diff --git a/submissions/bdgenomics/scripts/1_integration.scala b/submissions/bdgenomics/scripts/1_integration.scala
new file mode 100644
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--- /dev/null
+++ b/submissions/bdgenomics/scripts/1_integration.scala
@@ -0,0 +1,168 @@
+/**
+ *
+ */
+val dataDir = "/data/variant_db/1"
+val relatednessFile = "README.sample_cryptic_relations"
+val populationsFile = "phase1_integrated_calls.20101123.ALL.panel"
+val vcfFile = "1KG.chr22.anno.infocol.vcf"
+
+import org.apache.hadoop.fs.{ FileSystem, Path }
+import org.apache.spark.rdd.MetricsContext._
+import org.bdgenomics.adam.models.ReferenceRegion
+import org.bdgenomics.adam.rdd.ADAMContext._
+import org.bdgenomics.formats.avro.GenotypeAllele
+import org.bdgenomics.utils.instrumentation.Metrics
+import scala.io.Source
+
+object Timers extends Metrics {
+
+  // timers for setup code
+  val mungeVcf = timer("Munging whitespace in VCF")
+  val convertVcfToParquet = timer("Converting VCF to Parquet")
+  val loadMetadata = timer("Loading sample metadata")
+
+  // timers for running the query in pure spark (rdd's)
+  val pureSparkIntegrate = timer("Running integration query in pure Spark")
+  val pureSparkWithCachingIntegrate = timer("Running integration query in pure Spark with cached input")
+  val broadcastMetadata = timer("Broadcasting metadata")
+  val cacheInputRdd = timer("Loading and caching input RDD")
+  val queryRdd = timer("Querying the RDD")
+}
+
+// pull in all the timers
+import Timers._
+
+// convert VCF into ADAM genotypes and save to disk
+convertVcfToParquet.time {
+  sc.loadGenotypes("%s/%s".format(dataDir, vcfFile))
+    .saveAsParquet("%s/chr22.annotated.gt.adam".format(dataDir))
+}
+
+case class Sample(sampleId: String, population: String)
+
+// parse the sample metadata
+val (samples, relations) = loadMetadata.time {
+  
+  // make hadoop paths for the metadata files
+  val relationsPath = new Path("%s/%s".format(dataDir, relatednessFile))
+  val samplePath = new Path("%s/%s".format(dataDir, populationsFile))
+
+  // get the filesystem for the sample metadata
+  val fs = relationsPath.getFileSystem(sc.hadoopConfiguration)
+
+  // open the two files
+  val relationsStream = fs.open(relationsPath)
+  val sampleStream = fs.open(relationsPath)
+
+  // extract the cryptic relatedness data
+  val parsedRelations = Source.fromInputStream(relationsStream)
+    .getLines
+    .drop(4) // first 4 lines are whitespace
+    .flatMap(line => {
+      // line is tab delimited:
+      // Population      Sample 1        Sample 2        Relationship    IBD0    IBD1    IBD2
+      val splitLine = line.split("\t")
+
+      if (splitLine.length != 7) {
+        Iterable.empty[Sample]
+      } else {
+        val population = splitLine(0)
+
+	// exclude samples in ASW that are siblings
+	// we will not filter them out
+	if (population == "ASW" &&
+	    splitLine(3).startsWith("Sibling")) {
+	  Iterable.empty[Sample]
+	} else {
+	  Iterable(Sample(splitLine(1), population),
+	    Sample(splitLine(2), population))
+	}
+      }
+    }).toSeq
+
+  // extract the population labels
+  val parsedPopulations = Source.fromInputStream(sampleStream)
+    .getLines
+    .flatMap(line => {
+      // line is tab delimited:
+      // SampleId Population ? SequencerPlatform
+      val splitLine = line.split("\t")
+      
+      if (splitLine.length > 2) {
+        Some(Sample(splitLine(0), splitLine(1)))
+      } else {
+        None
+      }
+    }).toSeq
+
+  // Scala.io.source is lazy --> force materialization
+  println("Have %d related samples across %d populations.".format(parsedRelations.size,
+    parsedPopulations.size))
+
+  (parsedPopulations, parsedRelations)
+}
+
+// load the rdd from disk
+val genotypes = sc.loadParquetGenotypes("%s/chr22.annotated.gt.adam".format(dataDir))
+
+// munge and broadcast the sample metadata
+val bcastSampleToPopulationMap = broadcastMetadata.time {
+  val relatedSamples = relations.map(_.sampleId).toSet
+  val sampleToPopulationMap = samples.filter(sample => relatedSamples(sample.sampleId))
+    .map(sample => (sample.sampleId, sample.population))
+    .toArray
+    .toMap
+ sc.broadcast(sampleToPopulationMap)
+}
+
+// are we caching the data? if so, cache it now
+val cachedGenotypes = cacheInputRdd.time {
+    
+  // cache the genotypes and force the genotypes to be materialized into
+  // memory by doing a count
+  val cachedGenotypes = genotypes.transform(_.cache)
+  println("Have %d genotypes.".format(cachedGenotypes.rdd.count))
+
+  cachedGenotypes
+}
+
+val variantsByPopulation = queryRdd.time {
+ cachedGenotypes.rdd
+  .flatMap(gt => {
+      bcastSampleToPopulationMap.value
+       .get(gt.getSampleId)
+       .map(population => (population, gt))
+    }).filter(kv => {
+      val (_, gt) = kv
+        ((gt.getAlleles.contains(GenotypeAllele.REF) &&
+	  gt.getAlleles.contains(GenotypeAllele.ALT)) && // is het
+	 (Option(gt.getReadDepth).map(i => i: Int).orElse({
+	   (Option(gt.getReferenceReadDepth), Option(gt.getAlternateReadDepth)) match {
+	     case (Some(refDepth), Some(altDepth)) => Some(refDepth + altDepth)
+	     case _ => None
+	   }
+	 }).fold(true)(_ > 30)) && // DP or sum(AD) is > 30, if provided
+	 Option(gt.getAlternateReadDepth).fold(false)(_ > 10) && // alt depth > 10
+	 Option(gt.getVariant.getAnnotation.getAttributes.get("SAVANT_IMPACT")).filter(impact => {
+	   impact == "HIGH" || impact == "MEDIUM"
+	 }).isDefined &&
+	 Option(gt.getVariant.getAnnotation.getAttributes.get("ExAC.Info.AF")).fold(true)(afString => {
+	   try {
+	     afString.toFloat < 0.1
+	  } catch {
+	    case t: Throwable => {
+	      true
+	    }
+	  }
+	 }))
+      }).map(kv => {
+       val (population, gt) = kv
+       (ReferenceRegion(gt), population, gt.getVariant.getAlternateAllele)
+      }).distinct
+        .map(t => {
+	  val (_, population, _) = t
+      }).countByValue
+    }
+  }
+
+println(variantsByPopulation.mkString("\n"))
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diff --git a/submissions/bdgenomics/scripts/2_continuous.scala b/submissions/bdgenomics/scripts/2_continuous.scala
new file mode 100644
index 0000000..0248b4d
--- /dev/null
+++ b/submissions/bdgenomics/scripts/2_continuous.scala
@@ -0,0 +1,102 @@
+/**
+ *
+ */
+val dataDir = "/data/variant_db/2"
+//val na12878 = "NA12878.chr22.g.vcf.gz"
+//val na12891 = "NA12891.chr22.g.vcf.gz"
+//val na12892 = "NA12892.chr22.g.vcf.gz"
+val na12878 = "NA12878.chr22.g.vcf"
+val na12891 = "NA12891.chr22.g.vcf"
+val na12892 = "NA12892.chr22.g.vcf"
+
+import htsjdk.samtools.ValidationStringency
+import org.apache.spark.rdd.MetricsContext._
+import org.bdgenomics.adam.rdd.ADAMContext._
+import org.bdgenomics.adam.rdd.variant.VariantRDD
+import org.bdgenomics.formats.avro.GenotypeAllele
+import scala.collection.JavaConversions._
+
+// convert data into parquet
+val partitions = 64
+//sc.loadGenotypes("%s/%s".format(dataDir, na12878)).transform(_.repartition(partitions)).saveAsParquet("%s/NA12878.gt.adam".format(dataDir))
+//sc.loadGenotypes("%s/%s".format(dataDir, na12891)).transform(_.repartition(partitions)).saveAsParquet("%s/NA12891.gt.adam".format(dataDir))
+//sc.loadGenotypes("%s/%s".format(dataDir, na12892)).transform(_.repartition(partitions)).saveAsParquet("%s/NA12892.gt.adam".format(dataDir))
+sc.loadGenotypes("%s/%s".format(dataDir, na12878)).saveAsParquet("%s/NA12878.gt.adam".format(dataDir))
+sc.loadGenotypes("%s/%s".format(dataDir, na12891)).saveAsParquet("%s/NA12891.gt.adam".format(dataDir))
+sc.loadGenotypes("%s/%s".format(dataDir, na12892)).saveAsParquet("%s/NA12892.gt.adam".format(dataDir))
+
+// reload datasets and cache
+val na12878Gts = sc.loadGenotypes("%s/NA12878.gt.adam".format(dataDir)).transform(_.cache())
+val otherGts = sc.loadGenotypes("%s/NA1289*.gt.adam/*".format(dataDir)).transform(_.cache())
+
+// force materialization
+println("NA12878 has %d genotypes.".format(na12878Gts.rdd.count))
+println("NA12891/2 has %d genotypes.".format(otherGts.rdd.count))
+
+// filter high quality genotypes, hom alt in 12878, hom ref in others
+val gqThreshold = 20
+val na12878HomAltGts = na12878Gts.transform(rdd => {
+  rdd.filter(gt => {
+    val gtCalls = gt.getAlleles
+    Option(gt.getGenotypeQuality).fold(false)(_ >= gqThreshold) &&
+    !gtCalls.isEmpty &&
+    gtCalls.forall(_ == GenotypeAllele.ALT)
+  })
+})
+val otherHomRefGts = otherGts.transform(rdd => {
+  rdd.filter(gt => {
+    val gtCalls = gt.getAlleles
+    !gtCalls.isEmpty &&
+    gtCalls.forall(_ == GenotypeAllele.REF) &&
+    Option(gt.getGenotypeQuality).fold(false)(gq => {
+      if (gq == 0) {
+        // this is fun code.
+	// 
+	// the tool that generated the gVCFs we are working with occasionally writes out
+	// GQ = 0 for Hom REF gVCF lines where the Ref/Ref PL entry is high confidence.
+	//
+	// what is likely happening here can be inferred from one of the suspect lines,
+	// from the NA12892 VCF:
+	// chr22   24300634        .       G       <NON_REF>       .       .       END=24300634    GT:DP:GQ:MIN_DP:PL      0/0:24:0:24:0,0,713
+	//
+	// if you look at the PL field, Ref/Ref PL is very high while Alt/Alt and Alt/Ref PLs are 0.
+	// probably what is happening is that when the tool is calculating the GQ from PLs, it's doing something like
+	// max(PL) / sum(PL), or alternatively max(PL) / sum g in 0..m,g!=argmax_idx(PL(idx)) PL(g)
+	// and dumping a bad GQ
+	//
+	// since these lines are obviously strong HomRef calls, let's recover them by parsing PL's
+        if (gt.getVariant.getAlternateAllele == null) {
+          val nonReferenceLikelihoods = gt.getNonReferenceLikelihoods.toSeq
+	  nonReferenceLikelihoods.dropRight(1).forall(_ == java.lang.Double.NEGATIVE_INFINITY) &&
+	    nonReferenceLikelihoods.last != java.lang.Double.NEGATIVE_INFINITY
+	} else {
+	  false
+	}
+      } else {
+        gq >= gqThreshold
+      }
+    })
+  })
+})
+
+// now, do a broadcast region join
+val joinedGts = na12878HomAltGts.broadcastRegionJoin(otherHomRefGts)
+
+// and then take the variants that show up in two samples
+//
+// in a simple world, we would just count the number of home ref genotypes
+// that overlapped a given hom alt site. howaaaaaayver, a poorly formatted
+// gVCF "could" contain multiple overlapping records at a single site.
+// this would occur if we scored two different alts, and emitted hom ref
+// calls against each alt.
+//
+// note: this happens on the test data
+val selectedVariants = joinedGts.rdd.map(p => {
+    val (na12878Gt, gt) = p
+    (na12878Gt.getVariant, Set(gt.getSampleId))
+  }).reduceByKey(_ ++ _).filter(kv => kv._2.size == 2).keys
+
+// and save as vcf
+VariantRDD(selectedVariants,
+  na12878Gts.sequences).saveAsVcf("%s/selectedVariants.vcf".format(dataDir),
+    asSingleFile = true, stringency = ValidationStringency.LENIENT)
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