generalizes the well-known Haseman–Elston regression method. We show that PCGC regression yields unbiased estimates. Applying PCGC regression to six diseases, we estimate the proportion of the phenotypic variance due to common variants to range from 25% to 56% and the proportion of heritability due to common variants from 41% to 68% (mean 60%). These results suggest that common variants may explain at least half the heritability for many diseases. PCGC regression also is readily applicable to other settings, including analyzing extreme-phenotype studies and adjusting for covariates such as sex, age, and population structure.National Institutes of Health (U.S.) (NIH HG003067)Broad Institute of MIT and Harvar

Restriction Fragment Length Polymorphism Linkage Map for Arabidopsis thaliana

We have constructed a restriction fragment length polymorphism linkage map for the nuclear genome of the flowering plant Arabidopsis thaliana. The map, containing 90 randomly distributed molecular markers, is physically very dense; >50% of the genome is within 1.9 centimorgans, or approx 270 kilobase pairs, of the mapped DNA fragments. The map was based on the meiotic segregation of markers in two different crosses. The restriction fragment length polymorphism linkage groups were integrated with the five classically mapped linkage groups by virtue of mapped mutations included in these crosses. Markers consist of both cloned Arabidopsis genes and random low-copy-number genomic DNA clones that are able to detect polymorphisms with the restriction enzymes EcoRI, Bgl II, and/or Xba I. These cloned markers can serve as starting points for chromosome walking, allowing for the isolation of Arabidopsis genes of known map location. The restriction fragment length polymorphism map also can associate clones of unknown gene function with mutant phenotypes, and vice versa

Position specific variation in the rate of evolution in transcription factor binding sites

BACKGROUND: The binding sites of sequence specific transcription factors are an important and relatively well-understood class of functional non-coding DNAs. Although a wide variety of experimental and computational methods have been developed to characterize transcription factor binding sites, they remain difficult to identify. Comparison of non-coding DNA from related species has shown considerable promise in identifying these functional non-coding sequences, even though relatively little is known about their evolution. RESULTS: Here we analyse the genome sequences of the budding yeasts Saccharomyces cerevisiae, S. bayanus, S. paradoxus and S. mikatae to study the evolution of transcription factor binding sites. As expected, we find that both experimentally characterized and computationally predicted binding sites evolve slower than surrounding sequence, consistent with the hypothesis that they are under purifying selection. We also observe position-specific variation in the rate of evolution within binding sites. We find that the position-specific rate of evolution is positively correlated with degeneracy among binding sites within S. cerevisiae. We test theoretical predictions for the rate of evolution at positions where the base frequencies deviate from background due to purifying selection and find reasonable agreement with the observed rates of evolution. Finally, we show how the evolutionary characteristics of real binding motifs can be used to distinguish them from artefacts of computational motif finding algorithms. CONCLUSION: As has been observed for protein sequences, the rate of evolution in transcription factor binding sites varies with position, suggesting that some regions are under stronger functional constraint than others. This variation likely reflects the varying importance of different positions in the formation of the protein-DNA complex. The characterization of the pattern of evolution in known binding sites will likely contribute to the effective use of comparative sequence data in the identification of transcription factor binding sites and is an important step toward understanding the evolution of functional non-coding DNA