Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

CorrectionsBIOCHEMISTRY. For the article ''Interaction of RNA polymerase with forked DNA: Evidence for two kinetically significant intermediates on the pathway to the final complex,'' by Laura Tsujikawa, Oleg V. Tsodikov, and Pieter L. deHaseth, which appeared in number 6, March 19, 2002, of Proc. Natl. Acad. Sci. USA (99, 3493-3498; First Published March 12, 2002; 10.1073͞ pnas.062487299), the authors note the following concerning RNA polymerase (RNAP) concentrations. No correction was made for the fraction of RNAP (0.5) that is active in promoter binding. With this correction, the values of K 1 and K app (but not K f ) would increase by about a factor of 2. The relative values would remain essentially unchanged. Also, the legends to Figs. 2, 3, and 5 contain errors pertaining to the symbols used for data obtained with and without heparin challenge, the duration of the challenge, and the concentration of added heparin. The figures and the corrected legends appear below. Fig. 2. Determination of equilibrium affinities by titration of wt Fork with RNAP. The reactions contained 1 nM wt Fork and variable amounts of RNAP as shown and were analyzed by electrophoretic mobility shift immediately (OE; data shown are averages of three independent experiments) or after a challenge with 100 g͞ml heparin for 10 min (F; data shown are averages of four independent experiments). The curves shown reflect the simultaneous errorweighted fits of the data to Eqs. 3 and 4 -7. The parameters are shown in Table 1 (line 1). www.pnas.org͞cgi͞doi͞10.1073͞pnas.013667699 Fig. 3. Kinetics of complex formation. RNAP (65 nM) and wt forked DNA (1 nM) were incubated for various time intervals and then complex formation was determined immediately (Ϫheparin) or after a 2-min challenge with 100 g͞ml heparin (ϩheparin). The Ϫheparin data (s) were fit (error-weighted) with Eq. 8 with a 2 ϭ 0 (kaϪ ϭ 0.10 Ϯ 0.01 s Ϫ1 ) and the ϩheparin data (OE) with both single (k aϩ ϭ 0.036 Ϯ 0.004 s Ϫ1 ; thin line) and double-exponential (ka 1 ϭ 0.044 Ϯ 0.002 s Ϫ1 ; ka 2 ϭ (5 Ϯ 3) ϫ 10 Ϫ4 s Ϫ1 ; thick line) equations. Fig. 5.Comparison of the kinetics for formation and dissociation of competitor-resistant complexes between RNAP and wt Fork. Association data were obtained as described in the text and the legend for Fig. 3 except the concentration of forked DNA was 10 nM. Dissociation kinetics were obtained by challenging with 100 g͞ml heparin a mixture of RNAP and forked DNA that had been incubated for 30 min. The curves represent double-exponential fits of the data to Eq. 10. (A) wt RNAP. The observed association rate constants (s) are shown in the legend for Fig. 3; for the slow phase of the dissociation of the wt Fork-wt RNAP complex (F), kd 2 ϭ (1.3 Ϯ 0.2) ϫ 10 Ϫ4 s Ϫ1 . (B) YYW RNAP. The slow phase of the association reaction (F) has a ka 2 ϭ (1.1 Ϯ 0.3) ϫ 10 Ϫ3 s Ϫ1 ; the slow phase of the dissociation reaction (s), a kd 2 ϭ (6 Ϯ 1) ϫ 10 Ϫ4 s Ϫ1 . Fig. 6. BCL-6 preferentially binds to the wild-type exon 1 in Ly1 cells. Both Ly1 and the control Ly7 cells wer...

Mitochondrial DNA (mtDNA)-depletion syndromes (MDS; OMIM 251880) are phenotypically heterogeneous, autosomal-recessive disorders characterized by tissue-specific reduction in mtDNA copy number. Affected individuals with the hepatocerebral form of MDS have early progressive liver failure and neurological abnormalities, hypoglycemia and increased lactate in body fluids. Affected tissues show both decreased activity of the mtDNA-encoded respiratory chain complexes (I, III, IV, V) and mtDNA depletion. We used homozygosity mapping in three kindreds of Druze origin to map the gene causing hepatocerebral MDS to a region of 6.1 cM on chromosome 2p13, between markers D2S291 and D2S2116. This interval encompasses the gene (DGUOK) encoding the mitochondrial deoxyguanosine kinase (dGK). We identified a single-nucleotide deletion (204delA) within the coding region of DGUOK that segregates with the disease in the three kindreds studied. Western-blot analysis did not detect dGK protein in the liver of affected individuals. The main supply of deoxyribonucleotides (dNTPs) for mtDNA synthesis comes from the salvage pathway initiated by dGK and thymidine kinase-2 (TK2). The association of mtDNA depletion with mutated DGUOK suggests that the salvage-pathway enzymes are involved in the maintenance of balanced mitochondrial dNTP pools.

Schizophrenia and bipolar disorder are two distinct diagnoses that share symptomology. Understanding the genetic factors contributing to the shared and disorder-specific symptoms will be crucial for improving diagnosis and treatment. In genetic data consisting of 53,555 cases (20,129 bipolar disorder [BD], 33,426 schizophrenia [SCZ]) and 54,065 controls, we identified 114 genome-wide significant loci implicating synaptic and neuronal pathways shared between disorders. Comparing SCZ to BD (23,585 SCZ, 15,270 BD) identified four genomic regions including one with disorder-independent causal variants and potassium ion response genes as contributing to differences in biology between the disorders. Polygenic risk score (PRS) analyses identified several significant correlations within case-only phenotypes including SCZ PRS with psychotic features and age of onset in BD. For the first time, we discover specific loci that distinguish between BD and SCZ and identify polygenic components underlying multiple symptom dimensions. These results point to the utility of genetics to inform symptomology and potential treatment.

There is genetic predisposition associated with >=10% of all cancer of the prostate (CaP). By means of a genomewide search on a selection of 47 French and German families, parametric and nonparametric linkage (NPL) analysis allowed identification of a locus, on chromosome 1q42.2-43, carrying a putative predisposing gene for CaP (PCaP). The primary localization was confirmed with several markers, by use of three different genetic models. We obtained a maximum two-point LOD score of 2.7 with marker D1S2785. Multipoint parametric and NPL analysis yielded maximum HLOD and NPL scores of 2.2 and 3.1, respectively, with an associated P value of . 001. Homogeneity analysis with multipoint LOD scores gave an estimate of the proportion of families with linkage to this locus of 50%, with a likelihood ratio of 157/1 in favor of heterogeneity. Furthermore, the 9/47 families with early-onset CaP at age <60 years gave multipoint LOD and NPL scores of 3.31 and 3.32, respectively, with P = .001.